“Data-Driven Thinking” is written by members of the media community and contains fresh ideas on the digital revolution in media.
Today’s column is written by Lauren Moores, vice president of analytics at Dstillery.
The use of location data as a signal for content messaging, user functionality and context targeting continues to grow. At the same time, as we have seen with other emerging data streams, with more signal comes more noise. The likelihood of using inaccurate or even fraudulent location signals is increasing.
As with any of the digital signals we harness, location data, whether it’s from mobile phones, tablets or wearables, is only as good as its origin and classification. Don’t ever forget the importance of placeability, which is the accuracy of the location data we’re receiving.
Think about your own experience using applications that rely on location to provide content or functionality. On a recent road trip, for example, I found that as often as not at least one of my map apps could not accurately determine my current location or reverted back to a location from hours before. Similarly, how many times have you used Uber or Hailo and had to manually adjust the pin to ensure a pickup within a few feet and not blocks away?
Location accuracy can vary significantly. ThinkNear estimated that 19% of location-based ad inventory available through mobile ad calls is more than six miles off target. Ouch. We have GPS, WiFi or cell tower triangulation, reverse IP geocoding and registration data as sources to determine device locations. Sensor positioning technologies like iBeacon are used indoors. For external locations, both Google and Apple use a hybrid of GPS, cell tower and Wi-Fi data after originally using Skyhook before building their own.
Here’s a quick primer on each data type:
Cell tower triangulation: Relies on the density of cell phone towers and the response of at least three tower pings to be able to pinpoint a device.
iBeacon: Opt-in by the user; built into the app by developers; broadcasts where the device is approximately once per second and is not considered as accurate as other sensors.
GPS tracking: Opt-in through an app; it is likely the most accurate but limited if the device is not turned on or “sight lines” are obstructed, which more likely happens indoors.
Registration data: Uses the address that a user has given, most likely a zip code, which is then translated to a location centroid.
Reverse IP geocoding: Takes an IP address and chooses a nearby latitude and longitude coordinate. This type of identifier is fraught with error.
Wi-Fi geolocation: Uses the location services layer built into the phone operating system. When enabled, it looks for Wi-Fi networks and logs locations in a Wi-Fi database.
Wi-Fi sensors: Similar to Wi-Fi positioning for external locations, but it also incorporates the use of floor plans and measurement of how a device moves through an indoor area.
Given the increasing value placed on location information by publishers and marketers, there is a growing sub-industry in spoofing locations. Spoof location apps exist for users to download. Less sinister but equally disruptive, some apps hardcode randomized locations so that they qualify for a premium data category. In some cases, more than half of mobile bid requests can be considered suspicious.
With the growth of location as a means to provide services and advertising to the right person at the right time, we need to be aware of placeability. It is essential that we examine the data we use for audience and targeting so that we can avoid wasted ad impressions.
Stay tuned to this arena. The growth of mobile data and media will put placeability on par with viewability and bots as a challenge the industry needs to overcome.