Feeding the Funnel with High Quality Prospects

When it comes to the Internet, we’re all “hand-raisers.” We conduct searches to explore products and services that we may or may not purchase. We browse content because we’re interested in sports, automobiles, antiques, whatever. And we visit Web sites for myriad reasons that might—just might—result in a commercial transaction at some point in the future.

While we are taking all of these actions, marketers are making inferences as to our intent and targeting us with ads in hopes of making a sale.  These bottom funnel behavioral techniques are an essential tactic to scoop up the low hanging fruit. However, for all of its strengths, behavioral targeting is not very effective at differentiating between high value prospects and lower value ones. Moreover, it only works on the limited set of prospects that are showing behavioral signals. What about the millions of high quality prospects further up the proverbial marketing funnel? To focus advertising dollars on high value prospects earlier in the consideration cycle, one needs to look beyond behavioral indicators.

Marketers know the profile of their high value, profitable customers. So how do you find and target new consumer prospects that will yield the most lifetime value, even before they raise their hand?  By creating rich “lookalike” profiles of high-value existing customers, taking advantage of the vast pool of offline third party data available today.

Learn more about lookalike audiences in this comprehensive guide to audience expansion.

Take the online higher education market as an example. Many people of all ages consider taking online courses to pursue an advanced degree or to reinvent themselves after a layoff. Plenty of them will use a search engine or visit a web site and fill out a form seeking information about course offerings, tuition rates and procedures. And, yes, some will even sign on the dotted line and enroll in a program. However, the savviest online higher ed marketers are learning that the shortest route to their truly best prospects begins with a deep data dive on their most successful current students. This exercise can be performed by an outside data specialist, one who has access to many different data sources that can crisply define the traits of a successful, high value student.  By combining the power of many data sets into a single audience model, this process can identify millions of prospects that are more likely to enroll and successfully complete their degree program.

This custom-built audience, derived from the marketer’s own first-party (CRM) data, can be reached using relatively inexpensive real-time bidding (RTB) media inventory. Only consumers who match the high-value custom profile are served display ads, greatly reducing wasted ad impressions. Extending the example to life insurance produces the same result. Life insurers don’t want to serve an ad for a $1 million policy to a prospect who can afford just $100,000 worth of coverage. And they would prefer not to serve an ad offering $100,000 worth of coverage to someone who can afford $1 million. The look-alike profiles created by comparing the insurer’s existing $1 million policy holders to offline data will unearth millions of people who could very well need and afford a $1 million policy. The result of such precise audience segmentation is a scalable and cost-effective way to leverage display advertising.

Don’t get me wrong. Behavioral targeting and high-value profile audience targeting are very complementary. Profile audience targeting influences the right prospects earlier in their consideration cycle and behavioral targeting helps seal the deal at the bottom of the funnel. The beauty of high-value audience targeting is that marketers can reach highly profitable prospects before the competition. In order for the bottom funnel to grow, you have to feed the top. Just make sure that you’re feeding the upper funnel with your highest value prospects.

David Dowhan

About David Dowhan

As CEO and founder of TruSignal, David leads business strategy and product development efforts and is keen to create innovative, customer-centric solutions using data and analytics.
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