Where Are You on the Account Selection Maturity Model?

At Engagio, we think about account selection using a four-stage maturity model depending on the methods used to compile target accounts.

No matter where you are on the spectrum, from relying on manual approaches to using the most sophisticated data modeling and analytics, you can still see real and tangible value from Account-Based Everything (ABE). This reliability is one of the reasons that ABM is known to deliver the “highest Return on Investment of any B2B marketing strategy or tactic” (according to the ITSMA.)

Selecting your target accounts (the most important step in the process) is a blend of both manual intuition and data-driven logic. Doing it right combines gut feel, historical performance, and sometimes predictive data science to come up with an “Ideal Customer Profile.”

The ABE Account Selection Maturity Model starts with simple sales rep account selection and progresses through to full predictive analytics.

Consider each of these four levels:

Level 1: Rep Self-Selection

Picking accounts manually is all about gut feeling. Sales teams generally know who their best customers are, and what they look like. This collaborative decision is based on intuition and experience, and won’t be wildly off-base.

Level 2: Basic Data

At this level, marketers add in data such as company size and simple scoring to weigh their best options. By adding a bit of analysis, more opportunities can be uncovered, and the right level of value can be assigned to target accounts.

Level 3: Advanced Data

Every existing database is only as useful as the data within. At this stage of account selection maturity, add advanced data about prospective accounts from third-party vendors, such as technographic insights (which technologies are used at target accounts) and intent data (which identifies who is showing in-market behavior). In addition, to account for this new layer of insights, more advanced scoring or modeling is necessary to weigh accounts and prioritize the list.

Level 4: Predictive Analytics

Taking a more sophisticated approach to building a target account list for Account Based Everything includes the use of predictive scoring and modeling. This approach uses data analysis to choose the companies most likely to buy based on the ones who have already bought or become opportunities. All types of information used in previous levels (plus more) are aggregated and analyzed in a complex model that would be nearly impossible to execute manually.

Though any of these four stages will be an effective start to choosing the right accounts for your Account-Based Everything strategy, the more data you include, and the more sophisticated your modeling, the better your chances of optimizing resources.

Wondering where to begin with Account Based Everything? Start by downloading The Clear and Complete Guide to Account Based Marketing for the six ABM processes, the new ABM metrics, how to manage change, and a whole lot more.

Where are you on the model?

Jon Miller
Jon Miller
Jon Miller is CEO and founder of Engagio. Previously, Jon was the VP Marketing and Co-Founder of Marketo. He is a speaker and writer about marketing best practices, and is the author of multiple Definitive Guides including Marketing Automation, Engaging Email Marketing, and Marketing Metrics & Analytics. Jon has a passion for helping marketers everywhere, and is on the Board of Scripted and is an advisor to Optimizely and Newscred. In 2010, The CMO Institute named Jon a Top 10 CMO for companies under $250 million revenue. Jon holds a bachelor’s degree in physics from Harvard College and has an MBA from the Stanford Graduate School of Business.

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