Beyond Pavlov's Dog: Customer-centric Targeting

As it's often described and understood, behavioral targeting models the consumer as a passive "prey" to be "captured" by its advertiser captor. The challenge for advertisers in such a model becomes, Pavlov's dog style, to fashion the right stimulus to provoke the right conditioned response. Against this model, t:m interactive founder Jim Hering argues for a more customer-oriented view of the targeting transaction, one in which the role of advertisers becomes to use analytics to learn more about what customers want.

Behavioral Insider: Could you discuss how your approach to behavioral targeting has evolved?

Hering: For the most part the vast majority of people describe behavioral targeting as tracking consumers based on a particular kind of trackable behavior and hitting them with your advertising messages where you will hopefully provoke a response. That's one way of looking at it. But we think that's working backwards, by focusing on the advertisement before the customer is targeted by the advertising.



BI: So the targeting is more reactive to a rigid pre-set formula than actually learning from customer behavior?

Hering: Well, it misses a much bigger opportunity to create new touch-points between a brand and consumers by learning what information THEY are looking for . Behavioral targeting can, and ideally should, be a way of engaging a customer positively. And if you realize that targeting is about more than a one-time response and is about brand equity. Once you realize your brand is at stake, the imperative becomes giving consumers information they want. To do that, you have to locate where their interests and your message coincide--what we call the trigger points.

BI: How does that concern change or influence methodology?

Hering: The method for doing that will vary from client to client, but the main thing is to identify what we call the main transaction triggers that connect online behaviors with your message. The point is, there's nothing mechanical about this. There are many different kinds of online behavior, and it takes research and knowledge of your customers to know which information and what pattern of behaviors are going to be relevant.

Each behavioral target parameter is different, and the difference between truly strategic behavior targeting and random targeting is in matching target focus to goals and to measurement methodology.

BI: What role do or should publishers and ad networks have in this process?

Hering: The key to doing this right lies with publishers or networks. Technology is important, but you ultimately have to work with the assumption that publishers know their readers or their audience better than anyone. They've got to know how to use their anonymous customer data and make it possible for advertisers to do so in creative ways. They've got to make it possible to optimize less used or undervalued inventory.

The goal from an advertiser point of view is to maximize use of inventory. As category relevancy is more and more effectively targeted, and publishers sell out all of their contextually focused spots in areas like auto and travel, the challenge then comes to be how to get other pages. The smartest publishers are able to use targeting to differentiate ways advertisers can optimize inventory. That's what justifies premium CPMs.

BI: How do you gauge effectiveness?

Hering: Measurement is the friend of behavioral targeting. At first advertisers hesitated to pay higher CPMs, but soon, pleasantly, they've found that the expense was more than justified by net ROIs....

There's an emerging body of best practices and case studies. They make it clear that done right, behavioral can add enormous efficiency to traditional forms of targeting like demographics and contextual. For example, a large airline we've worked with has long advertised on financial sites to try to target business travelers, but through a partnership with the Wall Street Journal online,[the airline was] able to target specific advertisers to online Journal subscribers based on their site usage characteristics such as regular visits to travel columns which identified them as strong business travelers. Messages could then be framed for this select group of serious travelers that built on how they are already targeted in terms of demography and contextually....

We've seen clear benefits in a number of metrics from overall audience composition reached, to brand metrics, message association, cost per target audience, and cost for conversion.

Next story loading loading..