Year: 2025
Type: article
Abstract: Accurately predicting consumer visits is critical for businesses to plan resources and serve customers better. We examine the extent to which geo-tracking data about consumers' locations can improve the ability of businesses to predict total visits to their store. Given the sensitive nature of geo-tracking data, we also examine how potential privacy regulations that restrict such data may limit their usefulness for prediction. Using proprietary data from a safe-driving app with over 120 million driving instances across 38,980 app users and aggregate data on the total number of visits to over 400 restaurants in Texas, we quantify the value of geo-tracking data by training machine learning models to predict total visits to a restaurant one week ahead. Our results show that geo-tracking data improve the performance of prediction models by 17.96% relative to that of models that use demographic and behavioral data only. Simulation exercises that limit what data are tracked, where and how frequently these data are tracked show a decrease in the predictive performance of models that use geo-tracking data. However, the decrease varies by the type of restriction; regulations that restrict what data are geo-tracked result in the largest decreases in predictive performance (26.83%), while regulations that restrict where users are geo-tracked and how frequently result in smaller decreases (2.12% and 4.74-5.21%, depending on the frequency). Importantly, models with restricted geo-tracking generally outperform models that do not use any geo-tracking data by reducing the extent of overpredicting and underpredicting visits. These findings can assist managers and policymakers.
Authors: Unnati Narang, Fernando Luco
Institutions: University of Illinois Urbana-Champaign, Texas A&M University
Language: English
Subfield: Sociology and Political Science
Field: Social Sciences
Domain: Social Sciences
Open Access status: hybrid
Estimated APC paid: $2,990
OpenAlex ID: W4413841167