The industry is buzzing these days with marketers eager to test and prove the latest predictive marketing data. The idea is simple: By targeting companies that have a record of investing in a product category, you benefit from greater efficiency, a higher close rate, and shorter sales cycle.
That type of predictive-driven targeting manifests itself in the form of a "Named Account" list. It's usually based on user or company activity, and used either programmatically through systems like Demandbase or as a shared list given to third-party media vendors with the instruction of "only send leads who work at these companies."
Such specific targeting often results in restricted supply, naturally drives a higher price point for execution. The gamble for the marketer is that this price increase is well worth the conversion rate increases and sales acceleration.
According to what we hear and see as evidence, this gamble appears to be paying off. The question then becomes a matter of how to use all aspects of the marketing industry to develop the most effective account-based marketing (ABM) lists.
Letting Yesterday's Behavioral-Data Buying Inform Today's ABM Strategies
The idea of interest-based marketing is far from new. Audience relevance is the cornerstone of effective marketing.
Not long ago in B2C marketing, behavioral- and audience-based buying was all anyone could talk about. Every week, a team somewhere would launch a new audience-targeting technology, and every agency promoted the idea to its brands as a way to set itself apart.
Today's predictive bubble is eerily reminiscent of that former phenomenon, from which we can learn a lot. During that time, several things were discovered that most likely stand to repeat themselves today as ABM takes B2B marketing by storm.