We’ve been asking some of the industry’s most knowledgable boffins to break down jargon to help you through those confusing meetings and indecipherable conferences. Here, Blis’ Tom Gregory explains how geolocation targeting works.
If you think you know what geolocation is, you probably don’t.
Put simply, it’s using data from smart devices to accurately identify and target audiences for advertisers, based on patterns of behaviour.
When people first hit on the idea of harnessing this data from signals emitted by the billions of smart devices used every day – think wifi, bluetooth and carrier signals – they began to aggregate this information to deliver relevant messages to those people based on their location.
This is what we now call geofencing and location-based advertising. Walking past your favourite fast food restaurant? Great, let me serve you a targeted burger promo ad and everyone is happy right?
The truth is most early geolocation targeting was bad – more art than science. It completely underestimated the true power of the sophisticated data devices were able to capture.
Fortunately we now live in more enlightened times, and by applying the filter of smart analytics we can now do a lot more with this information than ping you with a coupon for a dollar off your next burger.
The term geolocation has greatly expanded to describe the power of real-time and historical behaviour data sets based on movement, rather than location.
Put simply, we’ve moved beyond in-the-moment geofencing, and can now use this data to predict what consumers will want and how they will act, based on the places they have been. This gives us an in-depth understanding of intent to purchase and act.
How is the industry applying it?
Our industry has long been enchanted with data, and in turn the promise of better targeting, less wastage and increased and demonstrable return for our marketing dollars. But the rise of DMPs and DSPs in the early part of this decade suddenly overwhelmed marketers with a tsunami of data points they had no idea how to use.
Geolocation is just one tool in this armoury, and a great way to ensure you are delivering the right message to the right person in the right place or a specific moment.
It relies heavily upon mobile, a channel at the very heart of most marketing strategies. But it’s not limited to mobile. Now we can link a mobile device that has been identified using geolocation data to the digital persona of other devices including desktop, a second smartphone, a tablet or connected TV, presenting brands the opportunity to target audiences across multiple devices and touchpoints.
This is done by mapping what is known as the Advertiser ID (commonly but mistakenly called DeviceID) to other devices using a device graph, which recent developments in cookie blocking in Apple and Google browsers this becomes a very useful unique qualifier.
The final piece of the puzzle is attribution – the power to prove a campaign has influenced or changed customer behaviour which is crucial to any modern marketing campaign. Combining location insights with complementary data points offers a way to reliably demonstrate not just intention, but actual behaviours as consumers move between real world businesses.
What are the misconceptions?
The first, and often quoted misconception is that geolocation data can only be used for hyper-local location targeting – classic geofencing we mentioned earlier. The truth is that this sophisticated data is actually part of a larger picture to help generate accurate and useful audience insights. It’s not a silver bullet on its own, but a huge help when added to other data points and is far more effective than traditional demographic parameters.
To be able to effectively filter and remove inaccurate location signals you need to gather lots of data. In this case, the more data from different sources you have, the better.
Causes of inaccurate location data can be wide and varied, it’s not just fraudulent activity. It can be technical limitations or errors in the app software development kit (SDK), some publishers and SSPs shorten lat/longs rendering them useless for accurate geolocation services, or it can be how the app is sourcing location data.
GPS is accepted as the most accurate location data source – the app is accessing the device’s GPS operating system for lat/long data.
And as technology advances there are new variables to consider every day, which become evident the more data we process. In some ways it’s a game of cat and mouse, being able to identify new causes/types of inaccurate location data as apps evolve by looking to increase inventory yield by adding location data to their inventory.
Filtering geo-location data is the process of testing and rating the quality or accuracy of the data passed. This is what technology, does and as important as the technology are the people managing the technology who set the threshold of what quality they will accept as accurate.
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