In the same way in which ad viewability was the hot topic issue these past 18 months, Placed anticipates location accuracy to enter into the headlines. Placed’s research found that on average the accuracy of exchange based locations were on average off by more than 4 city blocks! Additionally only 1% of bid request are accurate enough to identify store visits.
Download Placed’s Accuracy & Bias in Ad Exchange-Derived Location Data White Paper at https://www.placed.com/resources/white-papers/location-accuracy-bias
Excerpts from the white paper:
Growth in location based advertising is tied to continued growth in mobile usage. Current spend projections for location based targeting are estimated to reach almost $30B by 2020. As spend increases, so will expectations around validating the accuracy of both the location data and the subsequent visitation impact
However, there are several well known limitations of exchange-derived location data. First, the
source (e.g., GPS, cell tower, WIFI, IP) and accuracy of a given exchange-derived location data
point is generally unknown without additional validation. Second, given that ad impressions are
served and exchange-derived locations are observed only when the device is in use, there is the
potential for significant measurement bias to exist.
High-level results from the location accuracy analysis include:
- The average accuracy of exchange-derived locations is over 4 New York City blocks.
- After filtering for location accuracy, only 1% of bid requests are useful for in-store measurement (based on a location accuracy < 50 meters).
- 80% of bid requests are made while people are in between visits—and most of the rest are made at home, limiting viable use of the data for determining store visitation or affinity.
Takeaways from the analysis of bias in exchange-driven location data:
- Exchange-derived locations are only present when the device owner is using the phone and browsing an app that serves ads, thus bid stream data over indexes on location data from Lodging, and Gyms & Fitness Centers– likely due to readily available wifi combined with extended time spent at a given business.
- Key retail categories such as Fashion, Professional Services (ex. Staples, OfficeMax), Sporting Goods and Computers & Electronics are under-represented in bid data.
- The skew toward a subset of commercial business categories creates a bias in exchange derived data that requires validation against first party data to ensure corrected weighting.