Leadspace Customer data platform

When it comes to tools, everyone appreciates the ones that work, and usually that means they are easy to use. Unfortunately, analysts report that at any time, 25%+ of our business software is actually shelfware. Shelfware refers to software or technology solutions that have been purchased by a business but are not actively used or adopted. If you’ve bought useful tools from major software companies, you’ve probably fallen victim to the promises of what software can do, then realized that actually implementing it was too difficult and slow to end up using it. Additionally, you’ve probably bought numerous tools that you didn’t need only because you were forced to buy them in a package deal with the tools you actually wanted. These are tools that we don’t want and certainly won’t use, but we buy them anyway because we need that other tool. 

Shelfware has become so common in software and IT agreements that we’ve come to accept it and even expect it. This is a sad state of affairs that deals a devastating blow to our wallets (and to society’s moral compass). The cost of shelfware can even act as a barrier to entry for smaller companies and startups. We believe it shouldn’t be that way. We want tools that are easy to implement, adopt and use.

Of course, not all shelfware is collected this way. Other major causes of shelfware include:

  1. Companies agree to buy each other’s products but don’t end up using them.
  2. Things get put on the shelf after a change in leadership or direction of a project.
  3. Implementations that failed or were not as productive as expected (i.e. for whatever reason the solution can’t work within your architecture).

Regardless of how you accumulated your shelfware, it’s still a problem that you’ll want to minimize going forward, especially when it comes to buying a Customer Data Platform (CDP). Tools with easy adoption in the B2B context can bring several benefits, particularly in terms of reducing shelfware. These problems and solutions to shelfware are compounded when choosing a CDP because of the incredibly high business value that a CDP provides. 

Remember, the best CDPs will (1) offer real-time data unification and identity resolution, (2) support buying groups and have buying group expanded capabilities on the roadmap, and (3) use AI for insights, journey mapping, and next best action. CDPs provide much more than all that, but the point is that these three capabilities alone are incredibly valuable and would be critical to helping you find, create and prioritize closeable business for revenue automation. Now, just imagine how devastating it would be if all of those capabilities ended up collecting dust on your digital shelf. How do you make sure that doesn’t happen to you? By choosing a CDP with easy implementation and adoption!

In Forrester’s The B2B Customer Data Platform Landscape, Q3 2023 report, Forrester analysts indicate that B2B CDPs have evolved into an established market that, “push successful vendors to focus on UI improvements and adoption,” explaining that, “Leading B2B CDP vendors understand that a successful sales and marketing tool is one that sellers and marketers can use. Although a B2B CDP deals with complex functions like data management, identification resolution, and journey orchestration, requiring an advanced technical or data science skill set to perform foundational functions creates adoption friction points and limits the tool’s overall efficiency gains, lowering its perceived value.”

What are the advantages of having a CDP that’s easy to adopt?

Increased User Engagement

  • More likely to be adopted by users within an organization.
  • Higher user engagement leads to increased utilization of the software, reducing the chances of it becoming shelfware.

Faster Onboarding

  • Simplified and intuitive tools facilitate quicker onboarding of new users.
  • Users are more likely to start using the tool immediately, reducing the likelihood of delayed or incomplete adoption.

Minimized Training Costs

  • Require less training for users, saving time and resources.
  • Reduced costs make it more feasible for businesses to encourage widespread adoption.

Rapid Time-to-Value

  • Allow users to quickly realize the benefits and value of the software.
  • Faster time-to-value encourages continued usage and decreases the chances of the tool sitting unused on the shelf.

User Satisfaction

  • Tools that are easy to adopt and use contribute to higher user satisfaction.
  • Satisfied users are more likely to actively engage with the software, avoiding the scenario where the tool becomes shelfware.

Flexible and Scalable

  • Easy adoption tools often have flexible and scalable features, making them more adaptable to different user needs and evolving business requirements.
  • Adaptability reduces the likelihood of the tool becoming obsolete and abandoned.

Data-Driven Insights

  • Tools with easy adoption often come with built-in analytics and reporting features.
  • Organizations can use these insights to track user activity, identify potential roadblocks to adoption, and make informed decisions to enhance usage.

Cross-Functional Collaboration

  • Encourages collaboration across different departments and teams within an organization.
  • Increased collaboration leads to a higher likelihood of sustained usage.

Simply put, easily adopted tools in B2B not only help to reduce shelfware but also contribute to improved user engagement, faster onboarding, cost savings, and overall satisfaction. This results in a more successful implementation and utilization of the software within the organization.

Unlike most CDPs available, Leadspace is actually ready to use out of the box and doesn’t require any other purchases to use – no extra partners or implementations. Your salespeople and marketers can immediately access the user-friendly interface to explore your Total Addressable Market (TAM), discover leads/contacts, find lookalike companies, create and activate segments, and leverage our 30+ embedded sources to enrich buyer profiles across hierarchies. With Leadspace, no downloads, coding, or training are necessary to gain significant business insights. To unlock the full potential of our Revenue Radar, of course, you will need to discover your ICP and build models off of your first-party data – luckily, our team is here to walk you through that part of the process.

Don’t believe it’s that easy? Check out Forrester’s scoreboard, The Forrester Wave™: B2B Customer Data Platforms, Q4 2023, where Leadspace is the only platform to score a solid 5 out of 5 in Adoption. When you invest in a powerful CDP solution, make sure it doesn’t turn into shelfware. When we say Leadspace is ready to use and that you will use it, we mean it.

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Leadspace Identity Resolution

If you’re involved in enterprise-level B2B sales and marketing, you know that in order to compete, you’ll need a Customer Data Platform (CDP) to maintain the massive amount of buyer data that goes into data-driven decision making. If you’re still trying to decide which CDP is best for your company, you’re in luck, because Forrester has been evaluating and scoring your options to help you figure it out! In Forrester’s The B2B Customer Data Platform Landscape, Q3 2023 report, their analysts explored the B2B CDP landscape and key factors that would go into their evaluation. In their 29-criterion evaluation of B2B customer data platform (CDP) providers, they would research, analyze and score the most significant current solutions available. One of those factors they suggest you consider when choosing a CDP is whether or not that solution is actually ready to be used by your sales and marketing teams.

In their CDP Landscape report, Forrester’s analysts explained that, “Although still a fairly new addition to the revenue technology stack, B2B CDPs have made significant strides to set and stabilize their place as the customer data solution. B2B CDPs have evolved into an established market that… pushes successful vendors to focus on UI improvements and adoption. Leading B2B CDP vendors understand that a successful sales and marketing tool is one that sellers and marketers can use. Although a B2B CDP deals with complex functions like data management, identification resolution, and journey orchestration, requiring an advanced technical or data science skill set to perform foundational functions creates adoption friction points and limits the tool’s overall efficiency gains, lowering its perceived value.”

Unlike most CDPs available, Leadspace is actually ready to use out of the box. Your salespeople and marketers can immediately access our user-friendly interface to explore your Total Addressable Market (TAM), discover leads/contacts, find lookalike companies, create and activate segments, and leverage our 30+ embedded sources to enrich buyer profiles across hierarchies. With Leadspace, no downloads, coding, or training are necessary to gain significant business insights. To unlock the full potential of our Revenue Radar, of course, you will need to discover your ICP and build models off of your first-party data – luckily, our team is here to walk you through that part of the process.

Don’t believe it’s that easy? Just ask Forrester! Check out their new wave, The Forrester Wave™: B2B Customer Data Platforms, Q4 2023, where Leadspace is one of only two platforms to score a solid 5 out of 5 in Implementation and Professional Services. When we say Leadspace is ready to use, we mean it. 

What are the hallmarks of a best-in-class execution and implementation of a CDP?

  • Using the full range of offerings: Data Management, Recommendations (Models/Scoring), and Activations (Salesforce, Marketo, etc.)
  • Having very clear process in place already (or in progress)
  • Having a team that is able to manage, analyze, are savvy, and can make adjustments based on tests and experience
  • Have very clear initiative and goals that a CDP can help with vs. please just fix my messed up data (until it gets messed up again)

Don’t forget to read through Forrester’s entire report to see how Leadspace measures up against leading CDP vendors in every category (pay special attention to Identity Resolution & Profiles). Customer Data Platforms are here to help and we’re here to stay. Hop on the fastest road to revenue—with clicks, not code—today! Talk to us.

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Leadspace Identity Resolution

Identity resolution is the unsung hero in GTM ROI. Fundamental to any sales & marketing endeavor is knowing who that buyer is – and what role they play in any buying team. It’s the difference between flying blind and flying smart.  Whether or not you have a strong identity resolution framework is the main factor in determining and authenticating your knowledge of your buyer.

What is Identity Resolution?

Identity resolution is quite plainly the ability to resolve from a ton of data signals the identity of a buyer – who they are, who they work for.  It’s the science of connecting the growing volume of person identifiers to a single individual as he or she interacts across channels and devices. In other words, Identity Resolution is the process of accurately associating data (or buying signals) with the specific people who interact with your business. With Identity resolution you can build a 360-degree view of your prospects, customers, and partners. It is the foundation to automating your lead-to-account matching process correctly.

What do Identity Resolution platforms (or CDPs) do?

In B2B sales and marketing it’s important to track buyer behavior and attach those buyers to a company and ultimately a company buying team. Millions of marketing leads lie dormant in marketing systems because they cannot be associated with a company. They can’t be scored on anything other than behavior. They can’t be routed to the right salesperson. Identity resolution platforms aggregate data from numerous buyer touchpoints, enrich that data with great third party information and then unify it with the company’s first party data. All of this data is organized into a unified buyer profile based on a system of rules, helping sales & marketing teams associate buyers to companies and ultimately to access accurate, up-to-date buyer information. This drives accurate segmentation and targeting. Once audiences (buyer segments) are created, sales & marketing teams can activate them in ABM outreach, connect them with various media platforms and evaluate how these groups are responding to their campaigns across multiple touchpoints.

Why is Identity Resolution so important today?

Time kills all deals. Time kills all leads. In today’s economic environment, speed is of the essence.  Leading companies today with strong identity resolution platforms are able to take even the most limited of information in simple web forms, enrich them with great data, match them to a CRM account and route them to the right system or person in minutes. Identity resolution makes your underlying data (and profiles) more trustworthy, improves customer service, enhances operational efficiencies and increases data analytics reliability.

A lot of data goes into building the buyer profiles needed to personalize outreach and prioritize closeable business effectively. Accurately connecting firmographics, demographics, technographics, intent, fit, persona and engagement to a single buyer profile is critical to building the buyer profile we need to target buyers effectively. Identity Resolution and Profiles are the backbone of revenue targeting, and revenue targeting is the backbone of sales and marketing effectiveness.

What are the risks of adopting a solution with a bad Identity Resolution framework?

Worst case scenario, you’ll end up actively creating bad customer/prospect experiences as a result of incomplete and inaccurate personalization based off of bad buyer profiles. The right data attributed to the wrong buyer is ultimately wrong, and bad personalized outreach will make for a worse experience than no personalization at all.

Other common challenges along the way will include:

  • Long lead to response times
  • Incomplete or duplicate profiles
  • Violation of consumer privacy regulations
  • Data quality issues
  • Fragmented view of customer journey
  • Difficulty activating customer data across channels

How do I get THE BEST Identity Resolution framework?

With Leadspace, you can expect the best identity resolution available – just ask Forrester! In their 29-criterion evaluation of B2B customer data platform (CDP) providers, they researched, analyzed and scored the most significant current solutions available. Check out their report, The Forrester Wave™: B2B Customer Data Platforms, Q4 2023, where we were the only platform to score a solid 5 out of 5 in Identity Resolution & Profiles.

In this recent wave, Forrester came to find that, “B2B CDPs are using AI to improve audience creation and personalization.” They concluded that B2B CDP customers should seek a solution based on 3 major capabilities:

  • Offer real-time data unification and identity resolution.
  • Support buying groups and have buying group expanded capabilities on the roadmap. 
  • Use AI for insights, journey mapping, and next best action.

Leadspace’s perfect Identity Resolution score is extremely significant and at the heart of every B2B GTM platform. While other categories matter, it all starts with Identity Resolution and unified, active profiles. Without a good framework for that, the rest of the capabilities a CDP offers will lean towards being obsolete, as they’re not operating from complete, accurate and active buyer profiles to begin with. So, we’re quite excited with our evaluation. 

Long story short, if you want the absolute best identity resolution and profiles available (hint: you do) contact us today and we’ll show you what makes our buyer profiles better than anyone else’s!

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Leadspace identity resolution

Answer: Of course you do!

Sales & Marketing teams know that targeting customers with incomplete and siloed data is a complex process. Having a single, comprehensive view of your customer data in one place makes it significantly easier to effectively target the right people at the right time with personalized campaigns. Unfortunately, creating active, unified and accurate buyers profiles by hand is extremely cumbersome and error-prone. To overcome these difficulties, companies are beginning to automate the process by implementing Customer Data Platforms (CDPs), which empower sales & marketing teams to seamlessly blend all of their siloed customer data into a single source of truth – developing buyer profiles for people, companies (and even divisions with hierarchies) and accounts. Even better, with certain CDP solutions, those unified profiles are automatically updated in real-time as source data changes and new data is ingested.

A lot of buying signals (data) go into building the buyer profiles that sales & marketing need to successfully target their best customers.

Leadspace identity resolution graph

Identity resolution is the backbone of active profiles, and active profiles are the backbone of sales & marketing.

What is Identity Resolution?

Identity resolution is the process of accurately associating data (or buying signals) with each of the specific people who interact with your business. Identity resolution is required to build a 360-degree view of your prospects/buyers, partners, suppliers and more. It is entirely critical to automating and implementing your lead-to-account matching process correctly. In today’s world, profiling is the backbone of any data-driven organization – the stronger your data is, the better your next-best-step decision making process is.

To be successful, companies must be able to accurately identify potential buyers, partners and suppliers throughout their data environments. Consider large on-premises, hybrid, multi-cloud and cloud-based data ecosystems. With such a vast amount of complex data volume and variety, how can you be sure that your buyers, partners and suppliers are accurately identified in your systems?

With the right identity resolution and profiling software, you can leverage a centralized system to resolve buyer identities across all of your data and applications to ensure you’re generating the best possible buyer profiles at every hierarchy. Identity resolution makes your underlying data (and profiles) more trustworthy, improves customer service, enhances operational efficiencies and increases data analytics reliability. It can help you detect fraud, identify your best customers, and improve customer contact methods.

A high-quality identity resolution framework will help you at every stage of your data modernization journey, and enables you gain a competitive advantage to enhance business results in challenging areas including:

  • Strategic targeting / prioritized outreach
  • ICP analytics / TAM analytics / territory planning
  • Data-driven decision making
  • Revenue Automation
  • Effective application of predictive models
  • Account-based marketing (ABM)
  • Creating account/contact hierarchies
  • Lead-to-account matching and lead routing
  • CRM clean up and ongoing enrichment
  • Customer experience programs
  • Data Health Reports
  • Financial reporting and forecasting
  • Mergers and acquisitions
  • Governance and compliance

What are the advantages of having a high-quality identity resolution framework?

Master your conversations and inbound leads in real-time with conversational AI.

Conversational AI is at best a robotic conversation without understanding the person and context.  With identity resolution, you can match, enrich and route leads with the least amount of friction in your funnel – leaner questions, leaner forms. This lets you handle the most formidable tasks such as matching a lead to an account with only a personal email or routing high-value leads to the right sales team or nurture program.

Improve your decision-making capabilities. 

Base your business decisions on complete, accurate, consistent and current identity data. Identify, eliminate and prevent the spread of errors, duplicates or inconsistencies in your customer identity data hub. This ensures accurate buyer intelligence reporting/analysis and empowers you to make better decisions faster with complete, accurate and active customer data.

Improve your risk management and compliance processes. 

Identity resolution supplies your business with reliable, high-quality identity data that you can use to enhance compliance reporting and risk assessment.

Enhance your customer experience.

With a single, more comprehensive and accurate view of your buyers, you can identify prospects in online inquiries and for omni-channel commerce. Identify your best customers. Improve contact methods. Additionally, you can reduce customer turnover and improve customer contact to effectively manage customers through their own unique buyer journeys, ultimately providing them with a more satisfactory experience.

Maximize the value of your technology investments.

Identity resolution facilitates the anticipated value of your most strategic business initiatives. A powerful data-matching engine enables you to cross-reference siloed data sets and create unique identities across multiple records. So you can trust that your business applications and systems contain high-quality identity data at every step.

Handle your most challenging use cases in real-time.

With identity resolution, you can search, match and manage identity data records from multiple countries across multiple hierarchies. Enable your salespeople to immediately see up-to-date buying signals (including intent) associated with each new lead as they come in for quicker outreach. This lets you handle the most formidable tasks –  even using different alphabets and handling complex, multi-language identity data.

Having the best identity resolution framework means you can achieve all of these advantages faster and better than your competitors can. So, how do you find the best identity resolution and unified profiles framework? Back in 2021, Forrester’s evaluation of 14 leading B2B CDP companies, considering several factors to compare each CDPs’ Identity Resolution and unified profiles framework. Forrester started by looking at each CDPs’ identity resolution and profiling services and if the process was user-configurable. They wanted to know if (and how) the solutions create unified profiles at the account, buying group, and contact level. Finally, Forrester looked at each solution’s ability to create a persistent store of unified profiles and if they offer B2B Revenue Waterfall™ enablement. 

In that 2021 report, Forrester differentiated Leadspace as a leader in Identity Resolution & Unified Profiles. But, Forrester evaluated leading CDPs again this year… This time around, they scored Leadspace to be THE leader in Identity Resolution and Profiles! Leadspace was the only CDP evaluated in The Forrester Wave™: B2B Customer Data Platforms, Q4 2023 to score a perfect 5 out of 5 in this category.

Seeing as the effectiveness of data-driven decisions depend on the quality of the underlying data (unified buyer profiles), Leadspace’s perfect score in this category is extremely significant. While other categories matter, it all starts with Identity Resolution and unified, active profiles. Without a good framework for that, the rest of the capabilities a CDP offers will lean towards being obsolete, as they’re not operating from complete, accurate and active buyer profiles to begin with. So, we’re quite excited with our evaluation. Let’s dive deeper into Leadspace’s Identity Resolution & Unified Profiles framework.

Forrester Wave Strong Performer 2023

Our identity resolution framework is based on deterministic/probabilistic identifiers (IDs). Unique/non-unique company and person IDs are used in clustering algorithms to unify profiles and validate/dedupe data. Unification logic is customer-configurable for business needs. For scalability, we use probabilistic IDs, PII and anonymous, to complement unique IDs for matching. Probabilistic clustering leverages decision trees (XGBoost) and other algorithms. 

Clients that deploy the B2B Revenue Waterfall™ leverage us to support execution on the model. In order to optimize for accuracy, we use deterministic IDs for following types of unique Person and Company data. Our Person Unique Identifiers include Workmail, Webmail, Social Profile, Phone, and Cookie/Device ID, and our Company Unique Identifiers include Domain, ID/DUNS, Social Profile, Phone, and IP.

We provide identifiers at different levels of the company hierarchy so you have the ability to unify and create single records/groupings at the global HQ level, Country HQ level, Business HQ level and/or at specific regional/site locations. Account unification solutions are built to support the operational structure of organizations to identify buying centers and teams for customers’ offerings as well as the legal structure. For example, our operational hierarchies can be mapped to D&B DUNs legal based hierarchies to provide this interoperable framework across functions and teams. These hierarchies link profiles together and persist across systems. Events and behaviors then update and inform those profiles. 

With Leadspace, you can expect the best identity resolution available – just ask Forrester. Check out The Forrester Wave™: B2B Customer Data Platforms, Q4 2023, where we were the only platform to score a solid 5 out of 5 in identity resolution and unified profiles. To learn more about how you can achieve best-in-class B2B profiles that you can trust, backed by the best identity resolution available (all while cutting your data spend in half), schedule your meeting today. Seamlessly create complete, accurate B2B buyer profiles that are pre-blended and active with unrivaled, embedded third-party data for people, companies and accounts. Eliminate your buyer data silos and accelerate the success of your sales & marketing campaigns with Leadspace’s CDP for best-in-class B2B buyer profiles you can bet your business on!

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Forrester New Wave Leader 2021 Badge
Forrester New Wave Leader Badge

Forrester Research evaluated 14 B2B Customer Data Platforms, considering 10 key capabilities, and published their findings in The Forrester New Wave™: B2B Standalone CDPs, Q4, 2021. Leadspace was among those evaluated, and ranked as a leader in nearly every category!

  • Data Integration & Unification
  • Data Sources & Types
  • Identity Resolution & Unified Profiles
  • Activation & Alerts
  • Application Integrations
  • Decisioning
  • Reporting
  • Execution Roadmap
  • Market Approach
  • Product Vision

We assure you that we are still providing the best-in-class buyer profiles and predictive AI models for the fastest TAM-to-opportunity pipeline available. For more on how we performed check out our respective blog on each capability. We look forward to proving ourselves by cutting your data spend in half, eliminating your data silos, and driving your sales & marketing teams to closeable business faster than any of our competitors can. Contact us today!

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better data with leadspace

As B2B marketers, we aim to deliver effective campaigns targeted at the best opportunities within our Total Addressable Market (TAM) – at the lowest cost. Doing this successfully starts with creating complete, accurate, dynamic and unified buyer profiles of people, accounts and buying centers so we can properly prioritize and target opportunities with data-driven assurance that we’re delivering the right message to the right people at the right time. Unfortunately, building robust buyer profiles is a complex, cumbersome process – and it usually isn’t cheap.

It takes a village of data to populate all the fields that our sales & marketing teams need to effectively understand our buyers. Painting the whole picture requires Firmographics, Demographics, Technographics, First-Party Data, Third-Party Data and Revenue Signals (Fit, Intent, Persona Models). Getting all of these can cost an arm and a leg when bought separately, and then blending them all together is a whole new monster.

Legacy approaches to building B2B buyer profiles are riddled with obstacles. Buying data from multiple vendors gives you siloed data from the start. Then you need to manually unify, dedupe and update the data as it comes in. You’ll encounter blank cells and outdated information – so expect stagnant profiles down the line. Data blending and account matching is incredibly cumbersome and error-prone, and you’ll have to guess which source to trust when there’s a discrepancy between data sources. You’d make different databases for Sales & Marketing based on their respective needs because it’s quicker, but now they’re operating from different data which becomes a pain to update consistently as it changes over time. It will be extremely difficult, if not impossible, to support all opportunities within an account at scale. There’s also the issue of operational skills and training across vendors/sources. Finally you’ll encounter Intent & Technographic signals that are not integrated into CRM systems, which will be a mess to do yourself… 

Simply put, the traditional approach to building accurate, complete, active B2B buyer profiles is a much messier process than it sounds. If you’ve built B2B buyer profiles with account hierarchies at scale, you know what I’m talking about. So, what’s the solution? How can we automate or streamline this cumbersome process without breaking the bank? Believe it or not, we can actually address all of these issues and cut your data spend in half at the same time. To learn how, check out our webinar, How to Improve Your Prospect Database and Save 50% Annually, where Amish Sheth, VP, Solutions Engineering at Leadspace, explains how to easily achieve best-in-class B2B buyer profiles while cutting your annual data spend by 50% at the same time.

In his webinar, Amish takes us through the underlying goals of B2B sales and marketers, the challenges with their respective databases, strategies, the role of AI, use cases, value benefits, how Leadspace can help, and a Q&A at the end. If you want to truly understand the difference between good, great, and best-in-class practices, Amish is the guy to show you.

So, stop wasting time and money buying siloed data and attempting to blend it. Learn how you can replace your current data vendors with a single source of pre-blended, up-to-date third-party data. Get pre-built B2B buyer profiles for people and companies at all account hierarchies. See how you can spend less time (and money) building your buyer profiles, and more time prioritizing and pursuing them. Your competitors are also looking to leverage AI to facilitate closeable business – so don’t miss the boat, check out the webinar.

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leadspace CDP

As B2B marketers, our goal is to deliver effective campaigns targeted at the best opportunities that exist within our Total Addressable Market (TAM) – at the lowest possible cost. This involves identifying our Total Addressable Market (TAM), developing our Ideal Customer Profile (ICP), and then comparing our ICP throughout our TAM to determine which opportunities to focus on. Doing this successfully starts with creating complete, accurate, dynamic, and unified profiles of people, accounts and buying centers so we can properly prioritize and target opportunities with data-driven insurance that we’re delivering the right message to the right people at the right time.

Unfortunately, building robust buyer profiles is a complex, cumbersome process – and it usually isn’t cheap. As we all know, buying signals come in many shapes and from many sources. Which information do we trust when there’s a discrepancy between two or more sources? Buying data from multiple vendors, unifying it, updating it and manually checking its accuracy is an extensive process. At minimum, we’ll be making one-time purchases of firmographic, technographic, and intent data separately. Even once we have all the data in our hands, putting it to use is a significant challenge. Preparing and blending all that data from different sources by hand is an extensive, time-consuming process. By the time we’ve put it all together, a portion of the data will already be out of date. The process is riddled with human error, blank cells, and outdated information. Traditional demand and ABM approaches leave us with siloed data, stagnant profiles, and the inability to support all opportunities within an account at scale.

In the olden days of marketing – aka ten years ago – it used to be okay to do an annual data update because there were less acquisitions and job changes. That’s not the case today. People and companies change constantly. Companies make acquisitions, people change jobs, and intentions are dynamic. This means your buyer’s data changes every day – but does your database reflect that? Is the data you use to drive your business as accurate as the day you procured it? Data decay is an issue that every company must face at some point. Email marketing databases, for example, naturally degrade by approximately 23% every year according to Hubspot. While the degree of decay is not yet fully understood, our response to it can greatly mitigate the effects the decay will have on our organizations’ successful use of data to drive decisions. Operating from accurate, complete data is critical to making successful data-driven decisions. Yes, your data will decay over time, but how you deal with its decay will determine its value to your company’s overall success in our increasingly data-centric economy. Ultimately, legacy approaches to building B2B buyer profiles are error-prone, expensive, and time consuming.

There’s an old saying in business consultation, “fast, cheap and good – pick any two.” So, what’s the solution? Pick all three with Leadspace Profiling. With Leadspace Profiling, you can leverage unrivaled third-party account and person data, from 30+ leading B2B data sources (70 million companies, 240 million buying centers, 280 million contacts). Leadspace will automatically ingest and unify first- and third-party data for account context and prioritization. We’ll automatically generate full account profiles including hierarchies, firmographics, technographics, weekly intent, and mobile contacts. This includes weighted cross signal analysis so you can trust the data is being pulled from the most accurate source. Generally, our customers save over 50% on data costs through a combination of eliminating data purchases and better leveraging their existing data service vendors, and can automate the cumbersome, error-prone processes most data users endure today. Here’s how we recommend you do it with Leadspace Profiling.

Align your Sales & Marketing Teams with Account & Contact Profiles.

Step #1: Enrich both your CRM and Marketing Automation Platforms in hours. Use our enrichment APIs to directly connect to our award-winning B2B customer data platform.

Step #2: Unify your first- and third-party profiles with Leadspace. Our award-winning Customer Data Platform integrates, normalizes and matches profiles with existing accounts.

