Turn GPS Data Into Store Visit Intelligence

Learn how to use your GPS location data or IP-address data to determine if a device visited a place, brand, or type of store.

What's In the Store Visit Attribution Whitepaper?

1. Why measuring store visits is so important for online-to-offline attribution

2. Advantages and disadvantages of using store location centroids versus using building footprints (polygons) for store location geofences

3. How to clean GPS data to account for GPS drift, GPS sinks, and jumpy GPS pings

4. How to cluster GPS data using a modified version of the DBSCAN algorithm

5. How to derive store visits by matching GPS clusters to store locations using a learning-to-rank machine learning model

Why SafeGraph's Building Footprints & IP-Address Geolocation Data Is Key For Store Location Context

Polygons BEaT CENTROIDS
The Power Of Polygons
Having a detailed understanding of where Points of Interest (POI) are located is crucial for store visit attribution. This is why we originally made SafeGraph Places, our POI dataset with building footprints (polygons) for 5 million places in the U.S.

Download the whitepaper to learn about why store polygons rather than store centroids are better for accurately measuring store footfall from location data.
NO GPS LOCATION DATA? NO PROBLEM.
IP-To-Place To The Rescue
Thanks to IP-to-Place, it's now possible to do online-to-offline attribution without location data.  IP-to-Place maps a WiFi hotspot IP-address range to a POI for more than 1.8 million places. Simply check if your IP-address data matches an entry in our dataset to connect digital actions to a store visit conversion.

Download the white paper to see how IP-to-Place compares to other visit attribution approaches.

Improve Your Online-to-Offline Attribution

Download our technical whitepaper for more information on creating a store visit attribution solution or reach out for help.