Targeting And Life Stages

Most current approaches to behavioral targeting put all or nearly all their emphasis on what people are looking at and for online. While that’s an important first step, truly customized granular targeting involves going deeper, learning new ways of asking and answering the question of why and how specific consumer segments differ in their search behavior, as Susie Kang, senior vice president of WhitePages.com, explains below.

Behavioral Insider: What distinguishes your approach to leveraging behavioral data?

Susie Kang:The biggest misconception I see in the behavioral field right now is that if you have people who search for autos, all you need to know about them is that they’re looking for a car. That only tells you so much. The more important question, once you know that, is why they are looking for a car. Because if you know more about the specific segments within the wide universe of people who are searching for cars, you’ll know a lot more about how to target them, what kinds of cars they’re interested in, how often to deliver them, and exactly where else to deliver them. To do that you need to understand their auto searching in a wider context.

BI: How can you do that?

Kang: Well, one of the less-understood and -appreciated facts about online targeting in general, and behavioral in particular, is that behind site user activity data there’s a rich array of consumer data in third-party research which can be overlaid with your own data on user online behavior [to] yield whole new levels of understanding.

One treasure trove for us is NielsenNetRatings. What Nielsen has done is study how people in different demographic ‘life stages,’ as they call them, actually have very unique group patterns of online behavior based on where they are in their lives.

BI: Can you explain a little more about what this life-stage research entails and how it gets applied? How does demographic typology connect to actual behavior?

Kang: We have over 15 million business search categories, so we have a huge aggregate of vertical search data. What we’ve done is run that data against the Nielsen profiles and grouped them into separate categories. And indeed there are powerful correlations. One interesting nugget is that people on one of their life stage segments, called ‘New Families’ -- that’s couples having their first kids -- are far more likely to run searches for automobiles than those in other groups.

BI: That’s an intriguing connection, but what concretely might it enable an advertiser to do as far as enhancing their targeting?

Kang: Once you’ve identified a strong correlation between certain search activity patterns and a life-stage segment you can much more readily target them. Take the ‘New Family’ example I just mentioned. Once you know ‘New Family’ searchers are heavy automobile buyers and likely to be in market for a family car like a minivan, keyword searches like say ‘Baby Stroller’ or ‘Toy Store’ or ‘Childrearing books’ take on a very new meaning for an auto marketer. That line of inference can be extended further. Knowing not only that someone is looking for autos but that their search patterns are discernibly ‘New Family’ an insurance company can not just advertise them its car insurance but maybe life insurance as well. Or a financial company that might advertise their auto loan can also target ‘New Family’ searchers with a first home buyer mortgage package.

BI: Have or do you see implications for applying this approach beyond this specific cohort?

Kang: We are correlating our data with several of Nielsen’s broad life-stage categories. The groups like ‘Empty Nesters,’ people with fully grown children away at college, or ‘Mature Families,’ people with several children, have their own very different online search patterns for advertisers to identify and target. The key takeaway is that once you’ve identified a strong behavior pattern by segment, we can enable advertisers to reach consumers in the target group while they are in the directly relevant search category or to retarget them in search categories with established strong correlations with their life stage needs.

BI: Do you see increased segmentation as opening up a wider market for behavioral targeting among your advertisers or others who’ve avoided BT?

Kang: One of the biggest challenges for behavioral targeting going forward is to really get the adoption curve going. Because behavioral ads cost more than run-of-network, there’s a conception among many advertisers that doing behavioral targeting means taking out a big bet. But the fact is that the more tightly you can segment specific behavioral patterns by demographic, the more effective smaller-focused buys will be. Once you go beyond thinking that if you’re doing behavioral targeting you have to go after everybody who browses your product category, you can take targeting challenges on in a more granular way.

BI: Are there any other new developments that seem important to you looking ahead over the next several months?

Kang: We see what we’re doing with life-stage segmentation as a starting point. Looking ahead, we think that there are all sorts of ways to potentially better leverage aggregated data by segment behavior. For instance we are working with ideas for identifying and targeting specific geographical segments based on unique behavioral patterns. The same thing can potentially be done by drilling down into behavioral specifics of different segments of daypart searchers. So there seems to be no end of ways to segment behaviors, provided you learn how to look in fresh ways at the aggregated data you have.