Step #3: Enrich your Inbound lead flow in real-time, with unrivaled third-party data coverage. Leadspace goes far beyond basic firmographic data like company size, industry and revenue, to include more granular information like installed technologies, account intent, contact/company expertise, and much more.

Step #4: Discover new Account and Contact Profiles with the Leadspace Studio to create new sales and marketing campaign segments — aligned to your TAM and territories.

Organize the right leads & contacts within those accounts.

Step #1: Enrich your lead & contact data with unrivaled third-party data coverage, for a complete view of every individual person within your target accounts. Leadspace goes far beyond basic person-level data like job title, to include highly useful information like persona-based scores, specific job roles & responsibilities, what technologies they use, expertise, specialties, and much more.

Step #2: Match leads to accounts with Leadspace lead-to-account matching. (Our unrivaled data coverage means our match consistently outperforms point solutions that rely solely on questionable first-party data for matching, like email address domains only.)

Step #3: Create High Quality Buyer Profile Segments, for comprehensive coverage of the key decision-makers and influencers within each account. Use AI to create customized personas, or select from Leadspace’s vast persona library. Like our data, Leadspace custom personas aren’t based on superficial criteria like job titles— they’re built by analyzing the “DNA” of your best customers, which includes a vast range of criteria.

Profile Better: Guided Operations. (Identity Resolution, Lead-to-Account Matching)

  • Utilize our B2B data unification and buying expertise to build closeable unified profiles. We’ve critically evaluated, selected and curated the industry’s best third-party company and people data sources in the world. Then we unify these profiles with your first-party data to fuel your business and your B2B Buyer Graph.
  • Add the personal touch to your unified profiles. Create the best buyer profiles at the individual level by using personal demographics and buying behaviors from social signals and interests to assign personas instead of nondescript job titles.
  • Route better with the industry’s best lead-to-account matching from Leadspace. Our AI-powered engine enriches and scores leads in real time with firmographic, hierarchy, intent and propensity data to intelligently fuel the most sophisticated lead management scenarios.

Leverage the Leadspace CDP to enrich, unify and deduplicate your data.

Leadspace supports the maintenance of your data quality. Leadspace can unify multiple siloed CRM and Marketing Automation instances into a single dataset, removing the guesswork that may be necessary to do this manually – effectively consolidating and tackling multiple complex processes with a simple point-and-click function. Unification allows you to verify, dedupe, enrich and cleanse CRM & Marketing Automation data with firmographics, and demographic data, and even leverage AI intent and propensity models for true data-driven insights. Additionally, you can upload those records directly into your CRM or Marketing Automation tool to ensure consistency in data across your entire MarTech landscape.

To put it bluntly, we know you’re always looking for a way to do more with less, and we’d like to offer you a way to do a lot more with a lot less. If you give us 30 minutes of your time, we’ll show you how we can replace all of your current data vendors for less than half of the price you’re spending on them all today. We are sure that we can do all of this for 50% of what you currently spend your on firmographic, technographic, and intent data alone:

  • Firmographic, technographic, intent data
  • 30+ embedded 3rd-party sources
  • Multi-source unification & curation
  • Full ICP & TAM discovery and exploration
  • Profiles for people, companies & buying centers
  • Cross-signal analysis with confidence scores
  • Active profiles – continuously updated
  • Data Health Reports and TAM analytics
  • Best Lead-to-Account Matching
  • Segment activation and deployment
  • Full enrichment across 200+ fields
  • Full CRM and MAP integrations

The breadth of our embedded third-party data is best-in-class. Our data comes pre-blended and is automatically updated. No more siloed data! Your sales, marketing and product teams can all operate in alignment off of the same data. When someone switches jobs or a company is acquired, everyone will see it be reflected in their respective buyer profiles. So, if you’re sick of spending a fortune on data and you want something much better, let’s talk. If you’d like to learn more about Leadspace Profiling, take a look at the Leadspace product sheet to see how Leadspace’s CDP solution can modernize and optimize your B2B marketing approach.

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G2 Badges for Fall 2023

Today, we’re excited to report that Leadspace has once again achieved numerous awards from G2 in 2023. We earned eight new G2 high performer badges across three categories: Customer Data Platforms; Sales Intelligence; and Lead Routing in the Enterprise Grid Reports and the Enterprise Americas Regional Grid Reports for Fall 2023. In addition, Leadspace was again awarded the highest Quality of Support product rating as well as the Easiest Doing Business With in the Lead Scoring category. Quarterly, G2 highlights the top rated solutions in the industry, as chosen by the source that matters most: customer feedback to questions asked in reviews for G2’s reports. 

About Leadspace

Leadspace DriveTM is a top Customer Data Platform (CDP) solution which specializes in building best-in-class dynamic B2B buyer profiles across your Total Addressable Market (TAM) at the person, company, and buying center level – all at the lowest possible cost with Leadspace Profiling. Industry leading companies buy us for our robust data graph of 30+ of the top data sources pre-embedded into our system – but they love us for our models! Our Revenue RadarTM applies unique, multifaceted AI models for company Fit, Intent, and Persona across your dynamic buyer profiles to score them and determine which of them are the right companies, then which of them are the ready companies, and then which of their people are the right people. Add in your engagement scores to see which of those right people are the ready people, and you know who the absolute best customers are for competitive strategic investment. 

With Revenue RadarTM our four-signal AI targeting narrows the target by focusing on companies with a 2X, 6X or even 12X chance of closing. Leadspace takes the guesswork out of B2B revenue for companies like Microsoft, Salesforce and Gong by targeting the fewest number of companies with the highest odds of closeable business. 

More than 25 thousand sales and marketing professionals use the Leadspace Drive platform to build and operationalize their total addressable market (TAM), identify their ideal customer profile (ICP) and optimize their campaigns with fit, intent and persona models. Sales and marketing teams use Leadspace to give them a competitive edge so they know who their best customers are, understand their whitespace opportunities, and can dramatically improve the effectiveness of their sales and marketing programs.

Our Awards

Americas Sales Intelligence: Enterprise High Performer – Fall 2023

Enterprise Sales Intelligence: High Performer – Fall 2023

Customer Data Platform: High Performer – Fall 2023

Americas Customer Data Platform: Enterprise High Performer – Fall 2023

Enterprise Customer Data Platform: High Performer – Fall 2023

Enterprise Lead Scoring: High Performer – Fall 2023

Enterprise Lead Scoring: Easiest To Do Business With – Fall 2023

Enterprise Lead Scoring: Best Support – Fall 2023

Users Love Us – Fall 2023

*This report is based on ratings by business professionals. Leadspace received user reviews and responses for each of the relationship-related questions to qualify for inclusion in the results index.

What Customers are Saying About Us

G2 reviewers recognize Leadspace for its superior solution with notable recent feedback from enterprise customers including 

  • Leadspace helps with ABM motion.
  • With Leadspace analytics I discovered new opportunities.
  • Allows me to find the most detailed, accurate and consolidated buyer profile details.

“Our customers are the most important part of our business. Hearing from them validates that Leadspace products are unrivaled in the industry,” said Marge Breya, President of Leadspace. “With Leadspace, sales and marketing teams prioritize and laser target the accounts and buyers who are both most likely and ready to buy their product. Our customers see results.”

Conclusion

Simply put, it’s no surprise that Leadspace is earning awards! Having achieved so much as such a small company within our industry, we owe it all to our product. Our product already speaks for itself, but we can’t wait to show the world what we’re cooking up next. None of our competitors have the secret sauce that Leadspace does, and with more and more people starting to recognize and experience our product, we fully expect to see game-changing exponential growth in the near future. Our goal is to be the single source of truth for B2B sales and marketing teams. We’re so proud of our team for all that Leadspace has accomplished, and we cannot wait to see how many badges we earn this time next year! 

Of course, we especially want to thank the people over at G2 for all of their hard work, wonderful platform, great communication, and this amazing opportunity they’ve given us to evaluate our product and provide such valuable feedback. We look forward to using it to improve our product. Thanks, G2!

To sum it all up, not everyone wants or needs your product – but you can find the ones who do with Leadspace (and you can trust that we have the best support and are the easiest to do business with) – just ask G2.

To learn more about what Leadspace users have to say or leave a review, visit G2’s Leadspace review page. For more information about Leadspace, please visit www.leadspace.com, connect with us on LinkedIn, Facebook and follow us on Twitter. Come see what makes Leadspace Drive the fastest road to revenue today.

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Leadspace b2b customer data platform, engagement scores

An engagement scoring model is often the signature move for a demand generation team – and they’ve been around for a long time in the marketing world. Most campaign teams use them as a way to understand when a “lead” is ready to be routed to sales. Some teams use simple off-the-shelf models from their marketing automation platform and others develop sophisticated models that include other predictive signals to weight the scoring so that the right companies or buyers have a lower activity threshold before they are sent for follow up. Sounds simple right? But the scoring model actually represents the opportunity cost balance that a demand gen team sets for the GTM motion – when do they decide that sales capacity should be used to go after an opportunity.  

The battle between sales and marketing has always been about this opportunity cost balance. When is a lead qualified enough to put money into the sales development team qualifying it and the sales team pursuing it? Sophisticated demand teams get this balance right by constantly improving the mix of their engagement scoring models to include activity (often called first party intent), third party intent and predictive scoring to pre-weight scoring for the right accounts and buyers.

Let’s talk about why opportunity cost is so important and why it’s at the heart of the sales and marketing “is it a good lead” frustration. Let’s take two cases – the high velocity sales team and the high-value sales team.  

Imagine a selling market where minutes really count where enriching, scoring and routing the lead within 2-3 minutes makes the difference between winning or losing to the competition. It’s typically a single-buyer buying team with a deal size less than a few thousands per month and a sales cycle that is in days or weeks. The high-velocity sales team is all about getting the right contact information and the strongest signals possible to time their pursuit. They are looking to close a high volume of deals and they typically can’t afford to spend a lot of time in the qualification process. Step one is all about lead enrichment – are you routing the lead to the right territory and is there the right phone or email or LinkedIn contact information so that they can quickly engage. Step two is the scoring – enough information so that they can quickly get the most compelling product value proposition in front of that prospect. And they might only have one or two times to make that pitch. Scoring must be tuned to arm that sales person with this – can they tell which product that’s most likely to be sold, what are the intent topics is that account showing for that product, which buyer persona do they fit so that the value prop can be tuned and have they attended any webinars, downloaded content, or even just raised their hand to speak to a sales person. Time and precision is of the essence for these velocity teams.  

Now let’s turn to look at a different selling market where the deal size can be in the hundreds of thousands to millions and the sales rep might only be able to pursuit 5-10 deals every quarter. These deals have a long selling cycle – months to sometimes years. They have complex buying teams – often 7-10 influencers. This value sales rep is thinking about how to qualify and align the value of the company’s portfolio to that lead. They are thinking about whether the account is the right kind of company that has successfully bought their product and whether that person is influential enough to go after.  Who are all the buyers who might be influential in the process. And are they engaged? Is this a customer already so that they are already pre-qualified in the procurement system. The scoring model here needs to focus not so much on timing but on the odds of that business being closable. Propensity to buy and intent are important to get those odds right at the account level and figure out which product is the right way in. It might take into account the buying team in addition to the individual buyer engagement into the scoring model. Are all of the buying team personas engaged? 

In both the high-value and high-velocity selling motions, getting profile enrichment right is key. And enrichment happens not only in all of the core systems or data warehouses, it happens on the inbound lead funnel and in the real-time web interactions. And it’s always a combination of first and third party data in blended company and person profiles. Profile enrichment starts with regularly updating, cleaning and validating data, removing duplicates, and standardizing information. The profiles must take into account information not only from the RevTech stack but also the company’s operational systems, and financial systems. Building trusted profiles is only half the battle – the next step is to integrate them across the Customer Relationship Management (CRMs) and Marketing Automation Platforms (MAPs) to ensure sales and marketing are operating from the same, active data in pursuit of the same goal. Integrating systems provides a unified view of customer information and interactions, resulting in several significant advantages.

The next step is the scoring. As invaluable as engagement scores are, they’re not particularly useful by themselves. You know someone has engaged with your content, but… does their company match your ICP? Has their company shown intent for your type of product? Does that person have the buying power or persona necessary to buy from you? Knowing someone has engaged with your content doesn’t mean you start dumping your sales & marketing resources in pursuit of that person’s business. You need to qualify them first. There are simply too many unknowns that need to be known before you allocate your valuable, limited sales and marketing resources. We don’t want to waste precious time and money chasing down leads that aren’t likely to close in the first place, so how do we determine which of the high-engagement scoring people are most likely to close? How do we prioritize them?

So let’s talk about what should be in the scoring model. Clearly the activity-based scoring is critical. The prospect is revealing their interests and intent through the webinars/events they are intending, their engagement in the social channel, the content that they are downloading on websites and the frequency by which they are doing it. More points are given for higher value activity. Points are taken away if the activity isn’t recent. The activity part of scoring is always about the direct pulse of activity. Activity engagement isn’t enough however when we are talking about opportunity cost. If two leads do the same things, which should you prioritize first for sales?

Sophisticated marketing teams add or subtract points with predictive signals as well. If the account has a high propensity to buy, 50 points might be added to the score. As the intent level increases at the account level, 10 points might be added per level. The buyer persona fit might be used to give negative points for students or interns and higher points for prospects who show certain skills and interests. Weighting the engagement activity scoring with predictive scoring is the tie breaker. It means that the prospect who fits all the predictive criteria might only have to do half or a quarter of the activities before they are routed to sales.

The Weighted Engagement Scoring Model  

So let’s go through the elements of the scoring models to identify the right company, the ready company, the right person, and the ready person. Engagement scores represent the ready person. But again, the ready person doesn’t necessarily mean they’re the right person from the right and ready company. We need to correctly weigh and apply our scoring models in the right order.  So what should be in a Weighted Engagement Scoring Model?

  1. Company Fit or Propensity to Buy Model – The Right Account: An AI-model built from your historical conversion data set (opportunities). Applies scoring that indicates a company and/or person’s likelihood to be a good target buyer. Use cases include propensity to buy, inbound lead conversion, higher LTV, upsell/cross-sell, etc.
  1. Company Intent Model – the Right Time: A weekly set of customized signals that monitors your accounts for intent (third-party and/or first-party), and applies scoring based on the level of intent activity specific to a customers’ products/category.
  1. Buyer Persona Model – the Right Person: An AI-model based either on standard personas, or your custom persona profiles, to 1) score the existing database and inbound leads based on their closest persona fit (including skills and other identifiers), and 2) find net-new contacts within accounts that lack the right buyers using persona targeting.
  1. Buyer Engagement Model – the Right time: Your external first-party scoring model that is built through either simple point scoring for engagement in a marketing program or interaction with website content. It is typically implemented within the Marketing Automation Platform (Marketo, Eloqua, Pardot, Hubspot, etc.) and the higher the score the more engaged the buyer.

The sales teams are very familiar with weighted models. Their revenue forecasts are all about applying a weighted average of how far a deal is in the selling process, what’s the urgency that the sponsor is applying, and how complex is the procurement process. It’s time for marketing to move beyond simple engagement scoring models based on activities to Weighted Engagement Scoring Models that are a combination of predictive signals and the engagement activity. In conclusion, the Weighted Engagement Scoring Model is the answer to striking the right balance between sales and marketing investment. By applying Fit, Intent, and Persona models, then bringing in your first-party engagement scores, you can determine which opportunities to focus on, then get the right campaigns in front of the right targets at the right times – as efficiently as possible. Then the Weighted Engagement Scoring Model makes the decision of when to route that lead to sales for the most effective and efficient selling. It’s all about finding and creating closeable business. Get the full Revenue Radar Guide to see how Leadspace can add the AI targeting to your scoring model.

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Leadspace persona score

A persona refers to a representation of a user or buyer segment that is created to better understand and design for the needs, behaviors, and preferences of that group. Persona creation is common in fields like marketing, user experience (UX) design, and customer service. It’s especially useful in sales and marketing when it comes to prioritizing leads for content marketing or sales engagement campaigns. Knowing someone’s persona allows for significantly more effective outreach. It’s much easier to customize your messaging when you know the person’s roles, capabilities and expertise. Knowing someone’s level of buying power is also critical, as it indicates whether or not that person is even capable of making the decision to buy your product. Obviously, all of this information is extremely valuable for personalized outreach and lead prioritization, but how do we ingest all of this information and determine someone’s persona? Hint: we don’t just make a guess based on their job title and ignore the rest of the information!

Early personalization and nurture efforts leveraged job titles alone to segment content campaigns or to bucket inbound lead nurtures or cadences. But job titles in the B2B space are not standardized, change frequently, and often give no real insight into the seniority, buying power or even specific functions the person serves within their company. On the sales side, reps will search around in their sales list vendor for companies they know and guess at who would be good to reach out to, by eyeballing job titles. Often job titles are incorrect and not up to date. As a result, persona-based marketing and sales campaigns/outreach can be off base. 

So where do personas fit into modern sales and marketing approaches? After you’ve determined the right companies with a propensity or “Fit” model (based on firmographics, demographics, and technographics), and determined which of those are ready companies with intent models, the third step is figuring out who are the right people to pursue within those companies by scoring their personas. Is their current role at the company in line with the personas of your historical successes? Does their persona typically make decisions to buy your type of product or service? Who does have the purchasing decision power at the company? Who might see the value in your product and be able to bring it up the chain of command quickly and effectively? Simply put, are they the right person to invest sales and marketing resources into?

A persona model uses AI-analytics to determine the best people for you to target. Persona models leverage standard and/or custom-built persona profiles to score the existing database and inbound leads based on how well they match your product’s buyer or buying team and historical success. Persona models enable you to actively find net-new contacts within accounts and set your sights on the right buyers for your product. Instead of just relying on a potentially noisey/generic signal like job title, persona models enable you to identify and target your key buyers using a much wider range of insights such as title, level, dept, job functions, and technology expertise. Additionally, applying exclusion logic and adding weights to individual signals enables you to optimize your model, prioritize the right buyers, and ensure you’re reaching out to them with the right message that’s tailored to them.

A persona score is essentially a “fit” score, but at the person-level. With a persona scoring model, you can hone-in on the department, level, right job title, role or expertise to go after by identifying the best contact in a company for your type of product.

With Leadspace’s Persona models, each buyer profile within your database is assigned a Persona score (0-100), where a higher score represents a closer match to the level, skills, department and buying center of that profile to your ideal persona. Leadspace includes over 80 off-the-shelf personas in the Leadspace Persona Library that span across all departments and job functions in the most popular B2B buying centers. Custom persona models can also be built and leveraged for specific buyer use cases.

With Leadspace Personas, you can create buying team scoring and net-new campaign audience members for digital campaigns. With our persona-matching scores, it’s easy to match content accordingly to optimize targeted nurture programs and content syndication at every step in the buyer journey. They can also be used to score entire databases to categorize prospects, leads and contacts into actionable content campaigns. When integrated into Salesforce or Dynamics, sales professionals can quickly assess the best persona from ambiguous job titles to understand the best lead or contact to pursue in an account.

Key Features & Use Cases for Persona Scoring

  • Route inbound leads directly to sales or specific nurturing streams based on Persona.
  • Recommended content stream by persona, or product-best offering typical in cross/up-sell scenarios.
  • Keep your database current as job, skill or expertise changes with up-to-date categorization that leverages personal demographics and buying behaviors from social signals and interests to mathematically assign personas beyond nondescript job titles.
  • Laser-focus campaign segment members by leveraging our 80 off-the-shelf personas as well as your own custom personas.
  • Match your Marketo, Pardot or Hubspot scores against your Salesforce prospects and accounts, then apply engagement scoring on top of your persona scoring to identify the best engaged contacts/prospects to go after (lowering regional sales and marketing list buys).

But remember, persona scoring is a relatively new technology that isn’t offered by most lead scoring model providers yet. However, if your Customer Data Platform (CDP) does leverage AI-powered persona scoring models, you can finally add the personal touch that’s necessary to achieve unified, best-in-class, active buyer profiles at scale. This means active buyer profiles that automatically populate with personal demographics and buying behaviors from social signals and interests to statistically assign personas instead of nondescript job titles. Match the right people and optimize your marketing and sales engagement by leveraging a combination of predictive AI models to accurately prioritize the top 25% of leads that deliver 60-80% of your business. After you’ve achieved persona scores, start creating and activating precision nurture segments based on engagement scores and other account or buyer personalization insights to further optimize your conversion rates.

In short, do not rely on job titles to make persona-based decisions because they’re not standardized, and there’s so much more that goes into someone’s persona than their job title. Automating the classification and weighting of role, capabilities, expertise, and buying power is a lifesaver when compared with manually integrating all of that information into your buyer profiles, then ranking and regularly updating them. You can automate the process by implementing a CDP with AI scoring models to quickly and effectively identify and prioritize the right people to invest your sales and marketing resources in! For more information about using AI persona scoring models to prioritize your most closeable buyers, get the Leadspace Revenue Radar Guide here.

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Identifying the best leads is essential for any company’s success as it helps focus resources and efforts on prospects most likely to become customers. In marketing, we have limited resources (employees, money, time) – we rarely have the ability to “spray and pray” with our sales & marketing efforts. Going at the wrong person before they’re ready is time-consuming and expensive, and can ultimately hurt you in your ability to successfully reach them down the line when they actually are ready for your product. Either hit the nail right on the head the first time or you’ll set yourself backwards or miss the opportunity entirely. The process of lead identification can vary depending on the industry, company size, and target market, but here are some common strategies used by most companies:

  • Defined by the ICP (Ideal Customer Profile)
  • Weighted and/or predictive lead scoring
  • Buying behaviors / Engagement
  • Source & channel analysis
  • CRM data
  • Discovery & qualification processes
  • Feedback from sales teams
  • Customer feedback
  • Data analysis & machine learning
  • Continuous refinements

By combining these approaches, companies can optimize their lead identification process and focus their efforts on the leads most likely to become valuable customers. While all of these factors are important to consider, without one of them, the rest will provide minimal, if any, value.

No matter what, you have to start with defining your Ideal Customer Profile (ICP). Companies begin by creating detailed profiles of their ideal customers. These profiles typically include demographic information, buying behavior, pain points, and preferences. By knowing who their best customers are, companies can target similar prospects. More advanced methods to create your ICP can come from looking at all the customers or prospects who have converted and understanding their shared common attributes.  

Once you’ve determined your ICP and discovered your Total Addressable Market (TAM), it’s time to decide which leads to pursue. To determine where to focus your sales and marketing efforts, you need a way of scoring your leads across your TAM by their propensity to buy your product or service. Before you consider buying behavior, CRM data, feedback, discovery process, channel analysis and continuous refinement, you need a place to start. If you start with buyer behavior, you might spend months chasing down a lead that showed intent, only to find out their company wasn’t realistically capable of buying your product to begin with. Simply put, buyer behavior doesn’t help unless you know they are from a company likely to buy your product. Propensity to buy or predictive lead scoring is one of the best ways to take the guesswork out of lead prioritization.  

So what is lead scoring? Lead scoring is a system that assigns a numerical value to each lead based on their characteristics and interactions with the company. Positive actions, such as website visits, email engagement, and content downloads, increase the lead score. The higher the score, the more likely the lead is to be considered “hot” or “qualified.” Advanced lead scoring takes into account not only the behavior or actions but also gives “extra points” for companies with a high fit score.

A company fit score indicates whether a company is a good fit to be your customer—and worthy of sales attention. It’s calculated with fit data – details that make up a company’s firmographic profile (such as industry, country and number of employees).

A fit score, also known as a propensity to buy score, at the company level is a numerical representation of how well a lead aligns with your ideal customer profile (ICP) of a company. It helps businesses evaluate the suitability of a lead based on predefined criteria and how closely the lead matches the characteristics of their best customers.

The fit score is determined by looking at all of the positive outcomes over the last few years. This is typically about the leads that have converted into revenue. A Machine Learning model is developed in consideration of all the various factors that are common attributes across those positive outcomes. The specific criteria used to calculate the fit score may vary from one company to another, but common factors include:

  • Demographics: This includes data such as the lead’s location, company size, industry, and job title. If a lead’s attributes match the company’s target demographics, it receives a higher fit score.
  • Firmographics: For B2B companies, firmographics play a crucial role. Information about the lead’s company, such as revenue, number of employees, and business structure, helps to assess the fit.
  • Engagement (Person-level): The level of interaction that person has with your company’s marketing materials, website, and content is often a significant factor.
  • Behavioral Data – Intent (Company-level): Tracking the lead’s online behavior, such as the pages they visit, the content they download, and their time spent on the website, provides insights into their interests and alignment with the company’s offerings.
  • Technographic Data: For tech-related products or services, technographic data about the lead’s current technology stack and software usage can be relevant.
  • Referral Source: The lead’s source can indicate how well it aligns with the company’s target audience. For example, leads coming from specific marketing campaigns or referrals might be more likely to be a good fit.
  • Data Analysis and Machine Learning: In some cases, advanced data analysis and Machine Learning algorithms are used to evaluate historical data and patterns to determine the fit score.

Once these factors and buying signals are considered, the fit score is calculated using a scoring system that assigns weights to each signal based on its importance or “lift” (a numerical representation of the percentage change in odds of conversion based on that particular signal being triggered). The data is then aggregated, and the lead is assigned a fit score. The fit score helps sales and marketing teams prioritize leads, focusing their efforts on those with higher fit scores and a better chance of becoming valuable customers.

Many times, companies determine fit with firmographics alone, which is much better than skipping fit all together, but they can significantly compound the success of their fit scoring by factoring technographics (web technologies and install base technologies) into the equation. In software or technology companies, often the best Customer Data Platforms (CDPs) and fit providers will generate a fit score which takes technographics into account to provide the most accurate propensity score available.

It’s worth noting that while a fit score is a valuable tool in identifying the right companies to pursue, it is typically one part of a broader lead scoring system, which may also include intent scoring (to find the ready company), persona scoring (to find the right people), and engagement scoring (to find the ready people). Together, these scores provide a more comprehensive view of a lead’s potential and likelihood to buy your product or service.

In short, the most important step in prioritizing closeable business is the first step – determining fit scores across your Total Addressable Market (TAM)! Do not skip this step or else you’ll waste a tremendous amount of time, money and effort in pursuit of leads that were destined to be a bad bet from the start. For more information about using scoring models to optimize your lead prioritization, check out the Revenue Radar guide.

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Find the right quality leads with Leadspace

If someone is interested in your product or service, is it necessarily true that they will convert into a paying customer in the future? Does a higher level of interest translate into higher odds of sales conversion? How do we know if the way we are measuring and assessing their interest is accurate? Is that interest based on accurate, up-to-date data? Do they need your product now? Are they the right person within the company to pursue? Do they likely have the budget and authority to fund your product? Is their company aligned with your Ideal Customer Profile (ICP)? Simply put, are you confident that the data and signals you have validate that a lead is truly ready, able, and eager to buy your product or service? These are questions that need to be answered in order to classify a lead as a “quality lead.”

