Beware of False Location Data

Marketers, some technology vendors, and organizations such as the Location Based Marketing Association point to the benefits of being able to connect a prospect with a nearby business, using a combination of location information, the prospect’s own data, and marketing information to push relevant offers.

In the United States, location-targeted mobile ad revenues are projected to grow from $4.3 billion in 2014 to $18.2 billion in 2019, a 33.5 compound annual growth, according to BIA Kelsey's Local Media Forecast 2015. Due to the increase in demand for location data, combined with the lack of standards around how this data is controlled and used, there is a risk of it becoming compromised.

Some 60 percent of ad requests contain some form of location data, but of those requests, less than one third are accurate within 100 to 500 meters of the stated location, according to PlaceIQ, a provider of location intelligence. So marketers need to determine how trustworthy the location data is, and build that confidence into their strategy. The more confidence a marketer has in location data, after all, the more he can use that information to push offers.

And confidence in the data's accuracy is only part of the picture. “You have to be able to understand location,” says PlaceIQ's CEO and founder, Duncan McCall.

McCall notes, for instance, that location information from many urban settings is not as useful as location information from more isolated settings. A city building may contain a coffee shop or a quick service restaurant. While the prospect’s smartphone GPS will indicate she is at or near the building, it cannot define if she is at a specified location within the building. If she is merely walking by, she may have no intention of shopping at that particular location. If, on the other hand, the coffee shop or restaurant is in an isolated location without surrounding businesses, the marketer can have a much higher degree of confidence that that business is the prospect’s planned destination.

Marketers also need to consider whether the location data represents normal consumer activity, according to McCall. For example, if the location-based data shows that the prospect has been in the same location for several days or even for several hours, the data cannot be trusted. Similarly, if the location-based data shows the prospect “jumping around” from place to place in a way that would be impossible or highly unlikely in typical human movement, the data should be deemed unreliable.

Other times false location data is the result of technology misinterpreting ZIP code data, according to McCall. Some applications translate ZIP code data into latitudinal and longitudinal data. While the former data places a prospect in the ZIP code, if an application translates that data into latitude and longitude, it will place the prospect in the middle of the ZIP code. With ZIP codes in central business districts, that difference will be miniscule because the ZIP regions are fairly small. But in more rural locations, the regions are much larger, so any ZIP code data translated to latitude and longitude will be much more inaccurate.

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