Geo-Tracking Consumers and its Privacy Trade-offs

65 Pages Posted: 5 Oct 2023 Last revised: 28 Mar 2024

See all articles by Unnati Narang

Unnati Narang

University of Illinois at Urbana-Champaign

Fernando Luco

Texas A&M University

Date Written: March 27, 2024

Abstract

Can geo-tracking data allow firms to better predict consumers' future behaviors? If so, how might potential privacy regulations limit the usefulness of geo-tracking data for prediction? Using data with over 120 million driving instances for 38,980 app users, and their visits to 422 restaurants in Texas, the authors quantify the extent to which geo-tracking data allow restaurants to better predict the number of visits one week ahead. They show that geo-tracking data increase the performance of prediction models by 14.77% relative to models that use demographic, behavioral, and static home location information. Simulation exercises that limit what data are tracked and in what form, 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 (i.e., summaries of driving behaviors) and in what form (i.e., synthetic data generated with nearby users’ data) result in the largest decreases in predictive performance (16.24% and 8.09%), while regulations that restrict where (i.e., within a few miles of a business location) and how frequently (i.e., at longer intervals) data are geo-tracked result in smaller decreases (3.56% and .77-2.46%, depending on the frequency). Importantly, models with restricted geo-tracking generally outperform models that do not use any geo-tracking information. These findings can assist managers and policymakers in assessing the risks and benefits associated with the use of geo-tracking data.

Keywords: Mobile app, location tracking, privacy, prediction, machine learning, transformers

JEL Classification: C23, C45, M1, M3, Z23, Z28

Suggested Citation

Narang, Unnati and Luco, Fernando, Geo-Tracking Consumers and its Privacy Trade-offs (August 3, 2023). Available at SSRN: https://ssrn.com/abstract=4209378 or http://dx.doi.org/10.2139/ssrn.4209378

Unnati Narang (Contact Author)

University of Illinois at Urbana-Champaign ( email )

601 E John St
Champaign, IL Champaign 61820
United States
N/A (Fax)

HOME PAGE: http://unnatinarang.com

Fernando Luco

Texas A&M University ( email )

4228 TAMU
College Station, TX 77843
United States

Do you have negative results from your research you’d like to share?

Paper statistics

Downloads
219
Abstract Views
1,220
PlumX Metrics