When it comes to lead generation, we often focus on hitting KPIs and generating as many leads as possible – forgetting that quantity doesn’t equal quality. At the end of the day, the quality of those leads is the factor that most affects the bottom line of your business. You won’t grow your business with low quality leads. Consider the amount of time, money and effort that goes into pursuing every single lead you generate. And the odds are against quality.  According to lead generation statistics, only about 20% of B2B leads become sales-ready!

However, by accurately and proactively identifying those 20% that are high-quality leads in advance, we can cut an enormous amount of waste from our sales and marketing process. This is where AI scoring models come into play. A scoring model analyzes thousands of account or lead buying signals (demographics, technographics, firmographics, etc) to automatically score and categorize how well they match up with the Ideal Customer Profile. Scoring models are primarily used to assist sales and marketing teams in identifying the best leads for follow up, responding to them appropriately, and increasing the rate at which they become customers by prioritizing the leads that are most likely to buy. With scoring models, we can also analyze our Cost Per Lead (CPL) and determine what value to assign each lead based on their actual degree of compatibility with our products or services. Using multiple scoring models in conjunction with predictive models is a particularly efficient way of effectively identifying and prioritizing closeable business interest. Let’s dive into how we can throw out most of our leads by applying AI models to filter them by the dimensions of a “quality lead.”

While the specific criteria for a quality B2B lead may vary depending on the industry and business goals, there are several dimensions to consider in combination that indicate the potential for a lead to be considered “quality” or “high quality.”

  • 1. Relevance
  • 2. Budget and resources
  • 3. Fit with your value proposition
  • 4. Need and pain points
  • 5. Timing
  • 6. Decision-making authority
  • 7. Engagement and interest
  • 8. Relationship potential

A quality B2B lead should first and foremost be evaluated, and filtered, by its relevance to your business and alignment with your target market. They often belong to the industries or segments where your product or service has enjoyed success. The more closely a lead matches your ideal customer profile, the higher the likelihood of conversion. It’s also essential to determine whether a lead has the financial resources to afford your products or services. A quality B2B lead should perceive the unique value and differentiation your business brings, recognizing how your offerings can solve their problems more effectively than alternative solutions. This can be determined by a “fit” or propensity score, which indicates how well the company matches your Ideal Customer Profile (ICP) through firmographic and technographic comparative analysis. A strong fit score can signal a 2-12X increase in conversion rates.  This is the first model that should be applied to your individual lead to determine if it’s even the right type of company for your product. If the lead has a low fit score, you should throw it out! They’re more than likely not going to buy your product (even if your intent model says otherwise). Only after we’ve filtered for leads with a high company fit score should we take intent scores into account.

Your value proposition should resonate with the lead’s specific requirements and align with their expectations. Simply put, are they interested in your type of product or service? A quality B2B lead should perceive the unique value and differentiation your business brings, recognizing how your offerings can solve their problems more effectively than alternative solutions. A quality B2B lead should have a genuine need for your products or services. They should be facing challenges, pain points, or goals that your offerings can address effectively. Understanding their specific needs allows you to tailor your sales approach and demonstrate the value you can provide. Should you call them now, or nurture them for the time being? The timing of a lead’s need for your products or services is crucial. A quality B2B lead should have a relatively immediate or near-future requirement for your offerings. Timing is often influenced by various factors, such as business cycles, industry trends, or upcoming projects within the lead’s organization. Are they currently looking for a solution that you can offer? All of these dimensions can be determined by applying an intent model to your leads. Knowing if people within that company have been searching for your product, your competitor’s product, or a specific type of solution will indicate their current expectations and needs level of alignment with your value proposition and the urgency with which they’re looking to have their needs met. With an intent model, you can identify if that right company is also a ready company. By measuring the increasing or decreasing intensity of intent – the value – you can judge when to truly engage and when it may be too late as well.  If they’re high fit but low intent, put them in a nurture cadence to warm them up to your type of product or service before reaching out directly.

After we’ve determined a lead is the right company, and the ready company, we need to determine the right people to pursue. A quality lead should have the authority or influence to make purchasing decisions within their organization. It’s important to identify the individuals within the buying committee by persona, then validate that they meet the high-value lead qualification. Identifying decision-makers or key stakeholders ensures that you are engaging with individuals who have the power to initiate and complete a transaction. Does their role at the company line up with the personas of your historical successes? Is their persona typically responsible for making decisions to buy your type of product or service? Who makes purchasing decisions at the company? Or who might see the value in your product and bring it up the chain of command quickly and effectively? This is where a persona model comes into play. A persona model analyzes and delivers a relative ranking of a prospective buyer – the quantification of the individual fit to an ideal professional profile. With a Persona score, you can increase conversion odds by 30% and narrow in on the department, level, right job title, role or expertise to go after by identifying the right person in that company who is most likely the right contact for your type of product. You might know a lead is from a right and ready company, but if they’re the wrong type of person, it will be much harder to find success with that particular lead. In this instance, it would be smart to search for people within that company who have high persona scores, and pursue those people instead.

After we’ve applied AI models to identify the leads that are the right company, the ready company and the right person, the final step in filtering for our high quality leads is to identify the ready person – the one who shows genuine interest in your business and actively engages with your marketing efforts demonstrates a higher potential for conversion. They may have interacted with your website, subscribed to your newsletters, attended webinars, or requested additional information. Proactive engagement indicates a level of intent and commitment that is much easier to penetrate with a direct sales approach. To find the ready person, we should determine their degree of engagement at the person-level, using your marketing automation platform – Marketo, Eloqua or Pardot for example. Has that individual been on your website? Who specifically has been searching for your type of content, or has engaged with your previous marketing efforts? Did they “raise their hand” and ask for contact with sales?  With this final piece of information, you can look at the right people from the right and ready companies, and focus in on the people who are ready, able, and eager to buy your type of product or service – ensuring you don’t waste time, money and effort chasing down leads that aren’t likely to close.

Finally, assessing the potential for a long-term business relationship is important, especially for complex or high-value B2B sales. A quality lead should have the potential to become a loyal customer, provide repeat business, and potentially act as a brand advocate.  In some cases, the specific solution or number of products that they have can be used to predict lifetime value.   If they score well across your scoring models, odds are that they do have potential for a long-term business relationship. By first applying these 4 scoring models to prioritize our leads, we’re able to utilize our resources wisely in targeting only the accounts with the likelihood to become a high-success relationship on all fronts. Offering your perfect solution to the right company, the ready company, the right person, and the ready person allows you to provide the personalized touch and near frictionless sales and marketing efforts that lead to trusting and long-lasting business relationships.

By evaluating leads based on these criteria, businesses can focus their resources on pursuing leads that have a higher probability of converting into valuable customers, improving sales efficiency and effectiveness. Prioritizing our best leads reduces our overall lead volume to minimize our team’s workload and ultimately save valuable time and resources across our sales and marketing efforts. When you don’t take the quality of leads you are generating into account you end up spending a lot of time convincing the low-quality leads who have a slim chance of converting. By applying these 4 types of scoring models in the right order, we can effectively score our leads as they come in, and quickly eliminate the vast majority of our leads to focus on the 20% of leads that are truly ready, able, and eager to buy our product or service. To learn more about using AI-scoring models to filter, sort, and prioritize our leads to significantly boost productivity, close-rates, and revenue, check out the Leadspace Revenue Radar Guide.

Don’t forget, effectively applying scoring models and deriving accurate, actionable insights from your data all starts with discovering your Total Addressable Market (TAM) and building complete, accurate, and up-to-date buyer profiles at each hierarchy across it. For more information on the importance of, and how to achieve, high quality buyer profiles, check out the Leadspace Profiling Guide.

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leadspace b2b customer data platform

Three out of four B2B sales and marketing teams rely on intent data to prioritize ABM outreach. Intent data is incredibly important as it provides insight into which companies are searching for you – i.e. which companies are demonstrating some level of intent to use your type of product (or specific product). While that’s useful information to have when trying to put the right type of content in front of the right companies, it simply doesn’t paint the full picture necessary to target deals in the B2B world. In fact, intent data can even point us in the wrong direction and inspire our sales people to confidently follow a rabbit hole that goes nowhere fast. Let’s explore how to use intent data effectively and avoid diving head-first into a pit of bad leads.

What is intent data?

Intent data tells you when companies are actively researching online for a solution, and which products and services they are interested in, based on the web content those companies consume. Marketing and sales leaders that use Intent data have an advantage by understanding which companies are ready to buy – and avoid wasting time and money on those that are not.

There are 2 major types of intent – first-party intent and third-party intent.

First-party Intent data is information collected about your audience or customers from your internal programs and digital properties:

  • Behavioral data, actions, and interests shared across digital environments such as your business’ website or app 
  • Data collected in your CRM 
  • Data from subscription campaigns 
  • Information collected from social media efforts
  • Offline surveys, forms, and questionnaires

Third-party Intent data is information collected from outside sources that provides an external view of that company’s intent. There are several ways to source third-party intent data, and it’s important to use a trustworthy source that’s transparent about the methods of collection:

  • Behavioral data, actions, and interests shared across digital environments from a cooperative of publishers, websites and apps
  • Bidstream data that originates from a publisher’s website or app that passes some site visitor and page-level data  during the real-time bidding process for programmatic advertising
  • Behavioral data collected by a publisher’s owned and operated network of websites and apps

Why is using intent data by itself not effective?

Regular intent data tells you which companies are searching globally for the intent topic(s) (keywords or phrases) that you’ve selected within the confines of your intent provider. For example, your intent data might indicate that people from IBM are searching for a specific topic that you’ve ranked as a high priority, which is great to know – but it doesn’t tell you who, or even what region, those searches originated from. On the other hand, there is metro-level intent data which provides further insight into the specific metro area that those searches are coming from. 

While metro-level intent data provides a deeper layer of accuracy, it still doesn’t paint the picture you need to effectively target the right people within the right companies with the right content at the right time. A company might be searching for your product, but that doesn’t mean they’re even likely (from your perspective) to buy your product. Are they a big enough company? Are they in the right industry? Are they B2B? Do they look anything like the companies you’ve closed business with in the past? Essentially, do their demographic, firmographic, and technographic makeup indicate a company who is likely to buy your product? Searching for your product doesn’t indicate ability to buy. With intent alone, you’ll know people at a company have searched for your product, but you don’t know if that company “fits” your product. Do you even want IBM? It’s great to know who is interested in your product, but that doesn’t tell you who to actually focus your time, money and effort on. To effectively target closeable business, we need visibility into all of those qualifiers, and filter them in the correct order. Before we dive deeper into the problems with using intent data by itself, let’s explore the correct steps towards identifying closeable business.

What’s the best way to identify closeable business?

There are 4 steps to successful B2B targeting – applying four AI-models across your Total Addressable Market (TAM) to effectively score, filter and prioritize closeable business:

  1. Propensity = the right companies
  2. Intent = the ready companies
  3. Persona = the right people
  4. Engagement = the ready people

It starts with discovering your TAM and generating your Ideal Customer Profiles. The next step is determining which companies to go after. This involves using your historical first-party data to develop an Ideal Customer Profile (ICP), then comparing it throughout your TAM with an AI-based propensity model. By determining how closely each target company matches, or ”fits” your product, a propensity scoring model predicts the increase or decrease in the odds of a successful conversion. This is the first stage of honing-in on the best companies to target.

The second step is determining which of the companies are actually ready to buy. This is where an intent scoring model is used to determine intent at the product level – ensuring it’s the right time for the right company. We determine intent by a company’s search activities. Many of you may be buying weekly intent feeds delivering the names of companies who are searching for the terms that you prioritized. Knowing that a company’s employees have been actively searching in your field of expertise with either new high intent or sustained intent enables you to focus your efforts on the best companies that are truly ready to buy.

The third step is figuring out who are the right people to pursue within those companies by scoring their personas. Does their role at the company line up with the personas of your historical successes? Is their persona typically responsible for making decisions to buy your type of product or service? Who makes purchasing decisions at the company? Or who might see the value in your product and bring it up the chain of command quickly and effectively? With a persona scoring model, you can narrow in on the department, level, right job title, role or expertise to go after by identifying the right person in that company who is most likely the right contact for your type of product.

The final step in prioritizing closeable business is applying an engagement scoring model to those personas, or specific people at the company (or buying center), to identify which of them are actually ready to buy. This means scoring their engagement at the person-level, using your marketing automation platform – Marketo, Eloqua or Pardot, for example. Has that individual been on your website? Who specifically has been searching for your type of content, or has engaged with your previous marketing efforts? With this final piece of information, you can focus on the right people from the right companies who are ready, able, and eager to buy your type of product or service – ensuring you don’t waste time, money and effort chasing down leads that aren’t likely to close.

What are the risks (costs) associated with using intent data alone?

The main issue with relying on intent data alone is that it doesn’t indicate a company’s likelihood of buying your product.  Usually, companies will give intent to their sales people, who just open up their sales list vendors and search around through the companies that showed intent and guess at who would be good to reach out to – confidently spending their valuable time and resources chasing down leads without even knowing whether that company is actually a good fit for their product. Additionally, with intent data alone, sales people typically won’t have any visibility into the specific people who searched for their product within those high intent companies.

Essentially, using intent by itself encourages your salespeople to kick off a semi-random search for companies to reach out to, spending money buying contacts then eyeballing job titles (which isn’t the best way to pick the best buyers to begin with) not even knowing if the title’s are correct and up to date. They don’t even know which ones have already been lit up by their sales/marketing teams. They’ll likely do all of this in ZoomInfo without even looking at their own database or leveraging their historical first-party data and ICP as most companies don’t connect their Marketing Automation Platforms (Marketo, Eloqua, Dynamics) to their Salesforce instance – missing out on important historical context to the leads they’re pursuing.

The opportunity cost of aiming at the wrong company and person when time is of the essence is significant. Start with sales teams spending hours a week searching around.  Add to it the hard cost of money spent on license costs from popular sales list vendors (such as, LinkedIn Navigator), etc. It all adds up.  Investing those resources would be worth it if the business was closeable, but with intent alone, salespeople don’t even know if it’s closeable! Sales people getting it wrong means zero return on their effort, and not meeting their quotas. We want to ensure our salespeople get it right – that means giving them more than just intent data. With intent alone, we risk the likelihood of not closing, wasting time and effort pursuing bad leads, passing on bad information for ABM targeting to our marketing team, and wasting money on data licensing. How much time do sales people spend searching for contacts based on incomplete signals? Intent is critical to driving closeable business, but it’s incomplete/insufficient.

How to use intent data effectively? By using it in combination with other models!

In short, intent alone is not really closeable intent if the company doesn’t match your ICP. Intent data is incredibly valuable, but only if you’re applying it to companies that already fit your ICP, which most of us fail to do. Additionally, intent doesn’t take you all the way to the exact persona and specific person or buyer to pursue within a company. To effectively use intent data, we need to first identify accounts with high propensity, then apply our intent model, then apply persona and engagement scoring models to truly hone in on the right person within the right company at the right time to best improve our odds of closeable business and minimize the waste spent on pursuing leads that aren’t likely to pan out.
Intent is a lot like using a flathead screwdriver on a Phillips screw. It’s better than nothing and can work in a pinch, but it takes a lot more effort to make it work and it can ultimately damage the screw. For more information on successfully applying propensity, intent, persona, and engagement models in combination to identify the companies and the people within them who are ready, willing, able, and eager to buy your product, check out the Leadspace Revenue Radar Guide and hop on the fastest road to revenue today.

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audience engagement

Throughout this Revenue Radar blog series, we’ve discussed how to use Leadspace predictive AI models to determine the companies within your Total Addressable Market (TAM) who need your product (highest Fit scores), which of those companies are actually ready to buy your product (Intent scores), and which buyer Personas are best for you to pursue (highest Persona score). Now let’s move on to the final step to optimizing your Revenue Radar – determining and prioritizing the specific people to pursue. Have these individuals been on your website? Who specifically has been searching for your type of content? Who specifically has engaged with your previous marketing efforts? With this final piece of information, you can focus on the right people from the right companies who are ready, able, and eager to buy your type of product or service – ensuring you don’t waste time, money and effort chasing down leads that aren’t likely to close. This is where an engagement model comes into play.

An engagement scoring model is a tool that monitors and scores engagement (account buying teams, contacts, leads and prospects), and applies scoring based on the specific user actions and digital activity specific to customers’ products/category. This involves scoring their engagement at the person-level using your marketing automation platform (Marketo, Eloqua or Pardot for example). An engagement model – often called a scoring engine –  measures the amount of effort and time that someone has spent on your website, in your events or in meetings. Not all engagement is scored the same. For example, if someone clicked on an email they might only receive a few points, whereas if they downloaded high-value content they’d receive several points. And if they asked to be contacted by a sales person – the so-called hand raising – they might be immediately scored and routed to a sales person.  A person’s overall engagement score will be the total sum of all points assigned for each of their individual incidents of engagement.

Engagement Model Use Cases:

  • Optimize your ABM investments – create and activate precision segments by level or type of interest with engagement and other account or buyer personalization to optimize conversion rates. 
  • Leverage Leadspace Personas with engagement scores to populate and design customized persona-based buyer journeys and engage top prospects with relevant and compelling content through the right channel, at the right time.
  • Match your Marketo, Pardot or Hubspot scores against your Salesforce prospects and accounts, then use persona scoring with engagement scoring to identify the best engaged contacts/prospects to go after for outbound calling. Use this to optimize meetings for sales calling campaigns.

According to a recent marketing leadership conference survey, more than 80% of companies use engagement models alone to score leads – but be aware, engagement models can often be very noisy as they can be used by non-buying researchers, competitors or job seekers. It’s important to understand that engagement is best used in context. Is this person at a company with a high predictive fit? Has that company exhibited intent for your products? Is this individual a persona that matches the type of buyer that is likely to have the budget and need for your product? If the profile of that person is a great fit for these first three signals, buyer engagement shows that he or she is warmed up and showing buying interest. All of these signals can be put to work with products like Leadspace for Salesforce to deliver the right account contact details, buying signals and propensity-to-buy scores and an understanding of their individual level of engagement directly in front of your reps to prioritize leads and opportunities in their pipeline.

Revenue Radar Conclusion:

Account targeting is easy once you have models and put the four radar signals to work – Fit, Intent, Persona and Engagement. Better sales/marketing targeting is all about “lift” – territory and ABM investment based on GTM science. With account targeting, you can prioritize account assignments by tiering accounts into categories by closeable odds (the right accounts) and accounts that are showing intent (the ready accounts). Persona and engagement scoring takes it to the right person. With the right CDP solution, you can utilize AI-predictive models to filter your TAM further by firmographics, technographics, Fit, Persona, and Intent. This enables you to plan territories and target accounts beyond just job titles, locations, and company size, as it algorithmically determines who is likely to close and where to focus your sales and marketing efforts. In short, utilizing a CDP solution will give you the information you need to achieve closeable business as you target the right people, in the right company, with the right outreach, at the right time. Get the full Revenue Radar Guide to see how Leadspace finds your best customers with AI models for account targeting.

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Leadspace persona scoring for go to market

In our last few blogs, we discussed using Fit (propensity) models to determine the companies within your TAM who need your product, and how to determine which of those companies are actually ready to buy with Intent scores. Now you need to figure out who are the right people to pursue within those companies. Does their role at the company line up with the personas of your historical successes? Is their persona typically responsible for making decisions to buy your type of product or service? Who makes purchasing decisions at the company? Or who might see the value in your product and bring it up the chain of command quickly and effectively? This is where an AI-powered persona scoring model comes into play. With a persona score, you can narrow in on the department, level, right job title, role or expertise to go after by identifying the right person in that company who is most likely the right contact for your type of product.

After determining which companies fit your ICP and have expressed intent in your type of product, you need to establish where those buying centers are. Navigating the underlying hierarchies of large enterprises – trying to penetrate accounts with layers of subsidiary and parent-company relationships, with offices distributed across different geographies – is an incredibly complex and cumbersome process. In order for your ABM efforts to succeed here, you’ll need a data asset that can provide true visibility into identifying both where the key buying centers are located, how they’re connected, and where the true decision makers and buying committees actually exist – being able to navigate profiles throughout hierarchies at person, account, and buying center levels.

A persona scoring model uses AI-analytics and machine learning to determine the best personas for you to target. Persona models leverage standard or custom persona profiles to score the existing database and inbound leads based on their closest match. Persona models enable you to find net-new contacts within accounts and identify the right buyers for your product. Instead of just relying on a potentially fuzzy/generic signal like job title, persona models enable you to identify and target your key buyers using a much wider breadth of insights such as title, level, department, job functions, technology expertise. Additionally, applying exclusion logic and adding weights to individual signals enables you to both prioritize the right buyers, as well as ensure you’re reaching out to them with the right message that’s tailored to them.

With Leadspace’s persona scoring model, each buyer profile within your TAM is assigned a Persona score (0-100), where a higher score represents a closer match to the level, skills, department and buying center of that profile to your ideal persona. There are over 80 off-the-shelf personas in the Leadspace Persona Library. They range across all departments and job functions in the most popular B2B buying centers. Custom persona models can also be built for specific buyer use cases.

Leadspace Persona can be used to create net new campaign members for advertising or digital campaigns, and with our persona-matching scores, it’s easy to match content accordingly for the targeted nurture programs and content syndication. They can also be used to score entire databases to categorize prospects, leads and contacts into actionable content campaigns. When integrated into Salesforce or Dynamics, sales professionals can assess the best persona from ambiguous titles to understand the best lead or contact to pursue in an account.

Key Features & Use Cases

  • Enable inbound leads to route directly to sales or specific nurturing streams based on Persona.
  • Recommend content stream by persona, or product-best offering typical in cross/up-sell scenarios.
  • Leverage personal demographics and buying behaviors from social signals and interests to statistically assign personas instead of nondescript job titles.
  • Laser focus campaign segment members by leveraging our 80 off-the-shelf personas or create your own custom personas.
  • Identify the right and ready contacts for each territory or campaign.
  • Match your Marketo, Pardot or Hubspot scores against your Salesforce prospects and accounts, then use persona scoring with engagement scoring to identify the best engaged contacts/prospects to go after (use this to lower regional sales and marketing list buys).

With a CDP that leverages AI-powered persona scoring models, you can add the personal touch to your unified profiles, creating the best buyer profiles that automatically populate with personal demographics and buying behaviors from social signals and interests to statistically assign personas instead of nondescript job titles. This further enables you to optimize your marketing and sales engagement by leveraging AI predictive models to accurately prioritize the top 25% of leads that deliver 60-80% of your business. You would also be able to create and activate precision segments with engagement and other account or buyer personalization to optimize conversion rates.

And finally, scoring predictions and recommendations are delivered in real-time for active buyer profiles, as models are updated and refreshed on a quarterly or as-needed cadence. Not everyone wants your product, but by segmenting and prioritizing your TAM by Persona scores (especially in conjunction with Fit and Intent scores), you can direct your sales and marketing resources at the ones who do, targeting the right type of people at the right time – before your competitors do!

But what’s the next step? Now that you know which companies need your product (highest Fit score), which of those companies have been actively searching for your kind of product (highest Intent score), and who are the right type of people to pursue within those companies (highest persona score), the final step to achieving closeable business is to find the specific people within those companies to reach out to. Stay tuned for the next blog in our Revenue Radar series where we will explore how to find the right person to pursue using Engagement scores. In the meantime, get the full Revenue Radar Guide to see how Leadspace finds your best customers.

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Leadspace intent model, customer data platform

Finding the right type of companies within your TAM that need your product is a huge step towards closeable business, but you still need to figure out which of those companies are actually ready to buy your product – this is where intent comes into play. We can determine intent by a company’s search activities. Most of you are buying weekly intent feeds delivering the names of companies who are searching for the terms that you prioritized. With an intent model, you can automatically achieve AI-generated scores to identify low, medium or high intent. By utilizing Fit scores in conjunction with Intent scores, you can determine the right companies as well as the ready companies. 

An Intent model monitors user interest (first- and third-party, known and unknown), and applies scoring based on the level of intent activity specific to customers’ products/category. Knowing that a company’s employees have been actively searching in your field of expertise with either new high intent or sustained intent enables you to focus your efforts on the best companies that are truly ready to buy.

Intent is a great way to time your engagement with companies that are expressing in-market buying signals in your products and solutions. However, it’s easy to over-index on intent and just end up with more noise than results, because not every company that expresses interest is the right company for your business. Over-relying on intent often leads to sales reps chasing potentially bad accounts and deals, or spending excessive money on ads that are targeting companies that just aren’t the right fit. However, intent is particularly effective when applied to the accounts that you already know have high Fit scores (the ones that look like customers who have bought specific products, have led to your biggest deals, or have generated the most LTV (lifetime value), etc). 

A company might show high intent, but they’re not a good match for your product overall – pursuing such a lead can be a waste of time and resources. But if you’ve already used Fit models to determine that a company needs your product (and is able to buy it), you can further filter by intent to find the companies who are ready and willing to buy your product. Intent models compound the success of your Fit models, further increasing the odds of achieving closeable business as you focus sales and marketing efforts in order of likelihood to buy – enabling you to achieve ROI faster and more efficiently for overall revenue optimization. 

With Leadspace, once you’ve discovered your TAM, buyer profiles are automatically created at the person, account, and buying center levels. Sales and marketing teams can then amplify buyer profiles across their TAM with Leadspace predictive Fit scores, and amplify them even more with Leadspace Intent. Each buyer profile within your TAM is assigned an intent score (High, Medium, Low, or No Intent), where a score of “High Intent” represents that people from that account have demonstrated intent by searching for terms relevant to your product that you’ve determined ahead of time.

The best intent signals are targeted at specific market segments. Market segments that can be identified by the patterns of terms that they are using to conduct online searches. It’s useful to align your intent model with SEO/SEM keywords. Branded terms (like your company name or a competitor’s name), product categories, event signals (headquarters move or business milestone), competitive categories (competing or adjacent categories sold against or with your product) are all words that can be chosen to identify a product or region-specific intent signal. The highest intent signal can be the name of your own company or product.

Use Cases and Key Capabilities of Intent Models:

  • Enable inbound leads to be directly routed to sales or specific nurturing streams based on intent scores.
  • Utilize AI-driven intent scoring to enable customer success to proactively improve customer retention and prevent churn.
  • Improve BDR outbound and digital marketing conversion rates by utilizing AI/ML-driven intent scoring models to more intelligently curate ABM audiences and improve ad bidding strategies.
  • Enhance your active profiles with Fit and Intent models to automate insights for complete buyer profiles.
  • Identify the top strategic accounts for investment by focusing on those who are most likely to buy your product.
  • Monitor your target accounts for in-market buying signals, uncovering and targeting potential opportunities before competitors, and improving sales and marketing intelligence for more timely and effective outreach.
  • Power your Go-to-Market with CDP data & activation using multi-source aggregation and topic/trigger weighting.
  • Identify the right and ready ABM accounts for each territory or campaign. 
  • Match your weekly intent vendor signals to the ICP criteria for each of your Salesforce accounts and integrate account AI models and intent signals directly into Salesforce.
  • Prioritize outreach to accounts based on predictive Fit, weekly intent surges, and product-specific intent scores.

