Some providers of B2B predictive analytics solutions are describing the benefits of their technologies in rather effusive ways. Consider, for example, the following language in a content resource from a leading PA vendor:
“Imagine a world where you can find buyers early in the sales cycle and predict who your next customer will be with 85% accuracy. [XXX’s] predictive intelligence engine gives you the ability to see your entire universe of potential buyers at every stage of their buying journey. We uncover net-new, in-market prospects based on powerful data science and billions of time-sensitive intent interactions.”
This is heady stuff because the ability to know which prospects are engaged in an active buying process could enable fundamental changes in the practice of B2B marketing. For example, suppose that your company uses account-based marketing. With predictive analytics, you could select ABM target accounts based on both fit (how closely a prospect resembles your best existing customers) and interest (whether a prospect is “in-market”). You could also use your PA solution to frequently update your list of target accounts, so that you have a near real-time view of which accounts are engaged in an active buying process.
This sounds like marketing nirvana, right? When you know which of your prospects are actively in-market, you can focus your marketing programs on this “low-hanging fruit,” which should result in higher conversion rates, greater marketing efficiency, and lower customer acquisition costs.
There Be Dragons Here
Focusing marketing efforts on in-market prospects has undeniable benefits, but this strategy also carries some less obvious hazards. If taken to the extreme, it can lead marketers to ignore prospects who don’t make the “in-market” cut. This is a dangerous approach because of changes in how business decision makers consume information.
A B2B buying process usually begins when a company’s leaders or managers recognize a need or a problem, and decide to do something about it. These “buyers” then gather information about the need or problem, evaluate possible solutions, and may or may not decide to buy a product or service to address the problem or need.
So, our traditional view of buyer behavior is that most information gathering and learning occurs after an intentional buying process is underway. Today, however, information is so readily available that many business leaders and managers routinely consume information about business issues long before they’ve formed anything close to “buying intent.” I’ve used the term casual learning to describe learning and information-gathering activities that occur before an intentional buying process has started, and it’s clear that this type of “low-intensity” learning is becoming more and more prevalent.
What marketers need to remember is that casual learners will form impressions and embryonic preferences based on the content they consume, and that those impressions and preferences will remain influential when they get involved in an actual buying process. Therefore, marketing to casual learners is important, even though most casual learners probably shouldn’t be characterized as “in-market.”
As predictive analytics solutions get better and better at identifying companies that are in an active buying process, it will be very tempting for marketers to focus more and more of their marketing efforts on active buyers. That’s not a bad strategy, so long as you remember that you must continue marketing to prospects who aren’t currently “in-market.”
There’s nothing wrong with harvesting the low-hanging fruit, so long as you continue to tend the immature fruit that’s higher on the tree.
Illustration courtesy of Andreas Fischler via Flickr CC.