Using AI and Foot-Traffic Data to Pick Retail Store Locations

Brick-and-mortar retail chains are increasingly abandoning gut-feeling real estate decisions in favor of data science. By combining anonymized geospatial foot-traffic telemetry with artificial intelligence, retailers can accurately predict the profitability of a new store before signing a lease. This article provides an overview of how mobile location data is collected, how AI algorithms process this information alongside demographic and competitor metrics, and how businesses use these insights to select optimal physical locations and avoid costly real estate mistakes.

Understanding Geospatial Foot-Traffic Telemetry

Geospatial foot-traffic telemetry is the collection of anonymized location data from mobile devices. When consumers opt-in to location services on various smartphone apps, their movements generate data points utilizing GPS, Wi-Fi, and Bluetooth beacons. Data aggregators strip this information of all personally identifiable information to ensure privacy compliance.

For retailers, this telemetry provides a clear picture of human movement patterns. Instead of relying on outdated census data or manual clicker counts, chains can see exactly how many people walk or drive past a specific commercial property at any given hour, day, or season. It also reveals "dwell time"—how long people stay in one location—and the general neighborhoods from which these visitors originate.

The Role of Artificial Intelligence in Site Selection

Raw foot-traffic data is too vast and complex to analyze manually. This is where artificial intelligence, specifically machine learning, steps in. Retailers feed the anonymized telemetry into AI platforms alongside other datasets, such as local household income, weather patterns, public transit access, and property lease rates.

The AI processes these variables to find hidden correlations. By training the machine learning models on the historical performance of the chain’s existing stores, the AI learns the exact recipe that makes a location successful for that specific brand. For example, the AI might discover that a coffee chain performs best not just where foot traffic is highest, but specifically where morning pedestrian traffic intersects with a high concentration of nearby gym visitors.

Mapping the Trade Area and Competitor Impact

Traditionally, retailers defined a store’s trade area by drawing a simple three-to-five-mile radius around a property. AI and telemetry replace this guesswork with true catchment areas based on actual consumer behavior. The technology tracks the anonymized paths people take, showing how natural barriers like highways, rivers, or traffic congestion affect how far customers are willing to travel to reach a shopping center.

Furthermore, retailers use this technology to analyze the competition. By applying digital boundaries known as "geofences" around competitor stores, a retail chain can measure a rival's foot traffic. The AI assesses whether a competitor’s high traffic indicates a saturated market to avoid, or a thriving retail hub where a new store could successfully capture market share. It also identifies "co-tenancy" benefits, determining which neighboring businesses—like a popular grocery store or a target-demographic gym—naturally drive ideal customers to the immediate area.

Predicting Revenue and Preventing Cannibalization

The ultimate goal of combining telemetry with AI is accurate financial forecasting. Once the AI has analyzed a potential site's foot traffic, demographics, and competitive landscape, it runs predictive simulations to estimate the new store's annual revenue.

Crucially, these predictive models also calculate the risk of "cannibalization"—the phenomenon where a new store steals customers and revenue from the chain's existing nearby locations. Because the telemetry data shows the overlap in trade areas, the AI can warn executives if a proposed location will simply shift existing sales rather than generate new growth. By relying on these precise predictions, brick-and-mortar chains can negotiate better lease terms, minimize the risk of store closures, and maximize return on investment for every new location they open.