By enhancing your active profiles with Intent (and Fit) models, you can automate insights and enable your marketing team to achieve complete buyer profiles and leverage decisioning models to quickly seek out and achieve closeable business. This takes the guesswork out of identifying the top strategic accounts for investment by focusing on those who are most likely to buy your product. Put the right account contact details, in-market intent buying signals and propensity-to-buy scores directly in front of your reps to prioritize leads and opportunities in their pipeline.

In short, with a powerful CDP solution, you can turbocharge your growth strategy with AI models to identify the highest-returning market segments and optimize territory assignments to maximize sales effectiveness. But what’s the next step? Now that you know which companies need your product Predictive (Fit model), and which of those companies have been actively searching for your kind of product (Intent model), now you need to figure out who are the right people to pursue within those companies. Stay tuned for the next blog in our Revenue Radar series where we will explore how to find the right type of person to pursue using Persona models. In the meantime, get the full Revenue Radar Guide to see how Leadspace finds your best customers.

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ai driven segmentation

Finding the right type of companies for your product by hand is a cumbersome process that’s error prone and heavily relies on guesswork. With competition at an all time high, B2B sales and marketing teams need a way to automate this process, and replace guesswork with data-driven insights to reach their customers before their competitors do. This is where a Customer Data Platform (CDP) with AI-scoring models comes into play. 

You’ve already discovered your Total Addressable Market (TAM), identified your average deal size, and generated your buyer profiles – now you need to determine which companies to go after. This involves using your historical first-party data to develop an Ideal Customer Profile (ICP), then comparing it throughout your TAM by either basic firmographics, demographics, and tech install or ideally from an AI-based predictive Fit model. By determining how closely each target company matches, or fits your product, it calculates the increase or decrease in the odds of a successful conversion. This is the first stage of honing in on the best companies to target.

A predictive Fit scoring model is an AI-driven decisioning capability built from your historical conversion data set (closed/won opportunities) that leverages thousands of 3rd party data signals. It applies scoring that indicates a company’s and/or person’s likelihood to buy your product. This likelihood is termed conversion lift and parsed into lift buckets. Conversion lift is the multiple applied to the average conversion of your leads or opportunities. Leadspace Predictive Fit models categorize lift into four buckets – the top two buckets typically represent the 25% of the leads or opportunities that represent 50-80% of a positive conversion. Lift models can be configured for revenue, pipelines, or meetings/leads. It brings together firmographics, specialties and expertise, tech install, and technographics (and sometimes demographics, if it’s a lead model) from the account or buyer profiles within your TAM. It then assigns predictive scores that represent how well they “fit” your product based on your historical data.

Predictive Fit models enable us to go beyond reliance on subjective sales insights, static firmographic data, or potentially noisy intent data. Ultimately, when you analyze your own business you quickly realize that your opportunity conversion funnel follows the 80/20 rule – where about 20% of your prospect accounts leads to 80% of your wins and revenue. To nail down the right strategy here, you need to ensure your models are leveraging AI and machine learning, and utilizing thousands of critical signals to uncover the accounts that look like your best customers. Predictive Fit Models can be used to create equitable sales territories, prioritize ABM investment accounts, target high-cost media investments, score inbound leads for sales qualification, identify accounts for high lifetime value and to score install base accounts for up-sell/cross-sell opportunity.

With Leadspace, B2B sales and marketing teams can amplify buyer profiles across their TAM with predictive Fit scores. With Leadspace Fit, each buyer profile within your TAM is assigned a propensity bucket (A, B, C or D), where a value of “A” represents that the profile best matches your historic success – indicating high propensity. The “A” bucket represents scores for the top 5%, “B” the next 20%, “C” the next 60% and D the bottom 15%. It is common to see a 3-6X conversion lift (3-6X the average conversion level) for the “A” bucket. Again, the best Fit models reflect the 25% of the business that represents 50-80% of the revenue or positive outcome.

 The Fit model is a weapon to gain competitive advantage in determining who the best customers/prospects are, understanding whitespace opportunities, and in turn, dramatically improving the effectiveness of sales and marketing programs for revenue optimization. Fit can minimize your regular external firmographics augmentation signal purchases with more accurate scoring – helping to identify the most important data at possibly a fraction of the cost. Scoring predictions and recommendations are delivered in real-time and models are updated and refreshed on an annual or bi-annual cadence. 

So what do we recommend?

  • Put the right account contact details, buying signals and propensity-to-buy scores directly in front of your reps to prioritize leads and opportunities in their pipeline. 
  • Master your whitespace then laser-focus campaign segment members by targeting your best Predictive Fit accounts – and use this to understand renewals, upsell and new logo strategies and to determine resource allocation. 
  • Take the guesswork out of identifying the top strategic accounts for investment by leveraging Predictive Fit models to quickly seek out and achieve closeable business.

In summary, Sales & Marketing teams can turbo charge their growth strategy with predictive Fit models by identifying the highest-returning market segments and optimizing territory assignments to maximize sales effectiveness. Not everyone wants your product, but by segmenting and prioritizing your TAM by propensity scores, you can direct your sales and marketing resources at the ones who do so you can target the right companies – before your competitors do! 

So what’s next? Once you’ve determined the companies within your TAM who need your product (highest Predictive Fit scores), you need to determine which of the companies are actually ready to buy. This means determining their intent at the product level – ensuring it’s the right time for the right company. Stay tuned for the next blog in our Revenue Radar series where we will explore how to find the right companies using Intent models. In the meantime, get the full Revenue Radar Guide to see how Leadspace finds your best customers.

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Leadspace Revenue Radar Ideal customer profile

As marketers, we aim to confidently and repeatedly deliver effective campaigns to the best opportunities within our target market – at the lowest possible cost. This means identifying our TAM (Total Addressable Market), developing our Ideal Customer Profile (ICP), and comparing our ICP throughout our TAM to determine which opportunities to focus on, then get the right campaigns in front of the right targets at the right times – as efficiently as possible. Doing this successfully means creating increasingly accurate, dynamic, and unified profiles of people, accounts and buying centers so we can properly prioritize and target opportunities with data-driven insurance that we’re delivering the right message to the right people at the right time. By implementing a powerful Customer Data Platform (CDP) with profiling capabilities, we can easily create accurate and up-to-date unified buyer profiles – but once we’ve built these profiles, how do we determine which ones to spend money and time pursuing? How do we prioritize them? That’s where Leadspace Revenue Radar comes in.

Leadspace Revenue RadarTM takes standard profiling capabilities to the next level by determining which buying centers, accounts or people to focus on based on their likelihood to buy your product. This allows you to segment your TAM by predictive Fit, Intent, and Persona scores, then bring in your 1st party engagement to find the right companies and people to pursue within them. Basic or advanced profiling tools populate profiles with various types of firmographics and technographics such as company revenue, size, industry, sub industry, region, ownership, website technologies, installed base technologies, expertise, and specialties. Enterprise Profiling Models, like Revenue Radar, take the next step and analyze them as buying signals to generate algorithmic insights and drive you directly towards closeable business. 

Leadspace’s Revenue RadarTM: The 4 Buying Signals that matter.

  1. Fit / Propensity
    A Leadspace AI-Model built from your historical conversion data set (opportunities). Applies scoring that indicates a company and/or person’s likelihood to be a good target buyer. Use cases include propensity to buy, inbound lead conversion, higher LTV, upsell/cross-sell, etc.
  2. Intent Scoring
    A Leadspace weekly set of customized signals that monitors your accounts for intent (3rd-party and/or 1st-party), and applies scoring based on the level of intent activity specific to a customers’ products/category.
  3. Buyer Persona Fit
    A Leadspace AI-Model based either on standard personas, or your custom persona profiles, to 1) score the existing database and inbound leads based on their closest persona fit (including skills and other identifiers), and 2) find net-new contacts within accounts that lack the right buyers using persona targeting.
  4. Buyer Engagement Scoring
    Your external 1st-party scoring model that is built through either simple point scoring for engagement in a marketing program or interaction with website content. It is typically implemented within the Marketing Automation Platform (Marketo, Eloqua, Pardot, Hubspot, etc.) and the higher the score the more engaged the buyer.

Buyer Intent is critical, but is it enough to close business?

Buyer intent signals are typically the first signal that B2B companies consider to identify interested buyers, and are critical to Account Based Marketing (ABM). Intent is a third-party signal that identifies the domains that are actively searching for a given topic. This service can be bought from many different providers and is available openly on the market to anyone, meaning every one of your competitors can access that same signal. Intent is most useful in identifying accounts that are “ready” to buy – assuming you’ve properly identified the right terms for your products and that you’ve kept them up to date. It’s a great signal to narrow your TAM to a set of companies. But are the companies who are surging the right accounts for your company? 

The best way to answer this question is through a company Fit or propensity model that identifies which of these intent-surging accounts your company is most likely to close. Company Fit or propensity models compare your historical data and ICP against all the profiles within your TAM then use AI/ML Analytics to algorithmically score them by a variety of buying signals to determine their propensity to buy your product right now – as well as indicating whether or not they’re likely to buy in the future. Fit models are critical to prioritizing which account opportunities to spend expensive campaign dollars and sales resources on. Accounts and opportunities that have both high Fit and high Intent scores are the best candidates for high investment.  

A Persona Fit Model Can Identify Buyer Propensity

The next step is figuring out who are the right people in each of these accounts. A Persona Fit model is the best way to categorize buyer propensity. A persona model is built to look at the title, level, skills and expertise of a lead or contact and determine if they are a good fit to your ideal buyer profile, or Ideal Customer Profile (ICP). Many companies have products that span different buyer personas. When a lead comes in, it’s useful to have a scoring model that assesses which persona that person best matches. This is important because there are often ambiguous job titles or levels across global organizations. By categorizing your database and incoming leads into personas, you can identify which product they may be most interested in and where they might be effective in the buying process.

Finally is that right person in that right-and-ready account actually ready to buy? The best way to look at this is to understand what engagement a specific contact has had with your sales and marketing programs. Engagement scores from Marketing Automation Systems, like Marketo, Pardot and Eloqua, are the best way to look at engagement of individuals in the prospect account as proxy to their readiness for a next step in your demand generation tactics. By looking at both the Leadspace Persona score and your 3rd-party engagement score of an individual, you can identify the right people within your accounts and the readiness of an incoming lead to engage.  
In order to narrow TAM to the best possible opportunities, B2B leaders are activating each of these 4 models in order. With Revenue Radar you can see your best targets and ensure you’re achieving the highest possible close rates and the lowest possible costs to optimize your sales & marketing ROI. You can fuel and optimize your demand funnel with the best B2B buyer profiles enhanced by company Fit, Persona and Intent models along with your own engagement scoring for true TAM-to-opportunity prioritization. Stay tuned for our next blog in the Revenue Radar series where we will dive deeper into the first step to optimizing your Revenue Radar – using a Fit / Propensity scoring model to improve the odds of closeable revenue by honing in on the right targets at the right time! In the meantime, get the full Revenue Radar Guide to see how Leadspace finds your best customers.

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Leadspace predictive artificial intelligence sales marketing

The best way to answer this question is through a company Fit or propensity model that identifies which of these intent-surging accounts your company is most likely to close. Company Fit or propensity models compare your historical data and ICP against all the profiles within your TAM then use AI/ML-Analytics to algorithmically score them by a variety of buying signals to determine their propensity to buy your product right now – as well as indicating whether or not they’re likely to buy in the future.

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leadspace sales territory plan

Aligning your teams to ensure sales quotas are met has never been easy, and the current environment has only made it harder. If your sales goals are not being met, it may be time to optimize your sales territory management strategy by utilizing all available resources effectively to boost sales productivity.

What Is Sales Territory Management?

Sales territory management is the process of creating and identifying your sales territories, assigning those territories to your reps, and then continually optimizing those territories to keep your sales reps productive. When sales territories are well managed, your sales reps are busy and effective with a clear view into their most important sales targets. A sales territory is not a one and done exercise. It’s an ongoing process that needs nurturing. Ideally, you have a sales territory plan to ensure your sales and demand-generation teams are targeting the most valuable prospects, and to identify (and pursue) the steps necessary for your company to achieve growth, efficiency and revenue.

What Is Sales Territory Planning?

Sales territory planning is the process of organizing accounts’ and prospects’ engagement responsibilities, and then dividing them into sales and demand generation resources, in a way that helps maximize sales productivity (pipeline creation), and drive sales growth (winning accounts) over time.

There are many factors to consider when building sales territories. These could include your team’s historical data (previous revenue and bookings), firmographic attributes (company size, location, and industry), or the account executive or seller characteristics (tenure, recent performance, and product line or industry experience). 

Importance of Sales Territory Planning

Sales territory planning is one of the most important components of an organization’s go-to-market strategy. Frequently, sales leaders overlook territory planning as a catalyst for growth. Planning takes time, but right before the start of a new fiscal year or quarter, there’s often not enough time. It is also frequent to find organizations applying cookie-cutter or equal spread logic to the process of carving out territories in hopes of being fair or democratic to all the sales reps. However, these practices result in poor alignment with strategic goals, poor motivation among the different sales teams, and poor market performance in general.

Key Steps to Optimizing Territory Plans

There are five key steps to creating and optimizing your territories, while making them directly actionable:

1. Understand your Ideal Customer Profile (ICP) – at both the account and person-level.

2. Create your Total Addressable Market (TAM) by territory.

3. Map the terrain of each territory – understand your existing customers and whitespace.

4. Identify the right and ready accounts.

5. Identify the right and ready contacts within those accounts.

The novel concept here is the opportunity to accelerate revenue generation through the optimization of sales territories, and go-to-market operations with data-driven AI-targeting and lead prioritization. This would start with a prioritized view of worldwide opportunities in the context of the way you organize your business, your go-to-market, and your sales territories today. These would then be further optimized by adjusting criteria, individual accounts or regions as needed to effectively delegate your available resources to them accordingly.

Best Practices for Optimizing Sales Territory Plans

Let’s break down the best practices for each of the five steps for sales territory planning mentioned above:

1. Understand your Ideal Customer Profile (ICP) at the account and person-level.

A good practice would be to configure your Ideal Customer Profile (ICP) via firmographic data for accounts and demographic data for contacts. A best practice would be to create it with AI-models for sales territory accounts and contacts.

Pro tip: Base your ICP on the kind of accounts and contacts that they, or their competitors, have closed.

2. Create your Total Addressable Market (TAM) by territory.

A good practice would be to gather account universes from third-party data tools. A best practice would be to use a Customer Data Platform (CDP) that leverages your internal first-party data with third-party data to fully understand your TAM and fuel your AI scoring model. 

Pro tip: Conduct this analysis for every sales territory so that you understand if a state or zip code has the right resources and manage changes accordingly.

3. Map the terrain of each territory – understand your existing customers and whitespace.

A good practice would be to understand and collect the details on all the companies you do business with in each territory – this includes firmographics and technographics that will help understand the differences and segments within each territory. A best practice would be to map your overall whitespace and “lookalike companies” against that territory, adjacent industries, vertical integrations present, etc. – any opportunistic characteristic could be mapped! 

Pro tip: Use those to understand renewals, upsell and new logo strategies, and to determine resource allocation amongst teams.

4. Identify the right and ready accounts.

A good practice would be to leverage acquired intent signals from multiple vendors against your ICP for each of the accounts you target or monitor (in Salesforce for example). A best practice would be to integrate AI propensity models and intent signals directly into the CRM to prioritize accounts by fit and readiness (in order of likelihood to buy).

Pro tip: Conduct this analysis for every territory with weekly updates – intent signals and topics change every week so frequency is important.

5. Identify the right and ready contacts within those accounts.

A good practice would be to organize the accounts within each territory by their Marketo, Pardot, Eloqua, or Hubspot scores. A best practice would be to integrate persona scoring in conjunction with engagement scoring at the account level to identify the best (most ready-to-buy) contacts/prospects to pursue. 

Pro tip: Use this strategy to focus their efforts and lower regional sales and marketing list-buys and event costs.

CDPs Can Make Managing Sales Territories A More Seamless Experience

Independent of the strategy used to build your territory hierarchies, it’s important to plan territories in a transparent and equitable way for all sales reps. Historically, achieving that equity has been a game of chance when assigning territories by hand. Now sales leaders can apply predictive AI-models across their TAM, enabling them to make territory assignments knowing their decisions are backed by data. By implementing a Customer Data Platform, you can achieve data-driven territory management with automatic updates throughout the year. Your territories being equally assigned is important, but what if all of your sales reps could also view the accounts within their territory in order of their likelihood to buy? With equitable territories, all of your sellers hit their quotas – but with the right territories, they’ll exceed those quotas. For more information, watch our webinar, Best Practices for Improving Sales Territory Management, or check out the blog series, Moneyball for Sales Territory Management.

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Leadspace B2B Customer Data platform

People and companies change every day. Companies make acquisitions, people change jobs, and intentions are dynamic. This means your data changes every day – but is your database up to date? Is the data you use to drive your business as accurate as the day you procured it? Data Decay is an issue that every company must face at some point. Email marketing databases, for example, naturally degrade by approximately 23% every year according to Hubspot. 

Data decay has especially accelerated during and after the pandemic. This is agreed on by 79% of Customer Relationship Management (CRM) users according to The State of CRM Data Health in 2022 published by Validity. As the business environment restructures itself in the post-pandemic era, a new symptom is quickly spreading among companies: millions of workers are still quitting their jobs in 2022. The “Great Resignation” is affecting even the most solid data-driven strategies for B2B marketers. 

High-quality data is the fuel that makes the sales funnel engines spin. According to the Global Data Management Report, which considered responses from 700 data-centric business leaders around the globe, 84% of B2B companies saw increasing demand for data-driven insights within their organizations during the COVID-19 pandemic. The effects derived from the “Great Reshuffle” or “Big Quit” have accelerated this decay to levels not fully understood. 

While the degree of decay is not yet fully understood, our response to it can greatly mitigate the effects the decay will have on our organizations’ successful use of data to drive decisions. Here are 6 ways you can address data decay to ensure your data is accurate, up-to-date and, most of all, insightful:

1. Establish alignment between sales and marketing from a single source of truth

Ensure your sales and marketing teams agree on what “good” looks like for your data ecosystem and develop a rubric to foster such alignment. Without clear expectations across teams, the rate of data decay amplifies as already degraded data is being input through disparate processes without governance or attention to detail. Until this is done, SDRs, AEs, and Marketing Ops will all be focused only on the data points relevant to them without thinking about how it impacts other parts of the organization. Your Marketing and Sales teams should also align on which third-party data provider(s) to use as their external trusted source. This positively affects organizational change management through establishing a consistent set of semantics, a single data landscape/shared data values, and shared series of rubrics and lexicon to define data success and quality. 

Map out your sales and marketing processes, motions and channels > Determine which data is necessary to enable those processes effectively > Develop a rubric to measure data quality in key domains and define what “good” looks like.

2. Maintain your CRM ecosystem

Customer Relationship Management (CRM) tools are the best source of relevant information for building up and maintaining your dataset for your sales teams. They harbor all the interactions made with every lead, prospect, and client you ever contacted. A well maintained CRM ecosystem enables your sales and marketing teams to efficiently monitor and analyze the sales pipeline and easily organize automated email send-outs. Best practices include:

  • Determine how often you need to audit your data – quarterly is often a best practice. 
  • Remove unsubscribed or bounced records. Remove or merge any duplicate records you find. 
  • Once or twice a year, look at unengaged prospects and remove  or create re-engagement campaigns. Regular maintenance will give you better sales and marketing outcomes. 
  • Integrate your CRM system with other data solutions, making sure both sales and marketing use one system to collect data and align all the data sources into one space to collect as much input as possible.  
  • Conduct CRM data checks. Validate data as it’s captured in your CRM and use firewalls to ensure its accuracy.
  • Segment the critical data into categories. If you have multiple ideal customer profiles (ICPs), work for several markets, or sell a variety of products, segregating the data by categories can help you optimize the selling cycle and maintain data quality more efficiently. 

3. Use email verification tools to validate emails

While email is the top prospecting channel with 87% of B2B marketers using it for outreach, email marketing databases naturally degrade year by year which leads to deflated metrics, increased bounce rates and ultimately lower conversion rates. High bounce rates are a key indicator of your database health where it highlights people who have moved on from the organization, not obtained opt-in, or unsubscribed from your marketing efforts. Again, regular maintenance will also help keep your email campaigns performing at industry benchmarks or above.

One of the most effective ways to overcome this challenge and clean up your list of invalid and inactive email addresses is to use email verification tools. You can leverage the Leadspace Verification Status and other LS indications to support you in identifying emails that are no longer valid.

4. Have an opt-in strategy

Opt-ins have become increasingly important in the digitally-led sales and marketing space as brands are providing users the agency to decide whether or not they want to be contacted. Essentially, it is used to ensure that your subscribers have completed registration by providing an accurate, correct email address that is less likely to bounce. Once the user provides their information, there is also a record from a privacy/ethical standpoint that the subscriber requested to be contacted by marketing and sales teams. With new tactics you can improve your lead-generation by improving opt-in rates. Opt-in is also a very effective way to disqualify traffic that may not be of value and reduce the number of unqualified visitors to your properties based on disinterest in your messaging and communications.

5. Develop a data hygiene strategy

Adding regular data hygiene checks as a part of a routine is the most obvious way to keep your database fresh and updated. This process generally includes going through a quarterly refresh of your CRM and Marketing Automation Platforms to periodically perform checks and balances. Generally this includes checking existing data for updates, deleting contact duplicates, verifying new contacts, and filling in the missing information. With the right Customer Data Platform, this process can be done automatically with ongoing enrichment and refreshment, then subsequently filtering in both CRM and Marketing Automation. Collaboration within the teams contributes to fighting data decay significantly and helps build up a data-driven culture within the company. For more information, check out Database Refresh Best Practices.

6. Leverage the Leadspace CDP to enrich, unify and deduplicate your data

In conjunction with the recommendations above, you can utilize Leadspace to support the maintenance of your data quality. Leadspace can unify multiple siloed CRM and Marketing Automation instances into a single dataset, removing the guesswork that may be necessary to do this manually – effectively consolidating and tackling multiple complex processes with a simple point-and-click function. Unification allows you to verify, dedupe, enrich and cleanse CRM & Marketing Automation data with firmographics, and demographic data, and even leverage AI intent and propensity models for true data-driven insights. Additionally, you can upload those records directly into your CRM or Marketing Automation tool to ensure consistency in data across your entire MarTech landscape.

Accurate, complete data is critical to making successful data-driven decisions. Yes, your data will decay over time, but how you deal with its decay will determine its value to your company’s overall success in our increasingly data-centric economy. For a more in-depth look at best practices for combating the inevitable decay of your data – and to learn how to automate these complex processes with Leadspace’s industry-leading Customer Data Platform (CDP) solution – check out the Leadspace article, How to Fight Data Decay. And if you’d like to learn more about the Leadspace CDP,  take a look at the Leadspace product sheet to see how Leadspace’s CDP solution can modernize and optimize your B2B marketing approach.

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b2b Leadspace customer data platform

Pulse and Salesforce surveyed 500 IT leaders across the globe to find out 7 trends and the best practices that are reshaping IT. How are they navigating the data security complexities and pushing forward on their digital transformation goals? What are their winning strategies for retaining talent and resources? And how do they drive innovation across their verticals?

Let’s consider 3 of the top IT challenges and next steps they reported for 2022:

  • Securing IT and customer privacy.
  • Accelerating digital transformation.
  • Integrating data.

This Salesforce study finds security and privacy have replaced transformation as the top priority of IT managers. Additionally, 34% plan to consolidate their tech stack into fewer vendors and 72% aim to use no-code/low technology to enable digital transformation across their business – including 50% of marketing departments. Unsurprisingly, AI and automation transformation investments are a priority for 63% of these IT teams. 

At Leadspace we see this kind of advanced thinking every day amongst our customers. In terms of advanced profiling, 3 of the top 5 most recognized software vendors in the world have seen data procurement, processing and hygiene cost reductions of 30-70% on an annual basis after implementing Leadspace. 

For insights and behavioral-based targeting, the top electrical component manufacturer in the world saw a 36% reduction in cost per lead (CPL), and a 57% increase in click-through rates (CTR), as measured by an independent third-party on an annual basis after implementing Leadspace’s AI-driven propensity and predictive analytics models.

By leveraging our embedded third-party data you can achieve accurate and actionable data throughout your stack and cut your overall data procurement costs in half through the consolidation of data vendors. Our profiling and scoring engine curates the top 30+ B2B data sources and organizes 70+ million companies, 240+ million buying centers and 280+ million people into hierarchical profiles so you don’t have to. Our enrichment approach optimizes records and makes them actionable within the components of your existing marketing technology stack. This replaces your regular external firmographics augmentation and intent signal purchases with more accurate scoring – again, at half the cost.

Leadspace customers leverage tens of thousands of real-time buying signals and AI Analytics to build and operationalize their companies’ Total Addressable Market (TAM) and Ideal Customer Profile (ICP) amplified by fit, intent and engagement scoring models. They’re able to do all of this from a single source of truth, within an integrable point-and-click platform, optimized to save on licenses, high skill resources, and custom implementations – all while quickly reducing their data integration technical debt.

Sales and marketing teams use the Leadspace platform to give them a competitive edge in determining who their best customers are, understanding their whitespace opportunities, and in turn, dramatically improving the effectiveness of their sales and marketing programs.

Are you interested in knowing how Microsoft, Salesforce, Oracle, IBM, CISCO or Zoom amongst other industry leaders are doing this? Get started with Leadspace

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Leadspace customer data platform

In the last few blogs I discussed how to discover your TAM and assign territories, then how to determine where to focus your sales and marketing efforts by using AI/ML models to score leads and understand who is ready, eager and able to buy your product. We also discussed how to turn the profiles within your TAM into actionable segments to drive your business. Now let’s look at what is needed to make the right decisions with all this great data to accelerate business outcomes and maximize ROI. 

An average B2B database has well over 50,000 individuals. All of those profiles should be enriched with marketing campaign engagement data, intent, scoring and buying signals etc. It should be updated in real-time as source data changes and new data is ingested – along with using that data to make sound business decisions. That’s a lot to ask from a traditional salesforce or marketing automation platform. While marketing automation platforms are valuable for taking immediate action when working within a single channel or a small data set, that is not enough in today’s fast paced and dynamic markets. We need to take it to the next level with a platform that envelopes multiple channels, firmographics, scoring models, and other real-time consumer behaviors to include and analyze those insights that marketing automation platforms neglect. This is where a decisioning platform, like a customer data platform (CDP), can help.

Why Is a Customer Data Platform Built for ABM?

With a Customer Data Platform, companies can automate insights, propensity scores, and other data-driven decision-making processes, putting the power of your data directly into the hands of those who need it most – your sales and marketing teams. Using a CDP, you can create complete buyer profiles and leverage propensity models to quickly seek out closeable leads and truly focus your efforts with an account-based marketing approach.

Let’s explore the five steps to optimizing your sales territories for an account-based marketing approach. Let’s look at the best practices for each of the five steps and how a CDP’s decisioning capabilities can facilitate and automate these best practices along the way.

Best Practices for Optimizing Sales Territories or Account Assignments or ABM Picks:

Step 1: Understand your ICP (at the account and person level), and configure it via firmographic data for accounts and demographic data for contacts – create it with AI modeling and lookalikes for accounts and contacts. This should be based on the kind of accounts and contacts that you or your competitors have closed.

