Data Science Enables ABM to Achieve Its Full Potential

The article was originally published in streetfight.

What: B2B marketers have been embracing ABM for the last decade, but targeting can be challenging.

Why: When going after large, enterprise accounts the internal organizational units that ABM can but more than 100,000. But, the buying group that makes purchasing decisions is normally about 20 to 30 people.

Big Picture: In that environment, a B2B marketer could run an amazing ABM campaign that generates high CTR but very few qualified leads. Why? Because it never grabs the eyeballs of a single decision-maker. For the most part, B2B marketers still don’t know who is viewing and clicking on their ads, so accurate attribution is impossible.

ABM’s evolution must begin with finding ways to achieve greater levels of personalization that will enable marketers to identify, target, and engage the stakeholders within organizations who wield the real power when it comes to making buying decisions. Only when the targeting challenge is solved can the attribution problem be addressed.

Engagement is a particular area where evolution is required—not just on the part of ABM solutions, but in the thinking of the B2B marketers who use them. Too often, engagement is viewed as an ABM campaign’s KPI. Once a certain level of engagement is achieved, it’s chalked up as a win. The marketing team may then try different versions of messaging with engaged prospects or simply hand them off to the sales team. There are problems with both approaches, and they stem from the fact that outcome is still being measured at the account level.

As long as the universe of viewers of a particular ABM campaign within a targeted account remains essentially amorphous, the idea of A/B testing alternative messaging is dead on arrival. It’s impossible to determine individuals’ varying media consumption habits within the targeted group or whether they have seen both versions. Exposures might include a high percentage of viewers who have seen only one or the other version, but not both, which severely limits the advertiser’s ability to determine relative performance.

Passing along prospects as qualified leads to the sales team simply on the basis of engagement suffers from the same fatal flaw. Engagement can be a proxy for intent to buy. But without the ability to identify which of those prospects, if any, has a say in the buying decision, the potential for disappointment on the sales side is immense. It can undercut ABM’s objective of promoting closer cooperation between the marketing and sales teams over the long run.

To solve these problems, ABM’s evolution must follow a path similar to that of video technology. ABM 1.0 took B2B marketing from black-and-white to full color. ABM 2.0 brought automation into the picture and opened the door to digital solutions. Now it’s time for ABM 3.0, what might be called high-resolution ABM. Data will be its chief enabler.

High-resolution ABM solutions must be able to incorporate engagement data, buying-intent data, and behavioral analysis in ways that empower B2B marketers to target at a more granular level. It must give marketers the ability to serve their ads directly to buying-decision makers and eliminate the chafe of account-wide messaging.

High-resolution ABM must also be capable of deciphering the internal dynamics of multi-person buying teams, which research has shown play an important role in purchase decisions. With these capabilities in place, the attribution problem is finally solved and accurate measurement of ABM performance becomes a reality.

The data technology and tools to enable high-resolution ABM are already available and constantly being refined. The onus is now on ABM solution providers to embrace this evolution. They must offer B2B marketers the capability to target prospects with a level of data-driven accuracy that will vastly improve performance—not only on the marketing side but on the sales side as well.