Knowing a consumer’s real-time and historical location is only half of the equation for mobile advertising. Gaining any type of intelligence about that consumer – from building audience segments to simply using a geofence to trigger a proximity ad – also requires good point of interest (“POI”) data, the description of the physical context of the user.

POI data may not be as sexy or creepy as locating a user via their phone, but it is equally, if not more, important for the mobile-location advertising ecosystem.

A POI data set is a hyper-correct data representation of the physical world. There are as many pitfalls associated with this type of data as there are with user location data (see Challenging misconceptions in location data science). Get it wrong, and you risk sending a consumer to a place that does not exist, adding her into the wrong audience segment or even crediting a consumer as visiting a store for attribution purposes when she did not.

Moreover, bad POI data could lead to delivering an ad in the wrong place, irrelevant for the consumer, and charging the advertiser for doing it.

In this second article in our series, we will dive into why POI data is important, the challenges of using POI data, and how this data can be effectively collected and normalized for use in mobile ad campaigns.

Why care about POI?
Perhaps the best way to illustrate the importance of accurate POI data is with a specific example.

Appliance company Electrolux wanted to target rich-media mobile ads to drive store visits and sales for Frigidaire Professional products. Accurate POI data enabled a number of key components in this campaign.

First, highly accurate POI data ensured that all the dealer locations used in the campaign were correct, making it possible to create geofences around those stores and trigger ads only when a consumer was found in a geofence around the store.

The accurate POI data also ensured that the correct address for the closest store appeared dynamically in the ad unit, enabling a consumer to easily navigate to the store.

Lastly, it ensured that additional store data, such as hours and available inventory, were correct.

These same geofences around the store POI were then used to construct audience segments of likely shoppers.

Other geofences around locations selling high-end appliances created additional segments of in-market shoppers. And, tight geofences around specific dealer locations were used to measure the lift in foot traffic for consumers exposed to the advertising versus a control group.

All of these activities are built on a foundation of accurate, POI data.

In sum, accurate POI data made it possible for Electrolux to target relevant consumers at the appropriate time and place with no inaccuracies, while also allowing the company to collect valuable data about its customers’ behavior in the physical world and about the impact of their campaign.

POI challenges
Like many types of specialty data, working with POI data creates its own unique challenges.

To start, a single building can contain multiple business locations. Think of a large office building that may have multiple businesses on each and every floor. Figuring out which specific business a consumer has visited is not easy.

There may also be multiple semantic references to a single place.

For example, 1 Bryant Park = 1111 Avenue of the Americas = The Bank of America Building = The Starbucks on 42nd and 6th.

In addition, businesses are always evolving. Locations open, close, move or change identifiers, such as phone numbers or owners. And, of course, you cannot rule out human error, and incorrect POI data entry can lead to inaccurate or transposed POI data.

In my experience, a hefty 20 percent to 40 percent of any dataset about places is wrong, even when a retailer itself sends you a list of its store locations.

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