Analyzing the Shopping Journey: Computing Shelf Browsing Visits in a Physical Retail Store

TL;DR

This paper presents an algorithm to analyze customer browsing behavior in physical retail stores using 3D tracking data, with implications for retail planning and human-robot interaction.

Contribution

It introduces a novel method for computing shelf visits from trajectory data, validated across different stores and environments.

Findings

The algorithm accurately recognizes browsing activity across different store environments.

Shelf browsing patterns correlate with actual purchase behavior.

The method enables insights into customer behavior for retail and robotics applications.

Abstract

Motivated by recent challenges in the deployment of robots into customer-facing roles within retail, this work introduces a study of customer activity in physical stores as a step toward autonomous understanding of shopper intent. We introduce an algorithm that computes shoppers' ``shelf visits'' -- capturing their browsing behavior in the store. Shelf visits are extracted from trajectories obtained via machine vision-based 3D tracking and overhead cameras. We perform two independent calibrations of the shelf visit algorithm, using distinct sets of trajectories (consisting of 8138 and 15129 trajectories), collected in different stores and labeled by human reviewers. The calibrated models are then evaluated on trajectories held out of the calibration process both from the same store on which calibration was performed and from the other store. An analysis of the results shows that the algorithm can recognize customers' browsing activity when evaluated in an environment different from the one on which calibration was performed. We then use the model to analyze the customers' ``browsing patterns'' on a large set of trajectories and their relation to actual purchases in the stores. Finally, we discuss how shelf browsing information could be used for retail planning and in the domain of human-robot interaction scenarios.