Behavioral targeting is emerging as a standard expectation for ad targeting and dynamic content serving on the Web. Behavioral targeting promises improved media efficiency and the ability to identify and zero-in on those with a higher propensity to buy.
Yet in this evolving field, there are not yet clear understandings about what inferences can properly or accurately be drawn from demonstrated behavior. Other than the individual who clicks through to a completed sale, most behavioral targeting at this point is guess work.
Consider several approaches for evaluating or scoring behavior.
Logically someone who does the same thing or visits the same place repeatedly is probably more interested than the average Joe. Assuming that most people only make one or two clicks in error, it is reasonable to guess that someone returning for a 3rd click is probably interested, if not a real buyer.
My wife is a prime example. She visits future purchases frequently before buying. To her, multiple visits to the shoe store, the furniture store or the big box retailer to hover over her intended item are no big deal. In fact she enjoys visiting and revisiting.
The repetition confirms her interest or ratchets up her intent and her commitment to the purchase. The same holds true on the Web. She will click and click again on an item. Perhaps she’ll visit it at multiple sites in search of greater product detail, to compare prices or to discover a deal on shipping or a favorable return policy.
If we were to track her behavior with a cookie or some other technology, the question would be how many visits signal her intent? Should we dynamically serve her content or intervene by popping up an offer on her third or fourth visit? How do we know how much repetition is sufficient to encourage her to convert or at what point would we freak her out with a big brother intervention?
Perhaps if we watched where she went before and after visiting the product, we might get a better idea. If she visits the same product at a competitor site, does that signal intensity of interest or intent? If she looks at a similar product or a product that goes together with the first product can we infer a pending purchase?
What about if she makes a beeline for something? Would that be enough evidence to treat her differently from the great mass of Web surfers? Say she responded to an email and clicked on a designated landing page or navigated from the home page to a particular product page in the shortest possible sequence (3 clicks?), would that mark her as an A prospect and separate her from the herd?
How about if she fills in a form or clicks a “contact me” button? Assuming that only X percent click these frivolously or because they are lonely shut-ins, would using the expected response device qualify her as a hot prospect or merely mark her as a tire kicker?
Most direct marketers will tell you that even among those who answer the call to action and utilize the provided response mechanisms; most responders are generally interested but not ready-to-buy. So the act of responding, while rarely more than 2 percent of those exposed to an offer, still doesn’t turn you into a qualified, hot lead.
So what’s a marketer to do?
Charged with generating demand, we want to do so in the fastest most cost effective manner. Accepting the notion that actions speak louder than words we buy into the notion of behavioral marketing. But how do we draw the right inferences from the behavior we observe?
The short answer is we test and learn. It’s a classic way, but it is limited because we cannot project our learning across product sets or categories. Another approach is to aggregate the varying dimensions and triangulate purchase intent and intensity. This might work for larger value items and in B2B marketing where the shopping cycle is longer and more complex. The third answer to is share data and get collectively smarter at reading these digital tea leaves.
If behavioral targeting has value, it must be to help us sell more things faster to those most likely to buy. Otherwise it is just digital voyeurism.
Danny Flamberg is Presdent of Prescients, LLC, a marketing consultancy.
[tags]danny flamberg,behavioral targeting,web marketing,business psychology[/tags]