With a CDP, you can achieve accurate and complete multi-sourced AI profiles by leveraging embedded third-party company and people data sources. You can automatically unify your variably-sourced first-party data with embedded third-party data at scale with business-focused tools for the industry’s only TAM to opportunity profiling and activation. This sets the stage for a single source of truth as you seamlessly blend siloed customer data into a single source of truth at multiple levels—profiles for people, accounts, and buying centers – with the industry’s best lead-to-account matching.

Step 2: Create your TAM by territory and gather the account universe from third-party data tools. Use a customer data platform that uses third-party and your internal data to understand the TAM universe against your AI model. Do this for every territory so that you understand if a state or zip code has the right resources.

With a CDP solution you can turbocharge your growth strategy with Fit and Intent models to identify the highest-returning market segments and optimize territory assignments to maximize sales effectiveness. Put the right account contact details, buying signals and propensity-to-buy scores directly in front of your reps to prioritize leads and opportunities in their pipeline.

Step 3: Map terrain of each territory or campaign outreach with customers and whitespace. List the number of companies you do business with in each territory and look at your overall whitespace and lookalike companies against that territory. Use this to understand renewals, upsell and new logo strategies and to determine resource allocation.

With a CDP, you can master your whitespace by leveraging TAM and ideal customer profile to understand your most attractive whitespace then laser-focus campaign segment members by targeting your best lookalike accounts and personas. 

Step 4: Identifying the right and ready ABM accounts for each territory or campaign. Match your weekly intent vendor signals to the ICP criteria for each of your salesforce accounts and integrate account AI models and intent signals directly into Salesforce to prioritize accounts by fit and readiness. Update this weekly as intent signals and topics change every week.

A powerful CDP solution enables you to enhance your active profiles with Fit and Intent models to automate insights – enabling your marketing team to create complete buyer profiles. Leverage decisioning models to quickly seek out and achieve closeable business and take the guesswork out of identifying the top strategic accounts for investment by focusing on those who are most likely to buy your product. 

Step 5: Identifying the right and ready contacts for each territory or campaign. Match your Marketo, Pardot or Hubspot scores against your Salesforce prospects and accounts, then use persona scoring with engagement scoring to identify the best engaged contacts/prospects to go after. Use this to lower regional sales and marketing list buys.

With a CDP you can add the personal touch to your unified profiles, creating the best buyer profiles from engagement to personas as you automatically leverage profiles with personal demographics and buying behaviors from social signals and interests to statistically assign personas instead of nondescript job titles. This further enables you to optimize your marketing and sales engagement by leveraging AI predictive models to accurately prioritize the top 25% of leads that deliver 60-80% of your business. You would also be able to create and activate precision segments with engagement and other account or buyer personalization to optimize conversion rates.

In summary, by utilizing a Customer Data Platform (CDP) you can leverage AI, ML and custom modeling to prioritize ABM account selection and garner actionable insights surrounding data-driven next-best actions. Unification, active profiles, intent, persona, look-alike, and engagement scoring processes can all be automated and updated in real-time with a powerful CDP solution. Additionally, the AI/ML-driven scoring models can be used to drive/trigger automated workflows & processes across sales, marketing, & customer success use cases. This includes but isn’t limited to:

  • Enabling inbound leads to be directly routed to sales or specific nurturing streams based on propensity-to-buy attributes.
  • Utilizing AI-driven intent scoring to enable customer success to proactively improve customer retention and prevent churn.
  • Improving BDR outbound and digital marketing conversion rates by utilizing AI/ML-driven propensity-to-buy and intent scoring models to more intelligently curate ABM audiences and improve ad bidding strategies.
  • Recommending content stream by persona, or product best offering typically in cross/up-sell scenarios.

Empower your marketing, sales and BDRs to reach the right people within the right accounts, at the right time, with the right content – faster than your competitors. For more information, check out our product sheet Optimizing Sales Territories or watch our webinar, Best Practices for Improving Sales Territory Management, and take the first step towards hopping on the fastest road to revenue.

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Leadspace B2B customer data platform, sales territory

In the last two blogs I discussed how to discover your TAM and assign territories, and then how to determine where to focus your sales & marketing efforts by using AI/ML models to score leads and understand who is ready and able to buy your product. Now let’s look at how to turn the profiles within your TAM into actionable segments to drive your business. 

What makes a segment more “closeable”? We believe that the best segment strategy starts with two things – coverage and context. Do your segments start with your Total Addressable Market and cover its entirety? And are each of the TAM segments then divided into buyer profiles from your Ideal Buyer Profile? If done well, you’ll have precise segments that you can further hone-in on over time with activation, engagement and other account or buyer personalization like products owned, industry or use case context. Finally it’s important to understand which sub segments perform the best and may show intent or better conversion. Once you have effective segments, it’s time to activate, score, route and alert.

In the times of direct marketing, we were told that the list is one of the most important ingredients of a successful campaign. The truth is that creating a segment or list for a campaign is pretty much driven almost entirely by a campaign manager’s gut instinct. In recent years we’ve added intent to the process, but we’ve seen that it typically only adds a couple of percentage points in upping the odds for creating closeable demand. The most important buying signals come from understanding the types of buyers your team has successfully sold to, how to recognize them and classify incoming leads into A, B, C or D categories.  

Once the classification of incoming demand is done, making campaign activation quick and easy is a big part of streamlining your customer journey and improving your customer experience. By implementing a Customer Data Platform (CDP), companies can enable their sales & marketing teams to create smart segments and automatically activate them across numerous channels – all in the same tool. Furthermore, with a CDP you can receive automatic alerts about campaign performances that enable you to further direct the right campaigns towards the right people within the right accounts at the right time. 

Some features of a leading activation and alerting framework include:

  • Users can build account segments based on firmographics (geo, industry, size, revenue, etc.), look-alike modeling, predictive fit/intent scores, and install base technologies together with first-party attributes – at all hierarchy levels.
  • Users can expand account segments with priority contacts using targeted personas and any contact attribute. Once created, segments can be updated via workflows.
  • Data-agnostic segmentation is possible, creating a unified database of first-party data combined with embedded data and other third-party data sets clients have already licensed.
  • The ability to ingest first-party data and enable it as criteria for granular, focused targeting and segmentation purposes, e.g., to target accounts that score high for intent and predictive fit that exist within a specific territory/geography. 
  • Users can directly activate these segments/audiences in CRM, MAP, sales enablement, and digital ads via direct integrations or platforms like LiveRamp.

To summarize, implementing the right CDP solution can facilitate and improve your sales and marketing campaigns through seamless activation and alerts processes. Figure out your available market, see who is ready and able to buy, then activate campaign segments that are increasingly customized for specific target accounts. Accelerate your sales and marketing campaigns and improve their success rates by implementing the right CDP solution with activation and alert functions. Better segments. Better activation. Better decisions.

To learn more about optimizing campaigns based on territory assignments, watch this webinar on Best Practices for Improving Sales Territory Management , and get on the fastest road to revenue.

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Leadspace b2b customer data platform

It’s always that time of year. In sales and marketing we’re always starting a new quarter, ending a new quarter, trying to create demand for the following year or just planning. We always want to start strong and grow better. Assuming you start strong – how do you grow better? How do you outsmart the competition? How do you create sales and marketing systems that help identify the leads that matter? We need to create a system to identify right accounts and the right leads so that sales teams can understand which deals that they can count on for revenue going forward.

In last week’s blog we went over how to build your total addressable market (TAM) and align it with your global territories. Now let’s explore how to understand when the right accounts – and when the people in those accounts – are ready to buy. Which accounts are exhibiting buying signals? Which people within those accounts are exhibiting buying signals that indicate readiness to buy?

Now that you’ve figured out your TAM and estimated the size of a particular market, what kind of guardrails should you use for putting together go-to-market (GTM) strategies? Without guardrails, a company might chase after every potential lead or opportunity and waste time and money pursuing dead ends.

First, you need to determine the right company. This involves using your historical first-party data to develop an Ideal Customer Profile (ICP), then comparing it throughout your total addressable market by either basic firmographics, demographics, and tech install or an AI model. How closely each company matches, or fits, your product will depend on the conversion lift associated with numerous buying signals to produce an overall Fit Score. This is the first stage of honing in on your best targets.

Next, you need to determine which of the companies who fit your product are actually ready to buy. This means determining their intent at the product level – ensuring it’s the right time for the right company. We can determine intent by a company’s search activities. Most of you are buying weekly intent feeds delivering the names of companies who are searching for the terms that you prioritized – and they are typically categorized by low, medium or high intent.  By merging this data with the account propensity or fit scores you can get a view of how a company is not only the right company but a ready company.  Knowing that a company’s employees have been actively searching in your field of expertise with either new high intent or sustained intent enables you to focus your efforts on the fitting companies that are truly ready to buy.

Next, you need to figure out who are the right people to pursue within those companies. This involves scoring people by their persona. Does their role at the company line up with the personas of your historical successes? Is their persona typically responsible for making decisions to buy your type of product or service? Who makes purchasing decisions at the company? Or who might see the value in your product and bring it up the chain of command quickly and effectively? With a Persona Fit score, you can narrow in on the department, level, right job title, role or expertise to go after identifying the right person in that company who is most likely the right contact for your type of product.

Finally, you need to figure out if those personas, or specific people at the company are also ready to buy. This means scoring their engagement at the person level, using your marketing automation platform – Marketo, Eloqua or Pardot for example. Has that individual been on your website? Who specifically has been searching for your type of content? Who specifically has engaged with your previous marketing efforts? With this final piece of information, you can focus on the right people from the right companies who are ready, able, and eager to buy your type of product or service – ensuring you don’t waste time, money and effort chasing down leads that aren’t likely to close.

Account targeting is easy once you have models. Have you created a model, or is it at least in your head? What are the buying signals that matter? How many lookalikes in the world? Which territory is your next best place? Better sales account targets are all about “lift” – propensity, persona and intent. How does industry, persona, tech install, engagement or company specialty fit in? Are you doing territory and ABM investment tiering based on GTM science?

With account targeting, you can prioritize account assignments by tiering accounts into categories by closeable odds (the right accounts) and accounts that are showing intent and engagement (the ready accounts). With the right CDP solution, you can utilize AI predictive models to filter your TAM further by technographics, FIT, Persona, and intent. This enables you to plan territories and target accounts beyond just job titles, locations, and company size, as it algorithmically determines who is likely to close and where to focus your sales and marketing efforts. In short, utilizing a CDP solution enables you to bring predictive models into your TAM to automatically determine account readiness, giving you the information you need to achieve closeable business as you target the right people, in the right company, with the right outreach, at the right time.

For more information on optimizing territory assignments for closeable business, watch this webinar on Best Practices for Improving Sales Territory Management , and get on the fastest road to revenue.. 

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leadspace b2b customer data platform

It’s Q4 for most of us, and this is the time of year when revenue operations professionals spend time understanding and redefining sales territories. Sizing territories alone is next to impossible to do by hand, and the tedious process of narrowing down all of the possibilities is expensive, time consuming, and error prone. Even after you’ve discovered your Total Addressable Market (TAM) in every territory and honed in on select targets, you still have to ensure that your sales and marketing efforts are aligned in pursuit of the targets you’ve selected. While there are a variety of prospect/customer segmentation tools available to ease the process, there remains a major obstacle in optimizing the right segments and efforts – getting territories right.

How do we assign sales reps their territories, fairly and effectively? Do we base it on the number of accounts in each region? The number of accounts we think we can close in each region? How do we ensure that one rep doesn’t own all the fruitful accounts while others get stuck with stagnant accounts? We want to assign territories that are productive, but also not overwhelming – avoiding instances where teams try to manage so many accounts that should close, but fail to close (or take too long to close), due to a lack of the prioritized sales efforts necessary to effectively close the right deals in a timely manner. 

Traditionally, determining territories is done with tedious math on spreadsheets, using a lot of guesswork and assumptions about which accounts might be in a certain region and how much they might be worth, but it doesn’t even amount to a gamble when you don’t truly know your TAM from the start. Let’s look at 5 steps to what we call Moneyball for Sales Territory Management:

Step 1: Map your Global TAM and understand the total number of companies fully available to your product opportunity.

Step 2: Define your Ideal Customer Profile (ICP) and search globally against companies meeting that criteria.

Step 3: Define your whitespace by merging your current Customers into your ICP file – always including prospect or customer type – in order to understand how much of TAM is penetrated and for context of best customer lifetime value.

Step 4: Size your Account TAM by either using a simple approach like your average deal size, a more advanced progression of land/expand and lifetime revenue, or by using analyst projections on the size and spend projections.

Step 5: Test your current territory model by applying your current territory boundaries/definitions to the overall file and look at the results, adjusting staffing, quotas and boundaries as needed. If starting from scratch or looking for a next territory, analyze whether the traditional geographical, industry concentration, company size concentration or a combination makes the most sense for delineating equitable territories.

Bonus Step: Prioritize account assignments by tiering accounts into categories by closeable odds (the right accounts) and accounts that are showing intent and engagement (the ready accounts).

Again, if you’ve tried this before you know that doing each of these steps by hand is incredibly tedious and can take weeks or months of work. As you might suspect, there are tools available to facilitate the process and automate the majority of the tasks. 

Perhaps the most difficult step to do by hand is the first step – discovering and mapping your global TAM. The most practical tool available for TAM discovery would be a Customer Data Platform (CDP) with embedded third-party data sources. Using a CDP’s embedded data opens you to a world of profiles for people, companies, and accounts that you can leverage from the start, then leverage against your ICP and hone in by demographics, firmographics, tech install, and more. Additionally, with the right CDP solution, you can utilize AI predictive models to filter your TAM further by technographics, FIT, and intent. This enables you to plan territories beyond the numbers, locations, and company size of accounts by algorithmically determining who is likely to close and where to focus your marketing and sales efforts – making the process of assigning territories fairly and effectively much, much easier.

In short, assigning territories is no easy task, but with the right tools, you can do it faster and better than ever with the assurance that your decisions are backed up by data. Our next blog will dive into the first two steps of moneyball territories – your TAM and ICP. 


To learn more about how Leadspace buyer data platform can guide your territory assignments – watch this webinar on Best Practices for Improving Sales Territory Management , and get on the fastest road to revenue.

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Leadspace customer data platform, Enterprise profiling

Choosing ABM accounts based on gut or table stakes profiling – like firmographics – isn’t enough anymore. Successful ABM comes down to discovering your TAM, building profiles at the person, account, and buying center levels, and then comparing them against your ICP in order to focus your time and effort on the accounts most likely to close. Doing this effectively relies on accurate, up-to-date profiles. This makes the development of active buyer profiles leveraged with propensity fit scoring models the key investment in pursuit of closeable business.

In a recent webinar, Ranjit Rao explores how Active Profiling enables marketing teams to double the success rates of their legacy ABM strategies. He explains how every company today is focused on leveraging an account-based marketing strategy with the intent to ensure they’re focusing their efforts on the best set of accounts, so they can optimize marketing and sales efforts ROI and drive revenue. Unfortunately, many companies struggle to build out a cohesive ABM strategy because they lack some of the core pieces of technology necessary to help enable it. 

What is Account-Based Marketing?

Gartner defines ABM as, “a strategy in which a supplier targets a select group of accounts that represent significantly higher expansion or growth opportunities with tailored marketing and sales support.”

How do we ensure we’re setting the table for that ABM strategy with the right foundation to give you the best possible performance? The first step in the process – the account selection – can make or break your ABM strategy before it starts.

The 1st and simplest approach to account selection that companies will take is sales intuition, which is generally a good way to identify some of your very top accounts. While this method may be a good starting point, it’s a very limited approach because you’re not taking into consideration the most critical factor – which is data. Sales intuition ignores any insights about your current customers (the ones that spend the most $, buy certain products, etc.) that can help you more intelligently build your ABM list and focus on the right account profiles. Without considering that, you’re effectively shooting in the dark. 

Many companies also leverage firmographic data at the company level, having some understanding of the revenue, size, geographic parameters that make up their ideal customers, which is a good way to systematically target different segments. This is a step in the right direction, but it can be hard to trust the actual data. How many times do we have to ask ourselves when was this account record in our database actually last updated, or who updated it, was it manually edited by someone or did this come from a third party data source, and which one of our third party sources?

More advanced utilization of data involves intent signals – because intent is a great way to time your engagement with companies that are expressing in-market buying signals in your products and solutions. However, it’s really easy to over-index on intent and just end up with more noise than results, because not every company that expresses interest is the right company for your business. Over-relying on intent often leads to sales reps chasing potentially bad accounts and deals, or spending excessive money on ads that are targeting companies that just aren’t the right fit. Intent tends to work best in conjunction with the accounts that you already know are the best fit for you (the ones that look like customers who have bought specific products, have led to your biggest deals, or have generated the most LTV (lifetime value), etc).

Now we need to go beyond just over-relying on subjective sales insights, static firmographic data, or potentially noisy intent data by implementing Propensity Fit Modeling. Ultimately, when you analyze your own business you quickly realize that your opportunity conversion funnel follows the 80/20 rule – where about 20% of your prospect accounts leads to 80% of your wins and revenue – so it’s absolutely critical to be able to determine who your A and B accounts are from day 1 so that you’re not wasting time and money chasing bad accounts. To nail down the right strategy here, you need to ensure your models are leveraging AI and machine learning, and utilizing thousands of critical signals to uncover the accounts that look like your best customers. Now if you’ve nailed down this part, you’re going to be a lot more confident about which accounts are the best ones to go after. But equally important is being able to pinpoint where they actually are.

This leads us to our next solution – which is establishing where those buying centers are. We all know how complex it can be to navigate the underlying hierarchies of large enterprises – trying to penetrate accounts with layers of subsidiary and parent-child relationships, with offices distributed across different geographies, that poses a really complex challenge. In order for your ABM efforts to succeed here,  you’ll need a data asset that can provide true visibility into identifying both where the key buying centers are located, how they’re connected, and where the true decision makers and buying committees actually exist – being able to navigate profiles throughout hierarchies at person, account, and buying center levels.

Instead of just relying on a potentially fuzzy/generic signal like job title, persona fit enables you to identify and target your key buyers using a much wider breadth of insights such as title, level, dept, job functions, technology expertise. Additionally, applying exclusion logic and adding weights to individual signals enables you to both prioritize the right buyers, as well as ensure you’re reaching out to them with the right message that’s tailored to them.

Still, you need to ensure that these new personas you are adding across your target accounts retain their value by ensuring they are accurate and up-to-date. That calls for an active profile management solution that will not only ensure you’re keeping up with M&A activity across your target accounts, but also key activities like promotions and job movements as quick changes can potentially make your buying committees stale and no longer relevant. Gartner reports that B2B data decays at a rate of 70% per year – and our benchmarks show that moved individuals convert 2-3x faster than your average lead or contact – and your former customers who move convert even faster at 4-5x the average rate. So it’s imperative that your solution can both prevent the data on your ABM buying committees from decaying and take advantage of the higher conversion rates you can achieve through reactivating those who have moved to new firms.

Watch Ranjit Rao’s full webinar to explore a comprehensive approach to doubling ABM performance with Active Profiling. Learn how to create the profiling models to segment, score, and prioritize entire buying groups—down to the individual buyer, arming you with the information necessary to target the right people, with the right campaigns, at the right time—before your competitors do!

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leadspace b2b customer data platform

B2B Marketing and Sales leaders are constantly looking for the next innovative method to give them a competitive edge—particularly in driving revenue for their business. If you work in sales or marketing, there probably hasn’t been a day where you have not heard about implementing or operationalizing intent data to target and engage your prospects effectively.

But what is Intent data?

According to SalesIntel, “Intent Data is the set of behavioral signals that helps you to understand the intention of your prospects to purchase a product or service.”

It is collected from a web users’ observed behavior – specifically web content consumption – that provides insights into their interests. This insight often correlates to potential intent to take a specific action. For B2B businesses, these signals along with the context for who is the person searching might be a very valuable way to learn if your target accounts or their buying teams are inching closer to a conversion. Let’s take a closer look.

What Are Intent Signals and How to Use Them?

Intent signals are indicators that identify accounts that are actively researching your products on the web. This type of data can be used in a martech tool to score intent for current accounts you are trying to reach. 

In platforms like a B2B customer data platform, these signals can supplement other scores like propensity and persona to give quite a comprehensive picture for running targeted campaigns and building strong buyer models.

Here are the five of the most common use cases:

1. Automated Outreach: Marketing automation and lifecycle management platforms can be set up to track first-party behavioral data and build out a lead scoring model.  For example – adding weightage to certain high intent pages on the site in the model can help identify the surging accounts and automate the initial sales outreach.

2. Sales Prioritization:  Adding intent data into the Sales data mix gives reps an extra layer of accuracy when prioritizing which leads and accounts to go after first. Assuming Sales already have accurate data on who their prospects are, intent helps them to know when is the right time to reach out. This improves sales efficiency by minimizing the time wasted on cold leads.

3. Predictive Scoring The power of predictive marketing technology relies to a great extent on how much quality data is being used. “Quality” in this case means both accuracy and relevancy— If the profiles in your database are up-to-date, intent data can be a useful additional kind of data to use when building predictive models, by providing further insights into how likely a prospect is to buy right now.

4. Personalized Marketing Campaigns: Today’s B2B customer, like their B2C equivalent, expects your company to talk to them as an individual. That personalized marketing is considerably more effective than generic. Intent data helps you run more personalized email and direct mail marketing campaigns by gaining insights into which prospects are interested in relevant topics.

5. More Accurate Targeting for Ads and ABM: Similarly, intent data can be used to provide an additional layer of accuracy to your online ad campaigns. It also can  provide significant value for your ABM programs to help build accurate target account lists.

If you’d like more information about how intent works and what to look for in a vendor download this resource to learn the 5 key things you need to know about intent data to win.

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Leadspace customer data platform

As B2B marketers, our goal is to deliver effective campaigns targeted at the best opportunities that exist within our Total Addressable Market (TAM) – at the lowest possible cost. This means identifying your TAM, developing your Ideal Customer Profile (ICP), and comparing your ICP throughout your TAM to determine which opportunities to focus on, then getting the right campaigns in front of them at the right time – as efficiently as possible. Doing this successfully means creating increasingly accurate, dynamic, and unified profiles of people, accounts and buying centers so we can properly prioritize and target opportunities with data-driven insurance that we’re delivering the right message to the right people at the right time. By implementing a powerful Customer Data Platform (CDP) with profiling capabilities, you can automatically create accurate and up-to-date unified buyer profiles – but once we’ve built these profiles, how do we determine which ones to spend money and time pursuing? How do we prioritize them? That’s where Enterprise Profiling comes in.

By utilizing a CDP solution with enhanced Enterprise Profiling capabilities, B2B marketers can not only overcome the hurdles of demand and ABM, but are also guaranteed to optimize their demand funnel, boost close rates and drive ROI – effectively, cost efficiently and repeatedly. Enterprise Profiling features work in addition to standard profiling capabilities to determine which buying centers, accounts or people to focus on first based on their likelihood to buy your product so you can segment your TAM by persona fit, predictive fit or intent score. 

Enterprise Profiling models compare your historical data and ICP against all the profiles within your TAM then use AI-algorithms to score them by a variety of buying signals to determine their propensity to buy your product right now – as well as indicating whether or not they’re likely to buy in the future. While basic or advanced profiling tools populate profiles with various types of firmographics and technographics such as company revenue, size, industry, sub industry, region, ownership, website technologies, installed base technologies, expertise, specialties, etc – Enterprise Profiling Models analyze them as buying signals to generate algorithmic insights and drive you directly towards closeable business. 

Leadspace Enterprise profiling, customer data platform

All of the signals within these categories are generally scored by their individual “lift” as they relate to historic successes, and a set of overall propensity scores is generated for each buyer profile. Some key Enterprise Profiling Models include:

Persona Fit Scoring: which is built on standard or custom persona profiles to score the existing database and inbound leads based on their closest persona fit, and find net-new contacts within accounts that identify the right buyers using persona scoring.

leadspace customer data platform, persona model

 

 

Predictive Fit Scoring: which is built from the customer’s historical conversion data set (opportunities) and applies scoring that indicates a company’s and in concert with persona fit,  a person’s likelihood to be a good target buyer.

leadspace customer data platform, predictive fit

 

 

Intent Scoring: which monitors user interest (first-/third-party, known and unknown), and applies scoring based on the level of intent activity specific to customers’ products/category.

In short, Enterprise Profiling enables big and small B2B companies to minimize spend and maximize ROI by equipping them with the tools necessary to proactively target the accounts and/or people with the highest propensity to buy. Fuel and optimize your demand funnel with the best B2B buyer profiles enhanced by predictive fit, persona and intent models to revolutionize your TAM-to-opportunity prioritization. Not everyone wants your product, but you can find the ones that do with Enterprise Profiling. To learn more about Enterprise Profiling, check out our product sheet, Leadspace for B2B Profiling.

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leadspace customer data platform

As B2B marketers, our goal is to deliver effective campaigns targeted at the best opportunities that exist within our Total Addressable Market (TAM). Of course, this means identifying your TAM is the first step. The following steps are – developing your Ideal Customer Profile (ICP), comparing your ICP throughout your TAM to determine which opportunities to focus on, and then getting the right campaigns in front of them at the right time. Unfortunately, traditional approaches leave us with siloed data, stagnant (or incomplete) profiles, and the inability to support all opportunities within an account at scale. Not to mention, the amount of effort, money and time that is wasted in going after the inaccurate accounts/profiles.

For this precise reason – there is a need to constantly create (and keep updated) more accurate, dynamic profiles for people, accounts and buying centers so we can properly prioritize and target opportunities with the right campaigns. ABM in the last few years has been the go-to strategy for most B2B marketers to tackle this issue—it sure is useful in identifying the right accounts, assuming you’ve properly compiled your siloed data. However, it fails in identifying the buying teams and targeting the right people within those accounts. It does not factor in the best profiles that as a marketer one would like to target or sales would like to engage with.

So how do we make sure we have the best B2B profiles?

No matter which technology you end up using for creating and maintaining the profiles in your systems – you need to ensure that the vendor provides some of the basic capabilities—as outlined below:

  • Provide third-party account & person data, from a combination of leading B2B data sources. 
  • Unification of first- and third-party data for account context and prioritization.
  • Full account profiles including hierarchies, firmographics, standard or advanced technographics (web, SaaS and installed software and hardware), regular cadence of intent and mobile contacts.
  • Direct integrations into CRM, marketing automation and other data platforms.
  • Complete, up-to-date B2B profiles to target the right accounts and people, with real-time data enrichment, comprehensive segmentation criteria, intent, buyer persona scoring—and easily upgradable to advanced predictive profiling.
  • Simple, straightforward pricing with all-inclusive discovery, enrichment, and form fills— a fraction of the cost of alternatives.
  • Unified first- and third- party data including firmographics, demographics, technographics, account intent, mobile phones, account & contact context, specialties and more.
  • Open, uncompromised quality with dozens of data partners, multisource validation, semantic categorization, and both scheduled and real-time enrichment.

Tools like Customer Data Platforms can provide all of these capabilities beyond unification as they integrate AI-optimized buying signals from curated third-party data-sources that go into building dynamic, multi-level buyer profiles which are regularly updated for accurate, timely understanding of your buyers. That, in conjunction with scoring models (Fit, Intent, Persona, TAM, and ICP), it’s easier than ever for B2B marketers to target the right people, with the right campaigns, at the right time. Read this product sheet to learn more about what to look for as you build your B2B profiles and optimize the whole process so that once you’ve discovered your TAM, buyer profiles are created at the person, account, and buying center levels – meaning you can segment, score, and prioritize entire buying groups—as well as individuals within accounts—instead of just accounts.

In short,  B2B Profiling enables big and small B2B companies to minimize spend and maximize ROI by equipping them with the tools necessary to proactively target the accounts and/or people with the highest propensity to buy. Fuel and optimize your demand funnel with the best B2B buyer profiles enhanced by predictive fit, persona and intent models to revolutionize your TAM-to-opportunity prioritization and drive a full funnel optimization.

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Interview with Amnon Mishor

Leadspace Founder and CTO, Amnon Mishor sheds light on how technology is upgrading the marketing sector and how they are preparing for an AI-Centric world.

AI can dramatically improve the world we live in, and we have to shift the general paradigm to that, which requires education and scientific thinking.

1. Tell us about your role at Leadspace.
I’m the founder and CTO at Leadspace, where I help some of the biggest enterprise technology companies take full advantage of customer and prospect data — their most valuable business asset. As CTO, it’s my mission to perfect our technology offering so that we can help companies all over in identifying closable business using advanced data and AI modeling.

2. Can you tell us about your journey into this market?
Before I started Leadspace, I served with Unit 8200 in the Israel Defense Force, where I was in charge of the Intelligence Systems and Data Science Department of the army’s Technological Intelligence Unit. I learned invaluable lessons through that experience, a key one being how to design and deploy world-class technology to catch terrorists. As I finished my work there, I saw an opportunity to apply data and AI in another industry — B2B enterprise software — where leaders cannot afford to “fail” when they apply AI to their data.

3. How do you think technology is upgrading the marketing sector?
The smarter our technology gets, the better our marketing can be. When I first started my career in the early 2000s, every company was talking about “digital transformation” as everything we did started to move online. It’s interesting to look back at that time and see the very rudimentary digital marketing efforts put in place. Fast forward 10 years, and the focus shifted to data collection led by Google, Facebook, Twitter, and other social platforms, which enabled ad targeting with precision. Now, we are entering a phase that will be defined by how we connect and control the data we have, which will be crucial for marketing teams.

4. How has a data-driven approach enhanced marketing as a whole?
Clearly it is going to completely change customer experience, and help your marketing teams become as targeted and relevant with their offering more than ever before. The challenge is that
companies often buy data-driven tools the same way they used to buy leads or spend money on tactical programs. They treat them like simple add-on tools or features in an existing system. However, our clients have seen the greatest success when they treat martech purchases with the same level of careful consideration they’d use when investing in new software. This is the ideal: IT, sales, CX, and marketing are brought into the RFP process, where they each are able to share what is needed in order to integrate the tool in a way that contributes to customer retention and engagement and drives more sales.
When organizations handle their data strategically, with a single source of truth that aligns all departments, marketing investments translate into better sales and better online experiences for everyone — but only if we can truly trust the data we’re using to make decisions.

5. Can you explain how Leadspace helps in winning Account-Based Marketing?
ABM is a strategy, not a tool. In a sense, the rise of the term stems from the B2C-influenced approach to B2B that dominated in the times of content marketing, where we treated everyone as leads. ABM reflected the change that marketing needs to consider potential customers in terms of their account relationships, or more precisely buying groups, not just as leads. But to do ABM well, and not just pay lip service to the concept, requires: (1) merging first- and third-party data together, (2) understanding buying groups and relationships between them, (3) understanding buying intent, and then (4) figuring out the best way to engage. All the while remembering that people buy things not companies — cascading the insight down to the person level. This is not easy, but a Customer Data Platform (CDP) is at the heart of almost all effective ABM strategies, as it provides the foundation for a single source of truth and understanding of the customer, making tools like CRM, MAP, ABM ads and others more effective.

6. What features of your Customer Data Platform differentiates it in the market?
We have built an AI-driven agnostic B2B customer data platform that helps businesses find, create and prioritize closeable business. Where other CDPs on the market focus on consumers or specific verticals like retail or healthcare, our platform is created specifically for businesses with more complex buyer personas and accounts.
In particular, Leadspace has created a graph — called the B2B Buyer Graph — that allows businesses to layer firmographics and technographics to identify and score intent and customer fit so they can win deals faster. It also allows teams to enrich personas to go beyond name, title, email address and phone number. This graph is updated regularly to help go-to-market teams stay ahead of company and people changes. It also maps individuals to individual buying teams in individual buying centers, and it includes companies of every size, site and team hierarchies. We’ve listened to market demand and are providing an intuitive tool that’s more than just a database.

7. What advice would you like to give to technology startups?
As you race forward, by definition, 90% of everything you do will be sub-optimal, behind or even broken. Focus first on identifying the 10% that you truly do exceptionally well, and nurture it so it can take you to the next level. Say, for example, you have an amazing user experience — cultivate that and make it a core part of your business identity.
After you’ve determined and strengthened what sets you apart, only deal with 10% of issues which are truly broken or will have the highest impact on your business. Say your customer support is sincerely lacking — devote resources to bolstering it. It can be hard to do if you’re passionate about your business, but learn to live with 80% issues waiting to be fixed or sorting themselves out as you grow and move to the next level.

8. What work-related hack do you follow to enjoy maximum productivity?
Treat your life like an athlete that needs to perform at the highest level in every meeting, every day. I think of this more as a lifestyle than a life hack. If you take yourself and your career seriously, you have to treat yourself as a professional athlete would– you can’t be doing your best work if you’re sleepy, hungover, or have general low energy. These are the health routines I stick to to ensure I’m at my optimal performance level: 1) To get a good night’s sleep I don’t do any work two hours before bedtime, or eat or look at screens right before bed. 2) I adhere to an intermittent fasting schedule by skipping breakfast — most of my major meetings are in the morning, so I don’t waste any time on eating food, or thinking about my hunger level. I keep my mind very sharp without food. 3) After the work day, I disconnect and offload through exercise. It’s a great method to discharge the mental load of the day and re-energize.

9. How do you prepare for an AI-Centric world?
AI is a new type of software. The industry is just starting to adopt it and developing a set of tools, infrastructure and intelligence on how to produce AI-centric products on a large scale. It is just the beginning of a huge revolution that will change our experience with technology across the board.
I think there is a lot of uncertainty as to what an AI-centric world entails, and many people are wary of adopting AI so integrally into their lives.

AI can dramatically improve the world we live in, and we have to shift the general paradigm to that, which requires education and scientific thinking.

Preparing people for more AI means getting them accustomed to uncertainty, statistics and science in every instance.
At the macro level, we’re more or less in a race against the inevitability of AI. We have to educate ourselves to have different skill sets, because AI will eventually take over certain industries and leave people without jobs. We’re facing the reality that we have to keep learning new things, because who knows when the computer will render our existing talents obsolete.
As an entrepreneur, it’s about being very educated about what AI can do today, and looking for the best applications of those capabilities. We have to not only realize the huge potential of AI but harness it. As an entrepreneur, we constantly have to be curious and hungry for more. I think AI entrepreneurship involves being creative and thinking outside the box for how AI can be applied or adopted.

10. What are the most recent major developments you’ve worked on?
Leadspace most recently launched Leadspace Studio, the industry’s first performance marketing tool targeted at business users to double results across their funnel by creating high-performing segments and directly activating them across marketing automation and digital media platforms. We’re especially excited about this new product because current users are experiencing a doubling of inbound, outbound and ABM campaign engagement. It’s proof that AI is driving real results for B2B and the enterprise as a whole.
As for what’s next, I can’t share too many details, but all good stuff ahead.

11. Can you tell us about your team and how it supports you?
We have an incredible team across the board. Our product and engineering teams execute at a very high level, and on our go-to-market side, we have a great blend of loyal people who have been with us through the years and new teammates that are bringing new energy and ideas. I love that our company environment has a sense of comfort and collaboration. We’re very mission focused as well as team oriented, and we never blame each other or put sticks in the wheels of the other. I appreciate the members of my team as they not only share their expertise and work together, but they support me by holding me accountable to deliver on my goals and promises.

12. What movie inspires you the most?
For me, it’s always the last movie I’ve watched if it was a good one. I was really inspired by King Richard, which shows the development of Serena and Venus Williams into tennis stars. I learned about staying true to family values and educating our children about lasting truth.

13. We have heard that you have a very joyful work culture, so can you share with us some of the fun pictures of your workplace?

14. Can you give us a glance of the applications you use on your phone?
By order of use: Slack, Chess.com (addictive, be aware!), Apple Podcasts, Apple Health, Apple News (Yes… on an iPhone…).

Tune in to Martech Cube Podcast for visionary Martech Trends, Martech News, and quick updates by business experts and leaders!

This article was originally published on martechcube.com

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Customer data platform, leadspace, account based marketing

In this increasingly complex and dynamic B2B marketing environment, marketers are struggling to keep up with legacy demand and ABM strategies. Cutthroat competition and advancements in AI technologies are driving the need for B2B marketers to take full advantage of any and all tools available to stay one step ahead of their competitors. This means connecting quicker with more buyers, discovering and nurturing more opportunities, operating with accounts at a higher scale, and analyzing higher volumes of interactions even more precisely in order to optimize the delivery of the right campaigns to the right people at the right time – before their competitors do!

In a recent Forrester Report, B2B Needs to Move Beyond Demand & ABM, Laura Cross and Robert Peterson outline the problems that exist within the historically segregated demand and ABM methods, their evolutions, and the 4 cornerstones guiding the transformation above and beyond a simple convergence. The main problems noted stem from the fact that, “the silos that have typically existed between demand and ABM, for example, inhibit potential breakthrough thinking aimed at driving smarter demand and intelligent revenue growth.” And while the adoption of account-based strategies has delivered meaningful results to B2B marketing, they pose the question, “why can’t this data-driven, audience-centric approach be delivered to support all opportunities within all accounts, at scale?” As they expand upon the 4 cornerstones to moving beyond the convergence of these methods, they list critical concepts, actions and thought processes to internalize moving forward to ensure your company evolves with the B2B marketing world rather than falling behind it.

Forrester outlines the 4 cornerstones of transformation beyond convergence as:

  1. Revenue Growth Must Focus On All Opportunity Types
  2. Replace Leads With Buying Groups In Revenue Processes
  3. New Capabilities Must Be Addressed Beyond Convergence
  4. Success Metrics Must Evolve Beyond Sourcing

In short this means that ABM at the account or lead level is not enough — it has to be at all levels of the buying center and buying team.  Which means that active profiling is key. This requires unified first- and third-party profiles with advanced scoring and buying signal prioritization.

Let’s take a look at one of the ways B2B marketers can tackle these 4 cornerstones and ultimately move beyond Demand & ABM – by implementing a powerful Customer Data Platform (CDP). With a CDP solution, all opportunity types (acquisition, cross-sell, up-sell, and renewal) are categorized within dynamic buyer profiles where they’re monitored by numerous unique considerations and buying signals to paint an up-to-date picture of your buyer beyond just their transactional behavior.

With a CDP, you can identify your TAM, buyer profiles are created at the person, account, and buying center levels – meaning you can segment, score, and prioritize entire buying groups – as well as individuals within accounts – instead of just accounts.

A CDP offers capabilities beyond unification as it integrates AI-optimized buying signals from 30+ curated third-party data-sources that go into building dynamic, multi-level buyer profiles which are regularly updated for accurate, timely understanding of your buyers. 

You can take advantage of scoring models (Fit, Intent, TAM, and ICP), so it’s easier than ever for B2B marketers to target the right people, with the right campaigns, at the right time – before your competitors do!

How Leadspace enables advanced Account Based Marketing to buying centers and buying teams:

  • Account Identification & Prioritization: Account-based predictive models w/ full transparency built to prioritize your known set of accounts and identify unknown (whitespace) high value accounts to target.
  • Lead-to-Account Discovery: Ensures existing leads within your Marketing Automation and CRM are properly associated with accounts. The lead to account matching is also available in real-time for net new inbound leads.  Also, includes support for account hierarchy (site level) and subsidiary matching to maintain parent/child relationships.
  • Account Expansion: Use predictive models to “expand” accounts, finding new high-value contacts for each buyer persona.
  • Persona Targeting: Use  best-fit persona signals and rich attribute data at the company and person level to better understand prospects and engage with them in the most effective way.
  • Account-Based Sales Prospecting: Identify and prioritize ideal contacts within key accounts in real-time, using predictive models built on actionable insights at the company and person level.
  • Buying Signals (Intent): Identify unique, actionable buying signals using Leadspace proprietary data blended with traditional “intent data” at the individual and company level.
  • Leadspace Analytics: Measure, track and optimize your ABM efforts across all of your key accounts.

How Leadspace goes beyond ABM with Leadspace Professional and Enterprise Profiling:

  • Advanced Profiling for ABM to pursue and optimize high-spend retargeting campaigns, early funnel scoring prioritization, high-touch proactive nurturing, lookalike messaging, and vertical- and/or persona-based. Key capabilities include identification of buying propensity, in-market company or product interest, persona-scoring for specialist reps, and efficient account & contact targeting.
  • Advanced Profiling for Account & Lead Scoring to pursue and optimize account selection and prioritization, lead scoring & conversion, bigger deals (LTV), cross-sell/upsell, and retention. Key capabilities include being powered by CDP data & activation, actionable model insights, adaptive model refresh, and segment normalization.
  • Advanced Profiling for Product or Account Intent to pursue and optimize monitoring target accounts for in-market buying signals, uncovering and targeting potential opportunities before competitors, and improving sales and marketing intelligence for more timely and effective outreach. Key capabilities include being powered by CDP data & activation, having multi-source aggregation, and topic/trigger weighting.
  • Advanced Profiling for Inbound and Outbound Lead Prioritization enables you to prioritize outreach to Accounts based on predictive fit, weekly intent surges, and product-specific intent scores.

For more information about the evolution of Demand & ABM, issues surrounding their convergence, and a deep-dive into the 4 cornerstones of transformation beyond their convergence, check out the Forrester Report, B2B Needs to Move Beyond Demand & ABM written by Laura Cross and Robert Peterson. Read the Leadspace fact sheet to see how Leadspace’s CDP solution can modernize and optimize your B2B marketing approach in a world where Demand and ABM fall short.

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Leadspace customer data platform

In a recent report, The Future of B2B Marketing, Forrester indicated that B2B marketers are going to need to step up their game to meet marketing’s new challenges. Significant changes in buyer behavior, evolving business models, and technological advances in conjunction with a global pandemic are forcing an evolution in B2B marketing from—changing corporate purpose, changing workforce, expanding technology, and changing buyers. 

Hence it was not a surprise that when Forrester asked global B2B marketing decision-makers in their 2021 Global Marketing Survey how they would focus to help support their organization’s business strategy, almost half (49%) selected the response, “introduction of a new go-to-market strategy or a significant change in the way the organization reaches target buyers/customers and achieves competitive advantage.”

There is a considerable demand (especially after the pandemic and poor future economic outlook) for businesses to implement a laser focused go-to-market strategy in order to maximize ROI and drive efficiencies —but that’s easier said than done. In formulating your laser-focused GTM strategy, it’s important to keep these key steps in mind: 

Determine the Right Profiles

  • Target the right audience.
  • Use real-time analytics to optimize your data and campaigns, including database health and ICP analytics to ensure that your next move is based on insights derived from a complete and accurate profile of your target audience.
  • Identify your ideal customer profile so you target the right accounts.

Target the Right Segments

  • Leverage artificial Intelligence (AI) and intent data. 
  • Use look-alike modeling to generate lists of new accounts that closely resemble your best existing customers. 
  • Leverage intent data and scoring to find previously unknown accounts that are in-market for your product right now. 
  • Prioritize your target accounts using predictive account scoring to score your accounts (both existing and net-new) against your Ideal Customer Profile (ICP).

Move to Direct Activation

  • Combine predictive, persona and intent scoring to reach the right people, at the right accounts, with the right content, at the right time. 
  • Increase conversion-rates using real-time enrichment and lead-to-account matching to ensure optimal automated follow up for ad-sourced leads.

Measure and Optimize

  • Measure and optimize your campaigns.
  • Monitor your sales and marketing data quality in real-time and fix issues as soon as they arise with on-demand enrichment.
  • Optimize ABM digital ad campaigns with in-depth analytics to visualize and analyze how your audiences perform over time.

Many vendors claim to assist you throughout some of these steps, but nothing comes close to facilitating the end-to-end process as does a Customer Data Platform (CDP). As Forrester puts it, “CDPs construct, progressively maintain, and provide timely access to customer profiles. CDPs promise data orchestration capabilities to deliver more compelling customer engagement, including more personalized targeting, interaction design, and offer management across channels.” 

At its core, a CDP is a sales & marketing tool that leverages and unifies first-party data with third-party data then incorporates a variety of buying signals to create dynamic profiles of companies, contacts, buying centers and teams to explore your TAM and hone in on closeable business. Download The Ultimate Guide on Driving Revenue with a B2B Customer Data Platform if you would like to learn more about how CDPs empower your sales & marketing teams to significantly improve pipeline, cut costs and boost downstream ROI.

Going back to Lori Wizdo’s point of view in the Future of B2B Marketing report – “All B2B marketers should feel a sense of urgency,” quoting the 20th-century entertainer and folk hero Will Rogers, she says, “even if you’re on the right track, you’ll get run over if you just sit there.” Changes happening in B2B marketing are rapid and imminent. Learn more about what is driving it in this complimentary Forrester report: The Future of B2B Marketing.

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Leadspace B2B CDP, customer data platform

With ninety percent of companies either evaluating or deploying a CDP, the question comes up as to the difference between a B2B CDP and B2C CDP. In a recent BrightTalk webinar, Amish Sheth (Leadspace VP, Solution Engineering) explored the capabilities and differences between a business-to-business customer data platform and a business-to-consumer customer data platform. What are the core functions of a CDP? What are the benefits of a B2B CDP? What are the different types of CDPs and their key use cases? What makes B2B CDPs different? How do you determine which CDP is right for your business? 

First of all, a customer data platform is personal to your organization and it’s the key to defining shared customer insights. No use case is exactly the same for every company, so understanding your specific needs and use cases is a critical first step to selection.

Customer data platforms were originally designed with B2C marketers in mind, so they weren’t originally set up to cater to the unique needs of B2B marketers. In B2B sales and marketing, you need to know who your best customers are, understand your sales whitespace opportunities and be able to seamlessly activate across both sales and marketing processes to personalize the end-to-end customer experience. B2B professionals need to qualify, prioritize, segment and match your inbound leads to accounts in order to optimize funnel conversion. In addition, you need to get insights and leverage AI predictive and categorization capabilities to promote customer personalization and consistent omnichannel marketing. A solid B2B CDP should be able to do all of this and it should help eliminate data silos, compiling everything into unified company and persona buyer profiles to be accessed across sales, customer support, and marketing.

A customer data platform has three core functions – profiling, segmentation and activation. It needs to build unified account and person profiles from disparate internal first party data sources including CRM, MAP, ERP transactional systems. And it needs to integrate that first party data with third party firmographics, demographics, social and intent data. The profiles need to be kept up to date so that segmentation is accurate and then activate these profiles within campaigns deployed to systems of engagement like ABM, ad platforms, sales engagement, content management, and MAP/CRM systems. Most CDPs are commercially available products. Some companies have chosen, however, to build their own.

Homegrown CDPs function essentially as large-scale storage and typically focus support for first-party data. Key use cases include identity resolution, MDM, and business intelligence. These are highly technical tools that typically have no built-in normalization of data, and they require custom development and integration to use it. Homegrown CDPs are generally built and supported by IT groups and often are not suited for the ease of use necessary for business users.

B2C CDPs add ease of use, segmentation, and activation capabilities. B2C CDPs are typically limited to 1st party data. Activation and segmentation functionality makes them great for use cases such as person-level unified profiles, digital targeting, and web personalization. They’re designed for business users, enabling them to integrate and unify first-party data to create profiles. B2C CDPs incorporate basic and often proprietary activation and they help to improve targeting and create more timely experiences.

B2B CDPs add company-level profiling, cost optimization, operational scale & self-service. Unlike homegrown and B2C CDPs, B2B CDPs bring third-party data into the equation to create more complete account and buyer profiles. This active profile management can be used to build predictive models and buyer classification engines to prioritize leads as well as provide sales & marketing channel activation, unification, and segmentation functionality. Key use cases of a B2B CDP include unified profiles at the person, company, and buying center levels, TAM / ICP modeling, territory planning, business operations, digital targeting, lead personalization, and sales territory creation/assignment. B2B CDPs are purpose-built for the entire business-to-business buying journey and ideal for unifying first, second, and third-party data to build and maintain profiles. They should include models and analytics for insight-based decisioning and should be completely data and channel agnostic. What makes B2B CDPs different? Active profiling management & active funnel management.

Benefits of B2B CDP span across business operations, focused GTM campaigns, and closeable demand:

  • 40% lift in data quality
  • 2x lead conversions
  • 30% more pipe from target accounts
  • 100% more opportunities
  • 60% more pipeline from outbound programs
  • 50% reduction in data spend

Customer Data Platforms are powerful tools, but it’s important to weigh your options before making your selection. Before choosing a CDP, you must have your business priorities straight. First, clearly define your company priorities and strategy before starting vendor evaluation. Next, define the key uses and prioritize them based on potential business impact. Finally set success metrics for expected business outcomes relative to current benchmarks. For an in-depth comparison of CDPs and to gain insight into the CDP selection process, watch the BrightTalk webinar, How is a B2B CDP Different from a B2C CDP?

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Leadspace B2B customer data platform

It’s always that time of year – in sales and marketing we’re always starting a new quarter, ending a new quarter, trying to create demand for the following year or just planning. We always want to start strong and grow better. Let’s assume you start strong. But how do you grow better? How do you outsmart the competition? How do you create sales and marketing systems that help identify the leads that matter? We need to create a system to identify these closeable leads so that sales teams can understand which deals that they can count on for revenue going forward. 

I hosted a webinar where we explored how to up the odds of your marketing and sales campaigns’ success. Closeable revenue is all about the math. Whether that revenue is from demand generation, new opportunities, leads, etc. These days it’s easy (or should be easy) to use systems to determine those opportunities and leads that will convert into closeable business. 

We dove into the 6 steps to increase the odds of building closeable demand and closeable business. It boils down to creating the right profile so you can target the right audience and then building models to understand and categorize which are the right accounts/people to go after. Figuring out which are the best models to identify and categorize leads. Building campaigns using AI to optimize and go after the right kinds of companies and people. Testing the models to identify and evaluate the right results. And finally, do this across both sales and marketing to ensure both teams are fully aligned. So what are the right questions to ask as you go through these steps?

Step #1: Build Better Buyer Profiles – How do you build Buyer Profiles? Do you have a customer data project? Customer Data Platform? How often is it updated? Is it shared across GTM? What’s the breadth of data sources?

Step #2: Target Your Ideal Buyer & TAM – Targeting is easy once you have models. Have you created a model, or is it at least in your head? What are the buying signals that matter? How many lookalikes in the world? Which territory is your next best place?

Step #3: Create Your Buyer Model – Better campaigns are all about “lift” – propensity, persona and intent. How does industry, persona, tech install, engagement or company specialty fit in?

Step #4: Optimize Your Campaigns – Campaign to Buyers and Buying Team combinations by propensity, persona and intent. Do you understand which regions convert with which products or personas?

Step #5: Put Your Buyer Model to the Test – Increase lead flow by removing friction. Are you using A/B testing to formulate GTM campaigns and how much engagement is needed? Are you doing territory and ABM investment tiering based on GTM science?

Step #6: Align Marketing & Sales with Your Buyer Data Platform – Are both sales and marketing aligned together on the same customers and prospects to deliver the outcomes?  Are both teams together leveraging the profiles and funnel optimization techniques to deliver better growth?  Is there an aligned measurement system to track what you manage?

These are the six steps that I use in building scalable and closeable demand. Explore what we found really works and what is delivering today with results for sales and marketing professionals worldwide with respect to each of these 6 steps by watching last month’s webinar, Taking the Guesswork out of Sales & Marketing.

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Leadspace b2b customer data platform

Lead-to-Account Matching is an often overlooked capability of Customer Data Platforms. Depending upon volume, routing complexity, and the response time required in your GTM system, seemingly small errors in your pipeline can snowball into serious distractions and missed opportunities.

Accurate lead-to-account matching is critical to response times in today’s GTM funnel.  It provides a clearer understanding of your customers by ensuring leads and contacts are matched to the right accounts. Without an effective matching system, lead routing and qualifications suffer, and friction between your Sales and Marketing teams increases, and account-based marketing becomes impossible. Doing this manually is time-consuming, cumbersome, inefficient, and prone to errors that can manifest into much bigger problems down the line.

With Leadspace’s Lead-to-Account Matching capabilities, you can execute intelligent ABM, route leads faster and more accurately, and engage with the right people, at the right companies, at the right time, with the right campaigns, by automatically connecting accounts to leads with a higher degree of accuracy than ever before!

Leadspace lead-to-account matching is much more accurate than other matching solutions because we’re not an ABM or data point solution – we’re a Customer Data Platform (CDP), built on the most robust and open B2B data engine on the planet. Our CDP combines unrivaled 3rd-party data coverage with proprietary AI-based person-level profiling and custom personas, all via direct integrations into your existing Sales and Marketing channels. We match leads to accounts with a real-time, 360-degree view of every person and company. That’s why Leadspace constantly outperforms point solutions that rely solely on 1st party data for matching, for example from email address domains.

Leadspace’s account profiling enables you to conduct accurate TAM analysis with data on industries, revenues, and account hierarchies from a sales and marketing perspective. This is different from the way most data vendors look at account hierarchies. They typically focus on the financial and legal structure of the account while we focus on the buying teams and key decision makers.

You need a reliable way to identify the connections between entities. Too often this work is left to sales and marketing to try and carry out manually or is attempted using some type of third party vendor. If you can’t accurately determine a company’s parent or partner companies and the person responsible for making purchasing decisions, it becomes difficult and time-consuming to connect with the right leads and accounts.

How it Works

So let’s get into the details. Our platform can cluster, unify, link, and dedupe company & person identities originating from any data source. Using primarily proprietary AI-based classifiers, we fully unify a record while maintaining data integrity and custom business rules including validation and normalization. We offer real-time, on-demand, and scheduled sync of unified profiles for data management objectives. Profiles can be synced to any activation channel.

The platform is data agnostic and ingests both structured and unstructured 1st and 3rd party data in the backend as well as through our Studio (our self-service UI). Integrations are supported via native apps, REST API, and SFTP. Unification logic is customizable by our clients via the UI and customer service requests.

We support the typical first-party sources (and more!) including CRM, ERP, web analytics, MAP, product usage, and CSX data. Approximately 30 third-party sources provide firmographics, demographics, technographic, and intent data. Additional custom sources can be added upon request. We offer both real-time, scheduled and on-demand ingestion, unification and segmentation workflows for data management. The segments and profiles are persisted and can be synced to any channel for activation.

Lead-to-Account Matching is sometimes an overlooked capability in CDPs. Depending upon the volume, the complexity of routing and the response time required in your GTM system, small errors can mean a lot of distraction or lost deals by your reps. After working with lots of large B2B accounts, we’ve found that not all profiles require the same number of sources for complete profiles used in matching, routing and scoring. For account profiles, we’ve found that 80% of records have between three and eight data sources. And for people or contacts, nearly 95% of records have 8-10 data sources. So it’s important to consider the ability to normalize all of that data into a coherent profile. Our multisource matching does just this!

Finally we take the data and integrate it with our B2B Buyer Data Graph. This creates a customer-specific B2B Graph with multi-source validation — 70M+ companies, 240M+ Buying Centers, 280M people from 30+ curated third party data sources and a broad array of first party data. This is one of the unique capabilities that differentiates Leadspace from other B2B CDPs.

Top benefits of lead-to-account matching

Sales-Marketing Alignment: Ensure leads are routed to the correct account owners, reducing a common source of Sales-Marketing friction. Leads that Marketing spent time and resources to obtain are less likely to be ignored, and Sales is happy that Marketing is providing qualified accounts.

Account Penetration: Sales and Marketing also need to know which individuals to engage within their target accounts. Lead-to-account matching helps ensure accounts are populated with the right people.

Visible Engagement: It takes numerous touch points over several different individuals to close a deal. Lead-to-account matching provides visibility into sales and marketing engagements with all parties at the account level; for example, who’s downloading content from your website, or who responded to an SDR email. Connecting the different engagements across the account in this way helps you guide prospects through the buyer journey, via more targeted, personalized follow-ups.

Customer Experience: Create a smoother, more personalized customer experience, by knowing the full context of the individual you’re talking to. Having multiple reps contacting different people at the same company, or obsolete information on where a particular person is working, can cause a lot of frustration for everyone!

Sales Efficiency: Remove obstacles to Sales efficiency, like cluttered CRM data, multiple account owners and account duplication. In most CRMs, leads and contacts are separate, while only contacts are associated with accounts. This can cause duplication of accounts if the SDR or Sales rep doesn’t know that there’s already an existing account that the lead is associated with. With lead-to-account matching, you can see if the lead belongs to a new or existing account, and the right customer and internal stakeholders who might already be involved.

Don’t let leads die prematurely – ensure they end up where they need to be! Find out how Leadspace’s Lead-to-Account Matching capabilities can improve your pipeline and significantly boost your Sales & Marketing efforts’ ROI (and much more) by scheduling a customized Leadspace demo, including a Data Health & Pipeline Impact Report

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Leadspace b2b cdp, customer data platform

As we all know, great Marketing is the right balance of art and science. This week let’s take a look at how to use marketing science to free up time for marketing art. 

Last week, Leadspace participated in the GDS Summit event along with roughly 200 marketing executives to explore how technology is changing the game in high level Marketing & Sales operations. We met with dozens of companies one-on-one, and were given the opportunity to present a keynote presentation on the final day of the event. 

I gave the keynote – entitled Revenue By Design – in which I explored 4 key areas where companies see challenges in implementing their Go-to-Market strategies – profiling, targeting, campaigning, and closing. At the end of it, the audience was polled on where their biggest go-to-market challenges reside. The results showed 29% in profiling, 29% in planning, 21% in campaigning, and 21% in closing – a surprisingly balanced array of answers. What’s the reason for such balanced poll results? Perhaps there should’ve been an option for “all of the above.” 

What struck me about the polling results above was the 60/40 split. Sixty percent of the audience’s biggest challenge was aimed at targeting – the right company, buying center and buying profiles along with then setting up the right segments to drive a campaign strategy. Getting this segmentation right drives the intelligence and selection of persona-based content, suitability for higher account based marketing investments and more. The remaining forty percent is in making sure that the execution of that strategy drives list creation, media buys and sales activities – often a disconnect not only between marketing platforms/channels but also between sales and marketing.  

Here are a number of questions that were posed as a part of the discussion and my answers:

What step gives marketing efforts the biggest lift in improving the pipeline?

Propensity models. Being able to say here is the likelihood of conversion by company size, geography, industry, who’s in-market, fit scores and intent scores, then being able to sort leads by propensity is an absolute game changer.

Is there a quick way I can get a pulse on the health of my data before moving forward with the next steps?

If you give us a sample of your data, we can quickly produce a Data Health Report to evaluate the completeness of your data, which will indicate fill rates, absent or incomplete data, out-dated data and other indicators of your data’s quality.

What’s on the roadmap – where are you looking to take Leadspace?

We want to help marketing and sales teams to understand what the best strategy is to engage their targets –  allowing the system to target by media, intent and propensity so we as marketers can create the very best content, run it in those channels with A/B testing. By using machine learning to automate more and more segmentation and marketing channel decisions, marketing teams can focus more of their efforts on the art of marketing to deliver the very best content in front of the right buyers.

How does the elimination of 3rd party cookies affect companies’ ability to use data in their go-to-market strategy and how can we offset this?

Cookies go away, so yes, there’s a little more work to do now, but we can offset this by getting more precise about who we’re going after. The key is building great content, which you can dedicate much more time to once you’ve automated the mathematical analysis processes within your marketing efforts. With the right technology in place you can spend more time engaging people in a better way to ultimately earn the trust of data from potential buyers and customers. Implementing the right technology, such as a CDP, enables you to unify the public and private data that you have to target the right people with the right campaigns.  Rich content is the best way to persuade buyers to share information with you – it’s still all about permission-based marketing. 

What stakeholders or members of an executive team should be involved in implementing a CDP?

Sales and (mostly) Marketing Ops, and IT are generally involved, but a CDP is ultimately a business-oriented solution, so typically, it should be the heads of marketing operations and CMOs leading the implementation of a CDP, but if they can align the implementation with Sales Ops and CRO leaders – that’s even better!

What challenges have you personally faced in using a CDP to create closeable revenue and how did you address these?

The first challenge was figuring out what a CDP actually is – which is the most common question people have about CDPs according to Gartner. The biggest challenge I’ve faced though, is how do you actually put it into operations? Where are you going to really use it to sort out what you’re going to focus on? Will I really take the leap to not do any engagement on a lead and just send it straight to sales just because they have a very high propensity or fit? Giving up nurtures that focus on engagement is a great example.  What does it take to trust the AI models to say when a lead should be directly sent without further engagement to sales? This is a bit unsettling at first – until the math proves itself with results!

I had a lot of fun presenting this keynote. Freeing up time for marketing and sales people to focus on the art of their profession is important. As always, Leadspace can help deliver this result by focusing on the science. Contact us to learn more.

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leadspace b2b customer data platform

As a leader in the Forrester Wave, Leadspace is fortunate enough to meet with hundreds of companies every quarter who are looking to tackle sales & marketing challenges. Typically, the challenges fall into 4 categories – profiling, targeting, campaigning, and closing.

Let’s take a look at one of the most interesting customer use cases with Leadspace and how we worked together to go live in less than 90 days.

The Challenge: Double the Pipeline

Their Go-to-Market team was tasked with doubling Sales Qualified Opportunities driven through both new logo capture and installed base customers. The project scope was focused on inbound leads generated from two product lines with separate buying personas, and an existing marketing technology infrastructure that included a couple of Marketing Automation Platform (MAP) instances, batch data enrichment, standard website forms and basic lead scoring. 

The Leadspace Solution: Delivered in 90 Days

Leadspace was tasked with delivering a Buyer Platform measurably improving all aspects of the inbound lead management process. Let’s look at how we helped them profile, target, campaign, and close better.

Profile Better

Established Buyer graph for real-time operational enrichment of marketing and sales profiles across 180+ account/lead/contact fields. Over 30 B2B sources were curated from the hundreds of sources available around the world. These third party sources include over 180 fields of account firmographics, lead/contact demographics, intent and semantically-mapped skills. The Leadspace Buyer Graph now enriches and scores incoming leads at a rate of more than 10K profiles per minute.  This is used in SmartForms throughout the company’s website to eliminate friction and increase form-fill success.  

Target Better 

The team took a few years of salesforce data for each of the products and built a global Total Addressable Market model  and Ideal Customer Profile with AI-driven models for Intent and Propensity to Buy.  Dedicated propensity models for each of the products were developed and operationalized.  These models act as a categorization engine overall for inbound leads. This not only is able to inform day-to-day sales decisions on scoring for lead qualification and prioritization but also informs strategic account targeting, territory planning and campaign segmentation.

Campaign Better

Putting the propensity, persona and intent models to work for lead classification and routing is where the rubber hits the road on inbound lead flows. The first step was to expand the lead volume by developing automated SmartForms that would reduce website form-fill friction – use the minimum number of fields to give Leadspace enough information to fully enrich the profile and score the account/person. There are now over a dozen forms on the website that are being auto-enriched and scored as the lead is captured on the inbound process. As I mentioned, we created multiple custom personas – one for each product buyer.  Once any inbound lead is enriched, then the propensity or FIT scoring classifies the account into a strong or weak lookalike category, the persona score is calculated on the likelihood of the person being a strong persona fit and finally intent is calculated so that 3rd party term search context and 1st party site visit data can be signaled for in-market activity.  A/B testing is now in process to measure lead routing with and without engagement based on scoring.  

Close Better

Finally, Leadspace enriched profiles and models fuel the Salesforce Account, Lead and Contact views with accurate profile data (contact information, company hierarchies, etc.), propensity scoring, persona scoring and intent scoring/topics for both Business Development Reps and Sales Executives. Leadspace enabled their marketing and sales team to utilize the tools and data effectively and established quarterly executive business reviews to assess progress towards goals and platform effectiveness.

This was an example of how a great marketing and sales operations team in partnership with Leadspace can create a real-time lead management system within a quarter.  Next week we will look at the best practices that some of the top tech B2B companies are using for lead-to-account matching, account/lead/contact enrichment and scoring on inbound and outbound programs.

If any of this interests you, please reach out to us here!

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Why is it that Forrester Research finds that 90% of B2B companies are either implementing or looking to implement a CDP? This is an astonishing statistic. It’s surprising because most people question themselves with respect to what a Customer Data Platform (CDP) really is and whether they could describe it to their colleagues. Here are a number of the real world use cases that are driving the need and why this essential technology is changing the way Go-to-Market is done.  

As a leader in the Forrester Wave, Leadspace is fortunate enough to meet with hundreds of companies every quarter who are looking to tackle sales & marketing challenges. Typically, the challenges fall into 4 categories – profiling, targeting, campaigning, and closing.

Profiling Challenges

Despite all the industry mastering and data quality issues, most sales and marketing teams say that their data is still just not right for their GTM use cases. The contact information isn’t accurate. The data is in separate salesforce or marketing automation instances – often because of M&A – and the overhead of trying to do cross-sell or up-sell across these instances is daunting. The fill rates are low in key fields to discern or segment for persona-based content. As a result, actionable account and lead/contact profiles are not in the right shape for easy lead-to-account matching, lead qualification by sales development reps, or even basic campaign segmentation.

Targeting Challenges

The Account Based Marketing trend is driving both sales and marketing professionals to think differently about who really are the companies they could and should be going after. This starts with core fundamentals like what is a company or product or territory Total Addressable Market? What is the Ideal Customer Profile and who are the personas that matter? Who are the lookalike companies that are currently in a whitespace and what’s the best way to go after them? Many of these requirements are at the core of how to align the goals of sales and marketing. Can the sales territory plan be aligned with the TAM/ICP and how can the marketing strategy be aligned by territory, account, product and persona? And what are the buying signals that should inform each of these elements?

Campaigning Challenges

Typically this comes from the sales team suggesting they aren’t getting the right leads and the marketing team coming under pressure to generate more pipeline. The challenge is natural alignment and understanding how by territory, by account, by product and by persona the marketing team is targeting campaigns, which tactics/channels are effective in driving engagement. And which leads are likely to turn into closeable business. Aligning campaign segmentation with sales territory strategy is one of the first steps to performance-improve results. Finally, and maybe most importantly, it’s critical to understand when not to spend money as well. Which channels or activities are really top-of-funnel, which are middle and which are bottom? And when should lookalike buyers trump engagement activity in lead scoring, nurturing and prioritization.

Closing Challenges

Typically this is a combination of the marketing team generating what is seen as the wrong leads and the sales team not knowing which leads if any to focus on. This is a critical juncture of GTM investment. This is the point in time where the real money is spent. How can every advantage be taken to help the sales team understand all the buying signals that any account is giving and if they indicate a match with the company’s ICP and lookalike customers? How many deals can a sales rep focus on in any given quarter? Is it the right persona fit for the economic buyer? When is it the right time to choose to invest in a Proof-of-Concept – likely one of the most constrained resources in the closing process? 

In the coming 12 blogs in this series, we will explore real-world use-cases, best practices, and case studies to demonstrate how Leadspace enables sales & marketing teams to overcome these challenges within each of these four areas – all within a single quarter! We’ll be discussing how we’ve helped companies to…

Profile Better 

  • Utilize our B2B data management and buying expertise to build closeable unified profiles. We’ve critically evaluated, selected and curated the industry’s best third party company and people data sources in the world. 
  • Add the personal touch to your unified profiles. Create the best buyer profiles at the individual level by using personal demographics and buying behaviors from social signals and interests to assign personas instead of nondescript job titles.
  • Route better with the industry’s best lead-to-account matching from Leadspace. Our AI-powered engine enriches and scores leads in real time with firmographic, hierarchy, intent and propensity data to fuel the most sophisticated lead management scenarios.

Target Better

  • Visualize your territory, industry, and geography strategy with Leadspace FIT to identify the highest historically-returning market segments.
  • Leverage Leadspace TAM and ICP to understand your most attractive white space by targeting your best lookalike accounts and personas.
  • Take the guesswork out of identifying the top strategic accounts for investment by focusing on those who are most likely to buy your product.

Campaign Better

  • Leverage Leadspace ICP and Persona to identify the right ABM accounts and fuel high-performing lists based on lookalike buyer demographics, interests and job titles.
  • Laser focus campaign segment members with Leadspace Persona by leveraging our 80 off-the-shelf personas or create your own custom personas. Our persona-matching scores make it easy to match content accordingly for the best program results around.
  • Optimize your ABM investments to intelligently segment your audience by channel and then activate them in Leadspace Studio for closeable demand.

Close Better

  • Optimize your marketing and sales engagement with Leadspace FIT and Intent scores to prioritize the top 25% of leads that deliver 60-80% of your business.
  • Leverage the Leadspace platform to populate and design customized persona-based buyer journeys and engage top prospects with relevant and compelling content through the right channel, at the right time.
  • Put Leadspace for Salesforce to work to deliver the right account contact details, buying signals and propensity-to-buy scores directly in front of your reps to prioritize leads and opportunities in their pipeline.

Stay tuned for next week’s blog, where we’ll dive into the first of many best practices for using Leadspace to Profile Better in weeks – not months!

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leadspace, b2b customer data platform

Keeping your word is arguably one of the most accurate measures of integrity and gives insight into the level of trust someone can put in you – the same goes for companies. However, when it comes to companies, especially within the B2B technology industry, carrying out a plan to deliver a promise and meet customer expectations involves a much more complex process than an individual takes to follow through on a promise. It involves intricate thought and consideration into determining what you can realistically accomplish and what it will take to execute, achieve, and deliver a promise on time. When vendors fail to deliver, trust with that vendor is broken. In B2B, if a vendor fails to deliver, then their customers often won’t be able to deliver on their promises to their customers. This is why it’s especially important for B2B consumers to work with vendors that do what they say, and say what they do.

Not all CDP providers have the same execution roadmap. It’s important to compare each solution’s roadmap so you can know what to realistically expect from them. Consider leading research and advisory companies like Forrester, who recently published The Forrester New Wave™: B2B Standalone CDPs, Q4, 2021 in which they evaluated and ranked the top 14 B2B CDP solutions.

In evaluating each B2B CDP’s execution roadmap, Forrester looked at the product enhancements, innovations, and expansions each organization is planning over the next one to two years, then they examined how each organization’s product vision & roadmap will enhance its ability to serve target accounts. We’re proud to say that Forrester differentiated Leadspace’s Execution Roadmap as a leader among competitors – so let’s take a quick look at Leadspace’s Execution Roadmap!

Overall, our design point is to provide end users, applications and business processes with a breathtaking UX powered by the world’s most comprehensive, reliable, scalable, extensible and trusted B2B Graph. Our 12-month plan is to expand the end-user experience with dynamic Profile360 views, advanced self-service data unification and new sales and marketing analytics. Our B2B Graph is at the core of the unification and mapping process and will be continuously expanded with 3rd party data and customized with first party data across verticals and horizontals – with a focus on even broader coverage and proprietary account hierarchies. Our 24-month plan is focused on sales and marketing AI-driven profiling, planning, campaigning and closing workflow automation, self-service studio and algorithm enhancements. 

To summarize, Leadspace’s Execution Roadmap is not only ambitious, expansive, and in-line with the needs of B2B CDP users – it’s realistic! To learn more about the benefits of implementing a CDP, and to see how Leadspace compares to other B2B CDPs in all 10 evaluated categories (and is ranked overall in the Forrester Wave), check out The Forrester New Wave™: B2B Standalone CDPs, Q4, 2021. Just because we’re a leading CDP solution doesn’t mean we’re stopping there – take your close rates to the next level with Leadspace’s CDP and grow your business with us!

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leadspace, b2b, customer data platform

Market strategy is always an interesting topic to me as a product and go-to-market professional. Most often I think about it from the point of view of the company strategy rather than from the perspective of the customer. After all, it’s about how your company penetrates a market right? The phrases “actions speak louder than words” and “you are what you eat” come to mind as I think about the characteristics of a great B2B Customer Data Platform. The kind of data we normalize & unify, the kind of problems we solve, the algorithms we create, and the kind of user experience we develop speaks volumes about what the product is good at – and the customers who seek that solution.  

A CDP is very personal to an organization. And it’s even more personal than Business Intelligence, which more so represents the craft and strategy of the individual or department. A CDP also represents the customer acquisition and care strategy of a company. The curation of the data, the precision of the personas, the resolution of the predictions and recommendations. It’s important to think about what problem(s) you’re solving with your CDP. Even more importantly – what problem does the vendor solve and who have they solved it for? Evaluating an organization’s market approach now and in the future boils down to ranking their overall strategy and whether that fits with your strategy. 

This problem has been discussed in the Market Approach category within Forrester’s recently published The Forrester New Wave™: B2B Standalone CDPs, Q4, 2021.

In their evaluation of 14 leading B2B CDP solutions, Forrester considered several factors to compare each CDP’s Market Approach. First they looked at the components that make up each organization’s vision of an ideal account profile – variables such as size, vertical market/industry, marketing sophistication, data management maturity, etc. – and how the solution’s go-to-market strategy and tactics are optimized for such target buyers. Then Forrester considered the evidence each solution has to validate the success of their go-to-market approach (revenue growth, customer growth, differentiated positioning, verticalization, geographic presence, sales presence, support for partners, etc.). Forrester looked at any potential commercial model enhancements or partner ecosystem expansions each organization is planning over the next 2 years, as well as how they intend to compete or stay relevant in the face of existing and future competition. Concluding their evaluation, Forrester differentiated Leadspace as having a leading Market Approach – now let’s take a look at it.

Our Ideal Customer Profile is model-driven by 30K companies & 50K GTM leads/contacts. The model includes scored signals with 100+ installed technologies, 200+ firmographics & 75+ specialty signals. Top verticals are B2B Software/Internet, Cloud Communications, & Computers/Technology – with high fit in all tightly adjacent B2B services, manufacturing, financial and pharmaceutical industries. We are fortunate to count as customers 60% of the top 15 ranked software companies, 7 of 12 top Computer HW companies, 3 of 4 top Cloud Communications companies. Our ABM program is focused on 1500 high fit/high intent accounts. Geographical coverage is focused on NA/EMEA, expanding to Asia. Our partner GTM leverages leading B2B agencies like Accenture Interactive and Transmission Agency. And we are ramping with OEM and re-sell partners. 

With the pervasiveness of software, cloud communications and computers, our B2B Buyer Graph consists of a very broad set of small and large companies.  We value tech install data as a prediction of forward-leaning investment/strategy.  We have developed our 80+ off-the-shelf personas in partnership with our customers’ needs.  Our customers’ strategy starts with TAM then seeks the Ideal Customer then via personas the Ideal Buyer.  Our predictive FIT models look at thousands of signals to uncover the lookalike classification engine that can reason through and fuel routing, prioritization and acceleration. 

We believe strongly that data of all kinds can contribute to uncovering the nature and heart of these models. As a result, we are an open platform that coexists with 1st/2nd/3rd data sources/systems of record to provide a source of truth to unify, analyze & optimize buyer/buyer teams signals. Customers use our platform standalone, as a plug-n-play intelligence layer on their CDP/data lake or embedded in their applications or commercial applications/business workflows in an OEM model.

To pull out another old phrase – the proof is in the pudding!  Leadspace’s Market Approach isn’t just a plan – it’s a powerful ideal account profile & go-to-market strategy. To learn more about the benefits of implementing a CDP, and to see how Leadspace compares to other B2B CDPs in all 10 evaluated categories (and is ranked overall in the Forrester Wave), check out The Forrester New Wave™: B2B Standalone CDPs, Q4, 2021. We have every intention of being at the top of the leaderboards in our industry from our product to our approach – let us help you do the same with Leadspace’s CDP!

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Leadspace B2B customer data platform

A platform vision starts with the scope of the company, the product itself and the ecosystem that is built to enable a thriving market. It’s a description of the essence of the product: what are the problems it is solving, for whom, and why now is the right time to build it. A Product vision gives your team a bigger picture of what they are working on and why.

When it comes to choosing a CDP solution, it’s important to consider your current data needs as well as your future needs as your business inevitably grows. Choosing a solution that will grow with you can excel your relationship with your CDP provider from “customership” to “partnership”. In finding a CDP solution with a product vision in line with your organization, consider leading research and advisory companies like Forrester, who recently published The Forrester New Wave™: B2B Standalone CDPs, Q4, 2021.

In Forrester’s evaluation of 14 leading B2B CDP companies, Forrester considers several factors to compare each CDPs including each solution’s product vision. Forrester inquired about each of our visions for the B2B standalone Customer Data Platform market – especially in regard to how B2B standalone CDPs fit into each organization’s overall corporate vision and customer experience. We’re proud to say that Forrester differentiated Leadspace as a leader in Product Vision. Let’s take a closer look at the Product Vision that differentiates us from our competitors.

Front and center on leadspace.com we state that we are all about enabling customers to find, create and accelerate closeable business. We believe the B2B standalone customer data platform is the data and intelligence backbone that fuels the end-to-end customer acquisition and support business processes. That’s a big statement. It means that our vision starts with helping our customers know their market and ideal customer better – it’s all about the right data at the foundation. Next it’s about the AI and predictive models that recognize closeable business and assist in identifying lookalike customers and lookalike behavior. And finally it’s about helping sales and marketing professionals working from the same page on the same targets, with the same intelligence and in very familiar tools of their choice – putting the know-how in the muscle of the organization.

It’s also about building out a platform that can span every aspect of the customer acquisition process. It is driven by an open, scalable and performant knowledge graph sourced by hundreds of 1st/2nd/3rd party data feeds and able to relate billions of customer/buyer entities supported by trillions of firmographic, demographic, and behavioral data points. The graph is accessed by a rich, self-service application for segmentation, analytics, and activation by marketing/sales end users, and is embeddable via APIs in commercial, custom applications, and workflows. The platform must provide tailored AI models for scoring, lookalike, and Ideal Customer Profile analytics while enabling customization for horizontal and vertical specialization. It is the source of truth for all commercial signals at the buyer & buying team levels, and operates in real time or near real time to inform machine-to-machine or human workflows. But none of this matters unless it can be operationalized in the software-driven machine enrichment and scoring processes and the human-driven sales and marketing workflows day to day.  It’s about finding, creating and accelerating closeable business.

In short, Leadspace has a Product Vision that facilitates driving growth – just ask Forrester. To learn more about the benefits of implementing a CDP, and to see how Leadspace compares to other B2B CDPs in all 10 evaluated categories (and is ranked overall in the Forrester Wave), check out The Forrester New Wave™: B2B Standalone CDPs, Q4, 2021. Take your customer’s data and experience to the next level by implementing Leadspace’s CDP – adopt a customer vision worth pursuing.

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Marketing & sales operations systems are multifaceted, occupying across databases, channels and applications. Odds are that your team is using dozens of applications, each requiring different operational parameters. This brings us back to the challenges of leveraging siloed data and sharing scores and updated profiles in applications like CRM, bots, etc. While both are cumbersome processes by hand, the ability to integrate and aim them exists within certain Customer Data Platform (CDP) solutions that have application integration capabilities. With the right CDP, sales & marketing teams can seamlessly integrate data from (and to) a variety of application systems for a unified, single source of truth from which to operate from. Implementing a CDP solution doesn’t just reduce the burden of doing things in separate systems – it completely streamlines your entire sales & marketing pipeline, accelerates close rates, and maximizes your downstream ROI.

But remember, not all CDPs have the same integration capabilities, so you need a way to determine which is the most qualified to incorporate the applications you use. Consider leading research and advisory companies like Forrester, who recently published The Forrester New Wave™: B2B Standalone CDPs, Q4, 2021.

In Forrester’s evaluation of 14 leading B2B CDP companies, Forrester considers several factors to compare each CDP’s Application Integration capabilities. In determining each solution’s Application Integration, Forrester looked into the application categories and specific vendor solutions that are integrated and whether each is bi-directional, custom or native, and finally, which additional functionality is provided by these custom/native integrations. In conclusion of their evaluation, Forrester differentiated Leadspace’s solution as a leader in application integration capabilities! Let’s take a closer look at some of the application integrations Leadspace provides.

Leadspace has out-of-the-box native integrations for SFDC, MS Dynamics, Eloqua, Marketo, Hubspot, Pardot, Google Analytics, Adobe Analytics, SalesLoft, LiveRamp, and LinkedIn.  Beyond these specific platforms, our REST API can be leveraged to push and pull data (bi-directionally) into any API-enabled system on a real time/one-off or scheduled/batch basis. 

As an example of our native integration into Salesforce, check out Leadspace for Salesforce which puts the power of the Leadspace profiles, propensity to buy signals, and other relevant sales data directly into the CRM.

leadspace, b2b customer data platform, integrations

Clients can also feed data into our Ingestion API, which will support most custom data ingestion requirements (push, pull, periodic, etc).  Leadspace also OEMs Dell Boomi and Tray for application and data integration connecting with our REST API.  This enables us to integrate to many systems and channels seamlessly. Our Professional Services team can also develop on top of our Ingestion API to support more custom use cases.  

