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.
- 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.
- 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.
- 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.
- 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.