The Problem: Accurate Location-Based Audiences

How can Media Storm turn noisy geolocation data into precisely defined location-based audiences?

The Problem Solver: Media Storm

Media Storm is the 2nd largest independent full-service media agency in the US with deep roots in the entertainment, retail and the travel/tourism industries. Today with over 35 clients across key categories, Media Storm remains an agency centered around national and regional brands that transact at the hyper-local/community level.

Media Storm currently works with leading companies such as AMC, Big Lots!, Celebrity Cruises, WGN America and WE tv across services including media planning, audience segmentation, and digital and TV media buying. Through their data science and advanced analytics group, JubaPlus, they deliver true business ROI – not just media results.

The Challenge: Precise Store Location Data

Media Storm, on behalf of its clients, was tasked with running advertising campaigns to re-engage store visitors and turn them into repeat customers. To create these audiences of store visitors, Media Storm licensed a data feed of anonymous GPS locations tied to Mobile Advertising IDs (MAIDs). However, Media Storm found it challenging to determine from geolocation data whether a MAID had visited a store without accurate data on where stores were precisely located.  

Working with brands to source the store location data proved to be insufficient due to imprecise client data. For example, one big-box retailer client of Media Storm’s often reported centroids of stores being located more than 50 meters off from their true locations, leading to the creation of inaccurate audiences.

Clients also had the challenge of either having limited or no data on where competitor stores were located. This prevented Media Storm from advertising to store visitors of competitor brands for conquesting campaigns.

Media Storm found that even perfect store centroid data would fall short in creating accurate audiences in dense environments like strip malls or cities. In these situations, merely drawing a radius around a store centroid would include visitors to nearby stores or pedestrians outside the storefront. Without exact building footprints for the stores, Media Storm’s audiences would include irrelevant MAIDs resulting in wasted ad-spend.

Enter SafeGraph's POI Data & Geofences

Media Storm realized that in order to create accurate store visitor audiences from anonymous GPS data it needed an in-house dataset of where stores are exactly located. Media Storm chose SafeGraph Places, a dataset of over 10 million points of interest with business listing information, for this purpose.  

Using SafeGraph’s brand information and NAICS code (category) for a place, Media Storm was able to quickly identify store locations of its clients and those of its clients’ competitors.  For each of these POI, Media Storm used the polygon (building footprint) to geofence the anonymous GPS data feed and find which MAIDs visited a store. This helped Media Storm access all the actual visitors to the store location while filtering out nearby but irrelevant MAIDs.

Media Storm used the more accurate store visitor audiences to drive efficient advertising campaigns which in turn, led to superior performance for Media Storm’s clients.

Future Plans: Store Visit Attribution

Media Storm plans to use SafeGraph Places for store visit attribution. The plan is to create A/B tests of MAIDs exposed and not exposed to their paid media campaigns. Then, from the geolocation feed and SafeGraph polygons, they plan to measure the store visitation rates for the test vs. control group of MAIDs to measure the effect of the advertising campaign on store visit lift.