No one really needs to convince you that account-based marketing works. Today, ABM is part of the scenery for B2B Marketing and Sales. Businesses can see for themselves that it works, and they’re rapidly adopting it.
But at the same time, it seems many of them find it a real challenge to move beyond the starting line.
According to The State of Account-Based Marketing, some 70% of B2B marketers interested in ABM are either still just “considering” it, or dithering at the “Early Stage” of ABM implementation (which hardly sounds like true ABM at all: “some basic management, targeting, and measurement at an account level.”)
A big reason for this is that on the operations level, many ABM adopters just aren’t ready for it. One of the main challenges we’ve identified among customers is their data: you can’t conduct effective account-based marketing until your data is in order and providing you with clear, actionable intelligence, insights and recommendations.
In this post, we’ll cover seven crucial ingredients to preparing your data to execute successful, engaging ABM campaigns:
1. Selecting The Right Named Accounts
Your target or “named” accounts are the foundation of any account-based marketing campaign. Getting your target account list right will determine the success or failure of your ABM efforts.
For many marketers, it’s a real conceptual shift to begin from the account-level instead of just looking for qualified leads (although as we’ll cover below, the two aren’t mutually-exclusive by any means). That’s one of the reasons why effective ABM requires Marketing and Sales to collaborate closely from the very beginning (Sales will typically have a better idea of the kinds of accounts to go after); and conversely, why account-based marketing is a great way to align Sales and Marketing.
To create an accurate target account list, you’ll need highly specific data. It’s not enough to know a company’s industry, size or geographic location; if you’re focusing on a particular company you need to understand their pain-points and challenges, and formulate customized solutions that solve them. You need to dig deeper, for example:
- What technologies do they have installed already, and are they compatible with your product?
- Are they already using a competitor?
- Does the company specialize in a particular field within its industry, or offer some unique expertise which might be relevant to your own offering?
Ideally, you need to map out the anatomy of your best customers, and find new accounts which resemble them. As we’ll discuss a little later, that’s not something you can do manually at scale.
Another useful tip in identifying named accounts is that it can sometimes be effective to work backwards. If you identify a new qualified lead who looks very promising, just assign that entire company to a sales rep as a named account.
2. Individual-Level Data
Despite the name, “account”-based marketing always boils down to the key individuals within your target accounts. Your audience is still made up of real people. In fact, since with ABM you’re targeting a narrower, specific group of decision-makers and influencers — the buying centers or “Demand Units” — your messaging and engagement needs to be far more personalized and relevant than “traditional” lead generation methods.
Even with the most accurate account list and account-level data, if you’re lacking the deep, relevant insights you need to engage the people who matter, your best efforts could still miss the mark. To execute ABM effectively, you need both company and individual-level data. And again, it’s not enough to make do with superficial information like job title; insights like expertise, job responsibilities, seniority and familiar technologies will form a far more accurate picture of who they really are.
3. Lead-to-Account Matching
Lead-to-account matching is critical to any demand generation strategy, as it enables accurate lead routing. But with account-based marketing, the ability to match the right leads to their appropriate accounts is even more urgent: How can you align Sales and Marketing when contacts/leads aren’t matched to their right accounts? When a new, qualified inbound lead comes in, which account executive should you be assigning it to?
It’s worth noting here that not all lead-to-account matching solutions are equal. For example, if the vendor is only using your existing, first-party data, that might not be enough, and odds are that at least some of that data is incorrect or outdated.
Beware as well of “fuzzy matching” solutions, which use the domain from a contact’s email in your CRM to match them to the related company. Assuming the contact data is accurate this might work in some cases, but it won’t deliver accurate results in many instances — for example where a record only has a personal email. Another common limitation is when the company domain name is for a subsidiary of a larger, parent account (or vice-versa): through fuzzy matching it will register as an unrelated, separate account, which means you could miss a crucial lead, or alternatively end up with multiple sales reps handling the same account, just at different levels.