Leadspace’s solution integrates with the majority of relevant sales and marketing applications and is regularly being updated to support even more! To learn more about the benefits of implementing a CDP, and to see how Leadspace compares to other B2B CDPs in all 10 evaluated categories (and is ranked overall in the Forrester Wave), check out The Forrester New Wave™: B2B Standalone CDPs, Q4, 2021. Seamlessly integrate the power of Leadspace into your most critical applications to boost efficiency across the board with Leadspace’s CDP!

If you interested in learning more about Leadspace and how we can help your business grow better – contact us!

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Low conversion rates from campaigns, high email bounce rates, and poor pipeline velocity are symptoms of a bigger problem – poor data health. Inaccurate, inconsistent, missing, or incomplete data can all negatively impact your bottom line and muddle most other sales and marketing metrics you are trying to optimize. Being able to evaluate your data’s health with analytics is critical to further optimizing its performance and downstream ROI. However, without the proper tools, attempting to explore, monitor and sort your data with applied analytics is next to impossible. 

To overcome that hurdle, sales & marketing teams are implementing Customer Data Platforms (CDPs) with reporting functions to analyze and measure operational data management activities using AI  — all within a user-friendly, point-and-click analytics dashboards. With a CDP, you can derive actionable insight into operational data, prioritize accounts within your Total Addressable Market, customize native reports & dashboards, apply predictive models, track data enrichment, analyze conversion rates and much, much more.

If your team is ready to implement a CDP solution, it’s important to remember that not all CDPs’ reporting capabilities are the same, so you need a way to determine which CDP best fits your needs. Consider leading research and advisory companies like Forrester, who recently published The Forrester New Wave™: B2B Standalone CDPs, Q4, 2021.

In Forrester’s evaluation of 14 leading B2B CDP companies, Forrester considers several factors to compare each CDPs reporting capabilities. Does the solution include reporting functionality to monitor, analyze, and measure operational data management activities and performance data from marketing & sales engagement? Forrester looked at each solution’s approach and metrics, and level of reporting (contact, buying group, segment, account), as well as whether the solution provides revenue attribution reporting (first-touch, last-touch, multi-touch, etc.) and if clients customize and build their own reporting. Lastly they looked at how each solution determined marketing ROI (data sources, process, etc). We’re proud to say that Forrester determined that Leadspace’s reporting capabilities are up to par! Let’s dive deeper into Leadspace’s reporting process and capabilities.

Leadspace provides several mechanisms for account and contact level reporting…

Database Health Analytics – an analytics tool that provides actionable insights into the overall health of your lead, contact and account database from an operational perspective. It delivers clear visibility into how to optimize the database on an ongoing basis. Addresses key use cases such as quantitative and qualitative DB analysis, and reactivation and cleansing recommendations.

ICP Analytics – an analytics tool that drives insights into the lead, contact, and account funnel with respect to conversion rates, by giving visibility into the factors that drive deals forward (the positives) and what doesn’t (the negatives). Provides recommendations on what areas of your business to double down on to optimize efforts going forward. Uncovers previously hidden insights about which segments of your business have potential to drive business. These reports can provide lead source (first touch attribution) conversion and revenue insights.  

TAM Analytics – an analytics tool that gives you the ability to prioritize your total addressable market by applying predictive fit, intent and persona scores to the Leadspace universe of companies and people. This level of insight ensures you can proactively focus on the companies that have a higher likelihood to purchase, are in-market for a product or solution HP sells, and you can engage the right person in the organization based on what they are potentially in-market for.

CRM Reports and Dashboards – Leadspace’s REST API integration enables the ability to customize native reports and dashboards to analyze leads, contacts and accounts. Insights include: enrichment rates across leads, contacts, and accounts – lead and contact health (socially verified people, people who have moved) – identifying duplicate records – account/lead ownership conflicts – territory planning based on account firmographics and fit scoring.

Additionally, our CDP stores data in an OEM’d BI system (OLAP) which enables full BI API access where customers can choose to sync CDP data in standard databases and BI systems or data lakes (such as Snowflake and BigQuery) for their own internal reporting and analytics purposes.

To sum it all up, Leadspace empowers sales & marketing teams to heavily explore and customize a variety of analytics dashboards and derive relevant, actionable insights surrounding your Database Health, Ideal Customer Profile, Total Addressable Market, and Customer Relations Management. To learn more about the benefits of implementing a CDP, and to see how Leadspace compares to other B2B CDPs in all 10 evaluated categories (and is ranked overall in the Forrester Wave), check out The Forrester New Wave™: B2B Standalone CDPs, Q4, 2021. Filter, explore and apply predictive analytics to pursue the right leads at the right time and accelerate close rates with Leadspace’s B2B CDP solution.

If any of this interests you, contact us here.

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Leadspace, b2b cdp, better decisions

Over the last few blogs, we have moved through the process of building out and fueling your CDP, but how do you analyze high-volume data to derive and prioritize those most actionable insights to accelerate business outcome and maximize ROI? How do you determine your next best action? Which segments should you focus on? Which leads should you close first? Without utilizing a decisioning platform to analyze the data, you’re pretty much just flipping a coin.

While marketing automation platforms are valuable for taking immediate action when working within a single channel or a small data set, a decisioning platform envelopes multiple channels, firmographics, scoring models, and other real-time consumer behaviors to incorporate the insights that marketing automation platforms neglect. 

A decisioning platform automates data insights, propensity scores, and other data-driven decision-making processes, putting your data’s power directly into the hands of the people who need it most – your sales and marketing teams. With the right CDP you can create complete buyer profiles and leverage them with decisioning models to quickly seek out closeable leads. 

However, with numerous tools available, it’s important to understand that not all CDPs are the same, especially in terms of their decisioning capabilities, so you need a way to determine which is the most qualified to handle your data needs. Consider leading research and advisory companies like Forrester, who recently published The Forrester New Wave™: B2B Standalone CDPs, Q4, 2021.

In Forrester’s evaluation of 14 leading B2B CDP companies, Forrester considers several factors to compare each CDP’s decisioning capabilities. First, Forrester wanted to know if each solution included AI/ML capabilities to automate next-best action decisions or to make recommendations for next-best actions for prospects and customers, and can it make recommendations for engagement by marketing, sales, and customer success throughout the customer lifecycle. Additionally, they looked at the range of data and insights used to make decisions and recommendations, and the range of decisions and recommendations provided (engagement channel, propensity to engage, timing, messaging, offer, pricing etc.). Finally, Forrester wanted to know if those predictions and recommendations are provided in real-time. We’re proud to say that Forrester determined that Leadspace’s decisioning capabilities are up to par! Let’s dive deeper into Leadspace’s decisioning process and capabilities.

Leadspace native AI and custom modeling can provide actionable insights to recommend next best actions. Our unification, company/person graph, intent, engagement scoring & look-alike capabilities leverage AI/ML technology. The AI/ML-driven scoring models can be used to drive/trigger automated workflows & processes across sales, marketing, & customer success use cases.

  This includes but isn’t limited to:

– Enabling inbound leads to route directly to sales or specific nurturing streams based on propensity-to-buy attributes

– Utilizing AI-driven intent scoring to enable customer success to proactively improve customer retention and prevent churn

– Improving digital marketing conversion rates by utilizing AI/ML-driven propensity-to-buy and intent scoring models to more intelligently curate audiences and improve ad bidding strategies

– Recommending content stream by persona, or product best offering typically in cross/up-sell scenarios.

Finally, with Leadspace, scoring predictions and recommendations are delivered in real-time and models are updated and refreshed on a quarterly or as-needed cadence.

In short, Leadspace provides an optimal solution for automating and pursuing a data-driven decisioning process. To learn more about the benefits of implementing a CDP, and to see how Leadspace compares to other B2B CDPs in all 10 evaluated categories (and is ranked overall in the Forrester Wave), check out The Forrester New Wave™: B2B Standalone CDPs, Q4, 2021. Take full advantage of an automated data-driven decision-making process to accelerate close rates and boost ROI with Leadspace’s CDP solution!

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Leadspace Customer data platform, b2b cdp, marketing technology

In the last three blogs in this series, we discussed how to get your data together to fuel great buyer profiles and then how to use AI/ML-driven identity resolution to match incoming leads and changing buying signals with each of the profiles. Now let’s look at how to turn those profiles into actionable segments to drive your business. 

What makes a segment more “closeable”? We believe that the best segment strategy starts first with two things — coverage and context.  Do your segments start with your Total Addressable Market and cover its entirety? And are each of the TAM segments then divided into Buyer Profiles from your Ideal Buyer Profile? If done well, you’ll have precise segments that you can further hone-in on over time with activation, engagement and other account or buyer personalization like products owned, industry or use case context, etc. Finally it’s important to understand which sub segments perform the best and may show intent or better conversion. Once you have effective segments, it’s time to activate, score, route and alert.

Most of us who grew up in direct marketing were told that the list is one of the most important ingredients of a successful campaign. And the truth is that creating a segment or list for a campaign is pretty much driven almost entirely by a campaign manager’s gut instinct.  In recent years we’ve added intent to the process, which is important, but we’ve seen that it typically only adds a couple of percentage points in upping the odds of creating closeable demand.  The most important buying signals come from understanding the types of buyers who your team has successfully sold to, how to recognize them and classify incoming leads into A, B, C or D categories.  

Once the classification of incoming demand is done, making campaign activation quick and easy is a big part of streamlining your customer journey and improving your customer experience. By implementing a Customer Data Platform (CDP), companies can enable their sales & marketing teams to create smart segments and automatically activate them across numerous channels  — all in the same tool. Furthermore, with a CDP you can receive automatic alerts about campaign performances that enable you to further direct the right campaigns towards the right leads at the right time. 

In Forrester’s evaluation of 14 leading B2B CDP companies, Forrester considers several factors to compare each CDP’s Activation & Alerts capabilities. They wanted to know if the solution enables users to identify and create audiences and perform segmentation for activation, which channels they can deliver an audience for activation (email, advertising, sales, social, direct mail, etc.), if the solution ranks or scores accounts, contacts, and buying groups to prioritize activation, and whether or not segments or audiences are dynamic. Next they looked at how each solution notifies or alerts sales & marketing teams about significant activity or behavior involving a target account, buying group, contact, audience, or segment. In their report, Forrester differentiated Leadspace as a leader in Activation & Alerts. Let’s dive deeper into Leadspace’s activation and alerting framework.

With Leadspace, users can build account segments based on firmo (geo, industry, size, revenue, etc.), look-alike modeling, predictive fit/intent scores, and install base technologies together with 1st party attributes – at all hierarchy levels. Users can expand account segments with priority contacts using targeted personas and any contact attribute. Once created, segments can be updated via workflows.

Leadspace makes data-agnostic segmentation possible, creating a unified database of first-party data combined with our embedded data and other third-party data sets clients have already licensed.

We have a unique ability to ingest first-party data and enable it as criteria for granular, focused targeting and segmentation purposes, e.g., to target accounts that score high for intent and predictive fit that exist within a specific territory/geography. Within Leadspace, users can directly activate these segments/audiences in CRM, MAP, sales enablement, and digital ads via direct integrations or platforms like LiveRamp.

To summarize, Leadspace effectively facilitates and improves your sales & marketing campaigns with seamless Activation & Alerts – see a video of the process! To learn more about the benefits of implementing a CDP, and to see how Leadspace compares to other B2B CDPs in all 10 evaluated categories (and is ranked overall in the Forrester Wave), check out The Forrester New Wave™: B2B Standalone CDPs, Q4, 2021. Accelerate the activation of your sales & marketing campaigns and improve their success rates with Leadspace’s CDP.

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Leadspace Customer data platform, b2b cdp, marketing technology

In the last two blogs we’ve discussed how to get the best data together to drive complete, accurate and easily managed buyer profiles. Sales & Marketing teams know that targeting customers with incomplete and siloed data is tricky at best. Having a single, comprehensive view of your customer’s data in one place makes targeting the right people at the right time with personalized campaigns much more lucrative – but creating unified customer profiles by hand isn’t easy. To automate that process, companies are beginning to implement Customer Data Platforms (CDPs), which empowers sales & marketing teams to seamlessly blend all of their siloed customer data into a single source of truth at multiple levels – profiles for people, accounts, and buying center. Furthermore, with a CDP, those unified profiles (or customer graphs) are automatically updated in real-time as source data changes and new data is ingested.

However, with numerous tools available, it’s important to understand that not all CDPs are the same, so you need a way to determine which is the most qualified to handle your data needs. Consider leading research and advisory companies like Forrester, who recently published The Forrester New Wave™: B2B Standalone CDPs, Q4, 2021.

In Forrester’s evaluation of 14 leading B2B CDP companies, Forrester considers several factors to compare each CDPs’ Identity Resolution and Unified Profiles framework. Forrester started by looking at each CDPs’ identity resolution services and if the process was user-configurable. They wanted to know if (and how) the solutions create unified profiles at the account, buying group, and contact level. Finally, Forrester looked at each solution’s ability to create a persistent store of unified profiles and if they offer B2B Revenue Waterfall™ enablement. In their report, Forrester differentiated Leadspace as a leader in Identity Resolution & Unified Profiles! Now let’s dive deeper into Leadspace’s Identity Resolution & Unified Profiles framework.

Our identity resolution framework is based on deterministic/probabilistic identifiers (IDs). Unique/non-unique company and person IDs are used in clustering algorithms to unify profiles and validate/dedupe data. Unification logic is customer-configurable for business needs. For scalability, we use probabilistic IDs, PII and anonymous, to complement unique IDs for matching. Probabilistic clustering leverages decision trees (XGBoost) and other algorithms. 

Clients that deploy B2B Revenue Waterfall™ leverage us to support execution on the model. In order to optimize for accuracy, we use deterministic IDs for following types of unique Person and Company data. Our Person Unique Identifiers include Workmail, Webmail, Social Profile, Phone, and Cookie/Device ID, and our Company Unique Identifiers include Domain, ID/DUNS, Social Profile, Phone, and IP.

We provide identifiers at different levels of the company hierarchy so you have the ability to unify and create single records/groupings at the global HQ level, Country HQ level, Business HQ level and/or at specific regional/site locations. Our account unification solutions are purpose built to support the operational structure of organizations from an identity perspective that meet marketing and sales needs, not just the legal structure. However, for example, our operational hierarchies can be mapped to D&B DUNs legal based hierarchies to provide this interoperable framework across functions and teams.

To put it bluntly, Leadspace’s Identity Resolution & Unified Profiles framework is one you can trust. To learn more about the benefits of implementing a CDP, and to see how Leadspace compares to other B2B CDPs in all 10 evaluated categories (and is ranked overall in the Forrester Wave), check out The Forrester New Wave™: B2B Standalone CDPs, Q4, 2021. Seamlessly create unified customer profiles to eliminate customer data silos and accelerate the success of your sales & marketing campaigns with Leadspace’s CDP.

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leadspace, customer data platform

Over the past decade or so, the digital revolution has given us a surplus of data. This is exciting for a number of reasons, but mostly in terms of how AI will be able to further revolutionize the enterprise.

However, in the world of B2B — the industry I’m deeply involved in — we are still experiencing a shortage of data, largely because the number of transactions is vastly lower compared to B2C. So, in order for AI to deliver on its promise of revolutionizing the enterprise, it must be able to solve these small data problems as well. Thankfully, it can.

The problem is that many data scientists turn to bad practices, creating self-fulfilling prophecies, which reduces the effectiveness of AI in small data scenarios — and ultimately hinders AI’s influence in advancing the enterprise.

The term “self-fulfilling prophecy” is used in psychology, investing and elsewhere, but in the world of data science, it can simply be described as “predicting the obvious.” We see this when companies find a model that predicts what already works for them, sometimes even “by design,” and apply it to different scenarios.

For instance, a retail company determines that people who filled their cart online are more likely to purchase than people who didn’t, so they heavily market to that group. They are predicting the obvious!

Instead, they should apply models that help optimize what does not work well — converting first-time buyers who don’t already have items in their cart. By solving for the latter — or predicting the non-obvious — this retail company will be much more likely to impact sales and acquire new customers instead of just keeping the same ones.

To avoid the trap of creating self-fulfilling prophecies, here’s the process you should follow for applying AI to small data problems:

1. Enrich your data: When you find you don’t have a ton of existing data to work off of, the first step is to enrich the data you already have. This can be done by tapping into external data to apply look-alike modeling. We see this more than ever thanks to the rise of recommendation systems used by Amazon, Netflix, Spotify and more. Even if you only have one or two purchases on Amazon, they have so much information on products in the world and the people who buy them, that they can make fairly accurate predictions on your next purchase. If you’re a B2B company that uses a “single dimension” to categorize your deals (e.g., “large companies”), follow Pandora’s example and dissect each customer by the most detailed degrees (e.g., song title, artist, singer gender, melody construction, beat, etc.). The more you know about your data, the richer it gets. You can go from low-dimensional data with trivial predictions to high-dimensional knowledge with powerful prediction and recommendation models.

2. Model the future instead of the past: Here’s where we go back to the basics a little. There are two ways to do data science: We use the empirical method when we have no idea what we are looking for, which lets the data tell us the story. We use the classic scientific method when we claim a hypothesis or idea and then build a test to prove it. The issue is, companies often rely on one or the other, but in reality, you need a combination. If we rely on the empirical method and let the data tell us what we want, then we fall into the trap of creating self-fulfilling prophecies. We see this when working with a new product offering that has no historical data to guide you. You won’t be able to validate the product offering for a new customer base until you design a test to do so. So, if you can both create a hypothesis and combine that with a test that utilizes data or gathers even more, then you more accurately look to the future.

3. Add meaning (semantics) to your data: Once you have your hypothesis in place, teach the system what data relates to what. When your sample size is small, but you have many variables that describe it, you can run into the issue of “slicing your data too thin.” Imagine analyzing an online shopper who bought diapers, bottles and nursery decor. You zoom in too closely and you don’t see the pattern that this person might have a baby. External knowledge and human expertise can help businesses achieve better results by applying semantic modeling or context around these variables and accelerating machine learning — especially when modeling a “small data” problem. The trick to getting this right is in building out a strong taxonomy. We work with one of the largest medical device companies out there, and with millions of SKU numbers in their catalog, it’s imperative that human experts develop the taxonomy to understand and characterize families of products in order to also understand customer patterns and improve predictive modeling.

4. Think “fast” and think “control”: Nearing the final steps, we go back to the data, because it’s ready to support the hypothesis and you’re ready to run your test. If possible, create your own lab environment where you can introduce more variables and outcomes that haven’t been used in the past and quickly run multiple tests (A/B testing) to learn from. This approach works well in marketing campaigns where you don’t need to wait until the end of a long sales cycle to receive feedback around lead conversion. Especially when past data is limited and you need to model a potential future outcome, designing a “control” is a critical step to finding the whole truth. Take the COVID-19 vaccine as an example. If we zoom in and look at the fact that people who are vaccinated are still getting sick, data tells us that the solution is failing. But if we add a control group (unvaccinated people), zoom out and compare our past to the possible future, we see that the model is working.

5. Model to hit business metrics, not just past results: If you continue using what worked in the past to predict the future, then the past is all you are going to get. Marketing may tell you that your model is producing amazing results generating new leads, but if you aren’t closing new deals, the model is still ineffective. With your richer data, hypothesis, semantics, control and trials set in place, everything should now be measured against business results — that is, revenue. I work with some of the biggest B2B companies out there, one of which experienced tremendous growth during the pandemic. It was one of those “right place, right time” situations that took the company from a small startup to a household name. As it moves into a post-pandemic world, it can’t model a self-fulfilling prophecy, because its future is entirely different from the past. So, one thing it’s done really well is stay hyper focused on the bottom line rather than getting distracted by local, misleading conversion, optimization, etc. The world is changing so much and so quickly that the impact on the bottom line is the only metric you can really trust.

It’s easy to make the excuse that without enough data, AI will never be an option. But as discussed above, the trick to applying AI correctly to small data problems is in following correct data science practices and avoiding bad ones — like creating self-fulfilling prophecies.

So whether your data is limited because you are a B2B company or you are launching a brand new product, AI can still be a valuable asset.

When AI starts to correctly solve both the small and large data problems, that’s when it will deliver us into the next generation of science and technology.

This article first appeared on Techcrunch+ .

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Leadspace Customer data platform, b2b cdp, marketing technology

As we all know, buying signals come in many shapes and from many sources.  In last week’s blog we discussed how to bring them all together to create buyer profiles at the account, buying center and contact level. Now let’s look at how to practically source all of this data easily while understanding how you can use it in your sales and marketing teams.  

In the olden days of marketing — aka ten years ago — it used to be okay to do an annual data update. It seemed like there weren’t as many acquisitions. People didn’t change jobs as much. And market dynamics just felt a whole lot less fast paced. For the most part we were just beginning to feed dynamic websites. Today’s marketing and sales automation tools, chat bots, etc. need to be fed by the very latest data. Is this person at the same company they were? Does their social profile indicate an interest in our company’s kind of products? Are they a customer? Or not?  

In Forrester’s evaluation of 14 leading B2B CDP companies, Forrester compared the types and sources of data each solution supports. To make their determinations, Forrester considered several factors such as how the CDP incorporates 3rd party data, if (and how) they support consent-only data collection & usage model, whether the solution shows the source of data collected and stored, if the solution offers any proprietary 2nd party data, and if the solution offers native support for account data with a database table, structure, or schema that is account-aware. Let’s look at how Leadspace compares and go through some of the Data Sources & Types that are supported by Leadspace.

We’re proud to say that for supported Data Sources & Types, Forrester differentiated Leadspace’s Buyer Data Platform as a leader among standalone B2B CDPs!

Our B2B Buyer Data platform supports third-party data ingestion through native integrations (example: ZoomInfo) and custom data ingestion via API and SFTP. Included in our core offering is embedded third-party data from our partnerships with ~30 data vendors and our search know-how that allow us to contextualize open and public web/social data. Customer data combined with embedded data and third-party data are utilized to create a client-specific graph. As an open, flexible platform, our graph can easily onboard any new data source. 

It’s important in today’s privacy world to understand what consents are necessary for campaigns, phone calling or texting. Clients can unify and aggregate consent flags across systems within their unified graph and leverage this for segmentation. Leadspace also shares data source fields when performing embedded data enrichment. 

Many CDPs in the B2C space only operate on 1st party data. Leadspace offers both branded and embedded 3rd party data as part of our core platform offering. This is based on data partnerships we have established with ~30 data vendors as well as our platform search capabilities that allow us to contextualize open and public web/social data.

Leadspace Customer data platform, data integration

All customer data combined with the use of our embedded data and/or additional 3rd party data sources combine to create a client-by-client specific buyer graph. Native schema of core objects include: Person, Company, Activity, and the relationships between them. As an open and flexible platform, our graph has been built to easily onboard any new data source.

Finally, we have a standard templatized schema of core B2B objects as well: Person, Company, Activity and the relationships between them. Every data point that is passed is parsed into one of these three objects. 

Data Sources and types are important to consider — especially when thinking about the number of sources that actually complete a record for an account or contact. Thinking through the source by record and any applicable privacy concerns is also very important. To learn more about the benefits of implementing a CDP, and to see how Leadspace compares to other B2B CDPs in all 10 evaluated categories (and is ranked overall in the Forrester Wave), check out The Forrester New Wave™: B2B Standalone CDPs, Q4, 2021

In our next blog, we will be tackling Identity Resolution. Stay tuned!

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Leadspace Customer data platform, b2b cdp, marketing technology

Complete buyer profiles at the account, buying center and contact level are critical. This starts with leveraging your ongoing 1st party data with a variety of 3rd party data to create profiles (or a full buyer data graph) — combining what you know about the customer with what the world knows about the customer — for better lead to account matching, classification/scoring, context and insights. And buying signals come from all kinds of systems with data that can be in all kinds of formats.  

Integrating & unifying numerous types of variably-sourced data on your own is extremely tedious, time-consuming, and error-prone. Luckily for marketing and sales operations professionals, implementing a Customer Data Platform (CDP) turns that cumbersome process into the first of many automated steps in your buyer’s journey.

Of course all CDPs are not the same. So you need a way to determine which best fits your needs. Check out the latest from Forrester, who recently published The Forrester New Wave™: B2B Standalone CDPs, Q4, 2021.

In Forrester’s evaluation of 14 leading B2B CDP companies, Forrester considers several areas of capability to determine and compare their individual data integration and unification qualifications. Forrester takes into account integration capabilities (deduping, cleansing, normalization, lead-to-account matching, etc), integration processes (workflow visualization, data ingestion, frequency, customization, etc), and supported data sources/types (1st and 3rd party integration, CRM, marketing automation, structured/unstructured, data sources/providers, firmographics, technographics, intent, etc). While Data Integration & Unification is just one of 10 variables Forrester uses to rank B2B CDPs, let’s look at how Leadspace compares and go through some of Leadspace’s Data Integration & Unification capabilities.

We’re proud to say that for Data Integration & Unification, Forrester differentiated Leadspace’s Buyer Data Platform as a leader among standalone B2B CDPs!

So let’s get into the details.  Our platform can cluster, unify, link, and dedupe company & person identities originating from any data source. Using primarily proprietary AI-based classifiers, we fully unify a record while maintaining data integrity and custom business rules including validation and normalization. We offer real-time, on-demand, and scheduled sync of unified profiles for data management objectives. Profiles can be synced to any activation channel.

The platform is data agnostic and ingests both structured and unstructured 1st and 3rd party data in the backend as well as through our Studio (our self-service UI). Integrations are supported via native apps, REST API, and SFTP. Unification logic is customizable by our clients via the UI and customer service requests.

We support the typical first-party sources (and more!) including CRM, ERP, web analytics, MAP, product usage, and CSX data. ~30 third-party sources provide firmographics, demographics, technographic, and intent data. Sources can be provided upon request. We offer both real-time, scheduled and on-demand ingestion, unification and segmentation workflows for data management. The segments and profiles are persisted and can be synced to any channel for activation.

Lead to account matching is sometimes an overlooked capability in CDPs.  Depending upon the volume, the complexity of routing and the response time required in your GTM system, small errors can mean a lot of distraction or lost deals by your reps.  After working with lots of large B2B accounts, we’ve found that not all profiles require the same number of sources for complete profiles used in matching, routing and scoring.  For account profiles, we’ve found that 80% of records have between three and eight data sources.  And for people or contacts, nearly 95% of records have 8-10 data sources.  So it’s important to consider the ability to normalize all of that data into a coherent profile.  Our multisource matching does just this!

Finally we take the data and integrate it with our B2B Buyer Data Graph.  This creates a customer-specific B2B Graph with multi-source validation — 70M+ companies, 240M+ Buying Centers, 280M people from 30+ curated third party data sources and a broad array of first party data.  This is one of the unique capabilities that differentiates Leadspace from other B2B CDPs.

As you can see, there is a lot to consider in Data Integration and Unification. Simply put, Leadspace can handle your buyer data integration & unification needs and beyond – just ask Forrester! To learn more about the benefits of implementing a CDP, and to see how Leadspace compares to other B2B CDPs in all 10 evaluated categories (and is ranked overall in the Forrester Wave), check out The Forrester New Wave™: B2B Standalone CDPs, Q4, 2021

In next week’s blog we will tackle the next selection category — Data Sources & Types. 

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