Converging Online And Offline Data

The language and the guiding frameworks used by online and offline marketers -- one structured around transactional data, the other steeped in DMAs and demographics -- have until now remained distinct and largely incomprehensible to one another. In a world where online/offline fusion is no longer speculation but daily fact, traditional and digital teams need to learn to speak a common language, as Scott Knoll, senior vice president of display, Datran Media, explains below.

Behavioral Insider: Datran Media comes from a different tradition from most behaviorally oriented marketing companies. How do you distinguish what you're doing within the wider context of the behavioral space?

Scott Knoll: Our goal is to allow marketers to use accurate, verified household demographic data and online response data.  The way I look at it, the conventional practice of online behavioral targeting tracks where people go.  Many companies understand that once they have that data they still need to figure out what it means. They currently do that by inference, which is to say guessing. You draw a circle around all the behavioral targeting companies and there are some great ones, but they are still limited to response data. They are just able to guess about what patterns underlie that data, but have no insights into the audience makeup. At most it's buttressed by limited survey data or self-reported information.



We start with millions of transactions a year and take that data to understand what interests are being expressed by that. Direct mail has spent decades aggregating information on the household level. Their problem was just the opposite. Once they sent the mail out and it got delivered, they had no idea if it was ever opened or read. So what we're setting out to do is take household demographic data and online response data and work them together.

BI: So how is this offline data generated and then deployed online?

Knoll: We provide consumers a clear notice about how we intend to use their data and an opportunity to opt out of our targeting. We drop a cookie on an email open, which provides us with household demographic data such as age and family size. Once the cookie is released it lives in the online world and cannot ever be tied back to an email address. We have worked extensively with our Chief Privacy Officer to make sure the rich data on cookies is without any personally identifiable information.

BI: What kinds of benefits can this yield that transactional data can't?

Knoll: The exciting prospect is that we can give a report on the precise demographics behind online response. We can drill down on who really saw it and who clicked. For instance if an ad campaign is focused on reaching males 25 to 39 and ends up getting stronger response among females 18 to 34, that's critical information.

One thing that means is that [it attracts] advertisers who have held millions of dollars on the sidelines because they don't actually sell products online. Up till now they've known their audience is online and that they needed to follow them but they have lacked any reliable means of tracking how effectively their online media is at reaching the right people.

BI: Is your targeting offered under the aegis of an ad network? 

Knoll: Our model is similar to that of other ad networks in that we have our own sites and users. What's different is that we know far more about them. We can report where responses are coming from within the framework that marketers natively think in: by DMAs, age and income. If you talk to advertisers, they think in terms of reaching, for instance, mothers with kids between 2 and 12 who have higher-end incomes of over $70,00 a year. That's the language they use. Until now there's been no way of translating back and forth between transactional data and demographic data points in a consistent way.

Recently I was at a big conference and the hot topic of conversation on and off the panels was that marketers can measure clicks, but they still have no way to consistently report on audiences. If you look at how demographic information is described by online publishers, it's all over the map.

BI: What do you see as the next iterations for your platform?

Knoll: We see what we're doing as just the starting point. Using new data warehousing and software tools, the next generation of reporting will pull in behavioral data and segment ad responses by lifestyle categories and psychographic interests. On the horizon also is the ability to further customize reporting criteria. For instance, if an advertiser wants to see specifically how they're doing with online responsiveness with people who have three kids, we need a platform that can report that out.

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