Report argues location data is key to better mobile programmatic outcomes says pre-packaged audience segments have significant limitations.

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Location is a critical tool to personalize programmatic advertising, according to a new report (registration required) from Mobile-location data reveals user behavior, brand preferences and purchase intent. When combined with programmatic buying, these signals can yield better performance and insights at scale, the report found.

Though more than 75 percent of programmatic ad buying is soon expected to go to mobile devices, according to eMarketer, the report found that marketers are not yet fully utilizing the available benefits of location data in programmatic media.

For multi-location brands and the agencies that service them. reports that last year location-based mobile targeting “drove an average 391 percent increase in incremental physical site visits” for its customers. The company adds that the advertisers in the strongest position to benefit from a localized programmatic are “multi-location brands, agencies who service them, and local media groups seeking to localize audiences for national campaigns as well as companies who want to achieve performance on a high volume of localized campaigns.”

The report spends a good deal of time discussing the differences between pre-packaged audiences and structured data and unstructured data, which it says is critical to “unlocking localized programmatic’s potential.” Most data in programmatic is structured and represents “a digital black box.” It also offers the opposite of personalization, argues.

“Everyone in [the] segment is, in essence, the same person, with the same motives, interests, and character traits as their segmented counterparts.” Problems with pre-packaged segments include “unknown data sources, invisible data, limited insights into recency of intent or action, and that optimization can only occur at the segment level.” Unstructured data, by comparison is more flexible and reliable according to the report and offers more targeting and personalization possibilities.

Location-based personalization. Examples of location-driven personalization include:

  • Targeting customers who have previously visited a particular retailer
  • Geo-fencing competitors’ locations to build unique audience segments “who have already expressed interest in a similar product or service.”
  • Ability to target customers who’ve visited “specific locations of interest relevant to [the] product or service or are within a predetermined physical proximity to [the] business.”
  • Reaching audiences that have attended specific events
  • Targeting households/businesses at the address-level with OTT/CTV video

All this can be combined with store-visitation attribution. also promotes the concept of “addressable geo-fencing” as a complement to addressable TV and direct mail.

Keyword performance varies by location. says its campaign data shows significant variation in the performance of keywords by location, which suggests the need for a localized programmatic solution. In the QSR category, for example, “the best keyword in terms of national performance is ‘restaurants’ with nearly double the amount of impressions of the next keyword . . . With the state data included, suddenly ‘kids restaurants’ takes the top placement . . . California alone makes up almost half of all demand for ‘kids restaurants . . . Virginia is the top state for searching ‘sandwiches.’

Accordingly, keyword strategies that may succeed in one place may not work in another. argues that localized programmatic allows marketers to adapt to regional and local differences at scale.

Why you should care. There’s a separate discussion about the accuracy of some of the location data used in programmatic. However in theory the report is accurate: location data offers behavioral insights and preferences in real-time that can be optimized against real-world conversion metrics and yield greater personalization and performance improvements.

There are numerous location-data providers in the market, whose claims and capabilities sound very similar. Marketers should do in-depth due diligence before selecting a partner.

Contributing authors are invited to create content for MarTech and are chosen for their expertise and contribution to the martech community. Our contributors work under the oversight of the editorial staff and contributions are checked for quality and relevance to our readers. The opinions they express are their own.

About the author

Greg Sterling
Greg Sterling is a Contributing Editor to Search Engine Land, a member of the programming team for SMX events and the VP, Market Insights at Uberall.

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