Why Offline Data Is Key To Online Data Segmentation

Now that advertising is driven by “big data,” marketers are well aware that certain targeting segments can make a positive impact on their campaigns. In ad tech, anyone with an algorithm and a data scientist on their staff can make a few bucks by pulling together some appealing segments and selling them to marketers.

However, not all data segments are created equal. If online marketers and data companies keep trying to plug in the same segments, campaign after campaign, they will see diminishing returns. Targeting the same mom or auto intender segments doesn’t do much good. Marketers need some outside-the-box thinking to uncover new data segments, and the secret may lie in offline marketing tactics.

When thinking about data segments, it’s important to consider the major life changes when consumers end up making lots of purchases. This is how offline direct-response advertising works, hitting consumers with offers when there’s a likelihood of purchase.



Consider mover data, which could be one of the most powerful data segments out there. Have you ever received a mailer from a homegoods retailer when moving? The direct-mail guys have been making a killing off of this data for years, so why aren’t digital marketers?

It should be easy to track. A marketer’s partner can understand which customers are 90, 60, and 30 days out from moving based on publicly available real estate data. These 30-day buckets are effective because they allow a marketer -- say a home-supply store or big-box retailer -- to measure the degree of frequency for serving ads, as well as the creative. 

Home Depot and Lowe's are going to push moving supplies in that 60-day period, while a retailer like IKEA may target urban movers in a shorter 15-day window. Research shows that two-thirds of households that are moving formulate the majority of major-purchase decisions before the move. With a sense of the actual move date, marketers can also use this data to suppress irrelevant ads and audiences. Meanwhile, Best Buy can start serving ads post-move, when it’s time to get a new TV.

This kind of data is applicable across many other verticals that aren’t endemically tied to moving: retail, CPG, banking, insurance, you name it. Take a step back and ask “Why do people move?” It might be for a new job, moving to the suburbs with their spouse, or even to start a family. These life changes often lead to changes in purchasing behavior as well.

Stats show that even when a move event occurs within a short distance, such as within a Zip code, marketers can’t prove purchasing patterns will remain the same. According to a study conducted by Epsilon, brand loyalty gets put to the test during a move, with a mover being twice as likely to change brands or providers than a non-mover. So while an agency planner might not think a pre-mover segment is an endemic or valuable audience to hit, stats show those who move are 372% more likely to switch baby product brands than those who are not moving, as reported by Ipsos. Certainly, your beer-drinking habits may change based on where you lay your head: Sixpoint in Brooklyn, Yuengling in Philadelphia, and Augustiner in Munich. Why wouldn’t other brands change for consumers?

Moving is not simply about cars and mortgages and furniture. Consumers make big electronics purchases after moves: about 55% of moving homeowners purchased at least one major appliance post-move and tend to splurge on themselves more than non-movers. This qualified audience will spend more money on major purchases during the three months surrounding the move (pre- and post-) than non-movers will spend in a five-year period, representing the kind of  opportunities that online marketers dream about.

Creating the most accurate and current data set of “new movers” requires working with offline providers who collect lists of new home sales, using the new phone and utility installs (electric, water, gas, etc.) as validators against the municipal deed filings and address changes that are initiated by the consumer. This data can then be matched to IP addresses and scrubbed of personally identifiable information before being made available to marketers.

For other kinds of data, online partners can organize the offline CRM data into segments, then upload that data to a match partner, based on the segment needs. These onboarded segments can be updated on a weekly basis, just like online behavioral segments, but they’ll be much more highly qualified.

The truth is that offline direct-response data can be a lot better than what we make do with online. It’s not inference-based, but instead real consumer data that is cleaned up, made anonymous, and then made actionable. Marketers and ad tech companies alike should be figuring out the process of onboarding it for online use. In the very near future, we’re going to see the old-school way of targeting consumers becomes the new-school data source in online marketing.

3 comments about "Why Offline Data Is Key To Online Data Segmentation".
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  1. Nate Carter from Mediassociates, July 15, 2014 at 3:03 p.m.

    I would be interested in how accurate an IP match would be as, when a person moves, they sign up for new cable and thus are assigned a new IP address by their broadband provider. So the IP address that the data is being matched to could easily be the movers old IP which was reassigned by the ISP when they moved and terminated their contract. It would seem like a cookie sync, matched to PII and scrubbed would be a more accurate measure.

  2. Joe Smith from Student, July 16, 2014 at 2:11 p.m.

    @Nate: IP addresses are generally dynamically assigned by the ISP. From my own experience as a consumer, I've seen my IP address change, just by resetting my router. So to take your point even further, it's not just the termination of a service contract that changes an end user's IP address, but many ISP users will continue to have their IP address changed overtime.

  3. Kristi Anderson from Scanalytics, Inc., July 17, 2014 at 9:13 a.m.

    At Scanalytics we measure offline consumer analytics through intelligent floor sensors that anonymously monitor foot traffic. This technology helps retailers better understand customer experience and the purchasing process to improve marketing strategies and predict trends for their markets.

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