How Data Scientists Use Location Data for Retail Analytics

In this episode, Lucas and Luna explore how retail data scientists are using anonymised location signals from mobile devices to answer questions like 'which competitor stores lose foot traffic when we open nearby' and 'how long do people linger in our aisles before buying'. They break down a real case from a regional grocery chain that combined GPS pings with transaction data to optimise store layouts and reduce checkout wait times by 11 percent. Lucas explains the technical stack: how raw location streams get cleaned, how to handle noisy pings (people walking past, not in), and the privacy guardrails — differential privacy and on-device aggregation — that make this possible without collecting personal identifiers. Luna challenges whether the signal is good enough in dense urban areas, and Lucas walks through a simple validation method using door sensors. The conversation also touches on the tension between useful insights and consumer creepiness, and why the best projects start with a narrow, well-defined question like 'where do people park and walk from' rather than 'let's analyse all the data'. If today's tech conversation gave you something usable, consider supporting the show at buy me a coffee dot com slash fexingo — no ads, just listener support. #DataScience #RetailAnalytics #LocationData #FootTraffic #Geospatial #Privacy #DifferentialPrivacy #OnDeviceAggregation #GroceryChain #StoreLayout #CheckoutWaitTimes #Validation #DoorSensors #ConsumerPrivacy #SpatialDataScience #Technology #FexingoBusiness #BusinessPodcast Keep every episode free: buymeacoffee.com/fexingo