The explosion of account-based marketing (ABM) at scale has been enabled by the proliferation of data and insights. It’s now within the reach of all B2B marketers to get more precise about who, how, and when they are engaging with customers and prospects. But the question remains, for marketers as well as for the business at large: What are the best bets, the targets that should be defined and prioritized, and the audiences that will impact revenue?
Answering these questions means interpreting all that data to focus on the most important insights. Most will turn to their ideal customer profile (ICP) and/or win-loss analysis to gain an understanding of where they should focus. While these are good places to start, both have shortcomings that keep them from delivering a full picture of ROI potential:
- ICP stops short of the full opportunity landscape. In practice, there are often several weaknesses with ICP: lack of specificity, lack of clarity or actionability against product and solution offerings, or even a lack of basis in reality if too much focus is given to the “ideal” rather than the verified. But even when done very well and compiled according to best practices, ICP does not illustrate the relative weight of factors that contribute to opportunity fit — it shows one high-fit profile or a list of criteria without showing the spectrum of how strongly each relates to fit or to the other criteria points. It also falls short of mapping the conditions within an account and buying group that engender problem awareness, realization of value, and a favorable outcome. In other words, it is not a guide of all the opportunity factors that contribute to lasting revenue — which is what demand and ABM marketers need, represented in terms that they can “plug and play” into their engagement efforts.
- Win-loss analysis stops short of the customer lifecycle. The “favorable outcome” referenced above is not simply a won deal. Those who invest heavily in active buying signals or “quick wins” could find themselves with a thrilling win rate but a persistent, pernicious churn. Strong, lasting value does not mean the largest deals or even necessarily the strongest win rates. It means customers who derive value — who use and love your products and solutions — who stick around, and who come back for more.
Insights from existing customers therefore should be the first port of call to shed light on the nature of the opportunities that deliver durable business value. For more on how to understand and map out high-fit opportunities, including tools that can be leveraged and customized for data discovery and fit modeling, clients can check out How To Define Fit Variables For Propensity Modeling and schedule a guidance session with me to discuss in more detail.