4. Site-Level Matching
Following on from the previous point, true lead-to-account matching needs to include site-level matching capabilities.
By way of illustration: A new inbound lead belongs to Company X, which looks like a great net-new account, so you route it to James in Sales. What you don’t know, however, is that Company X is really just a subsidiary of Company Y, which is already being handled by Rebecca. This could result in a needless duplication of efforts, which means wasted time and effort, and potential friction within your Sales team.
Site-level matching takes lead-to-account matching to its logical conclusion, by providing you with the complete picture of your account hierarchies.
(Site-level matching is also very useful for territory planning.)
5. Intent Data
Intent data is a very hot topic right now, and it’s easy to see why. Although we’re still in the early days as far as intent signal analysis is concerned, there’s little question that intent data can be effectively used to boost sales and overall engagement.
You can read more about the advantages of intent-data here, but in short, it’s a great way to understand where a particular prospect is in the buyer journey and then engage them accordingly. Did they visit your website? Download a particular piece of content? Visit a page/pages on your site that indicates a certain interest or particularly high propensity to buy?
Of course, intent data isn’t sufficient without the other layers of company and individual-level data we outlined earlier. “Intent” needs to be paired with context regarding who the individual you’re looking at actually is, and whether your solution is even relevant to them at all.
6. Persona Modeling
As mentioned earlier, getting enough of the right data on individuals you need to engage within an account is crucial for ABM. But how do you know who those people are to begin with? Sometimes, particularly with smaller companies, it’s fairly simple to work out who the decision-maker is likely to be. More often, however, it could be any one or more of a number of options. To execute ABM effectively, you need to map out your buying centers in advance — that includes identifying the relevant decision maker or makers, as well as any potential “influencers” within that account who could also be key to winning a deal.
This can be done manually for one or two accounts (and even then, with some degree of trial and error), but there’s no way to scale this kind of painstaking research for a fully-blown ABM campaign — or for any strategic demand generation strategy for that matter.
Watch — How to combine Persona and Predictive Modeling to improve engagement & conversions:
Using persona modeling, you can create custom personas to look out for in your target accounts, and then score prospects based on their fit to that persona.
Here at Leadspace, we use persona modeling in our own account-based marketing campaigns, to pinpoint who are the decision makers vs. influencers vs. everyone else in our target accounts. Leadspace persona modeling essentially combines three factors to build a highly accurate persona model:
- Direct input from Sales, Marketing, and anyone else who knows what to look for
- Analyzing examples of customers who bought the product already
- Using 3rd-party data to uncover any key “hidden” attributes to look out for
7. Artificial Intelligence
In terms of hype, Artificial Intelligence is taking over where ABM left off. But the truth is, where applied correctly AI martech is the perfect compliment to account-based marketing.
ABM is all about being more strategic, more data and intelligence-oriented and personalized than “traditional” lead generation. To pick the right accounts from an endless ocean of prospect data, and then understand precisely how to engage with each one, isn’t something you can do manually — no matter how large and hard-working your Sales and Marketing teams are.
Marketers and sales reps alike don’t want (and can’t afford) to spend weeks on end pouring over data. By contrast, AI is built precisely to carry out those kinds of activities at scale, leaving the people in your organization to do what they do best: marketing and selling your product.
For example, companies like Tipalti have managed to automate the entire target account list-building process — saving their marketing team countless hours, increasing conversion rates and significantly expanding their target market. They’re using look-alike modeling, powered by Deep Learning AI, to generate customized lists of net-new accounts who closely resemble their best customers. (Read more here.)
Others, like Marketo, are combining predictive modeling on both the account and individual level, with persona modeling, to quickly and accurately predict which accounts to go after first and which leads within those accounts to engage. (Read the full story here.)
For more tips and best practices for executing successful account-based marketing campaigns, check out our ABM Toolbox for B2B Marketing and Sales: