Case Study: Olvin achieves more accurate store-level foot traffic insights with SafeGraph data

Olvin cuts 1-2 months of data cleanup with SafeGraph for store-level foot traffic insights

Olvin, a retail analytics platform, faced the challenge of providing granular, store-level foot traffic insights to its customers. Their previous data provider's information was inaccurate and "noisy," often misplacing store locations and forcing Olvin to aggregate data to a local area level. This lack of precision made it impossible to build a reliable store visit attribution model, which was a key requirement for their clients.

By implementing data from SafeGraph, specifically its Places and Geometry datasets, Olvin gained access to accurate polygons and clean data that adhered to industry standards. This allowed them to build a more accurate attribution model and provide the precise, store-level insights their customers demanded. As a result, SafeGraph's solution helped Olvin unlock new customer conversations, simplify the user experience, and strengthen its competitive edge by offering predictive foot traffic insights.

Olvin