Customer In-Store Behavior Analysis Using Beacon Data at a Home Improvement Retailer

Abstract

In this study, we aimed to analyze the in-store behavior of customers at a home improvement retail company using data collected from Bluetooth Low Energy beacon devices installed on shelves and shopping carts within a selected store. The beacons were strategically placed on store shelves to ensure complete coverage, leaving no blind spots. To cover 18 departments spanning a total area of approximately 4,800 square meters, 99 beacons were deployed. The duration of stay in each department, the order of visits, and the absolute visit date and time were recorded in the database. To investigate the relationship between in-store behavior and purchase data, we combined customers' behavioral data with their purchase information. Correlation analysis revealed a positive relationship between visit duration and purchase amount, particularly in the Floor Deco, Paint, and Taps departments. Additionally, we visualized store-wide data using a network diagram, highlighting key shopping areas, customer flow patterns, and high-revenue departments. The problem was also formulated as a multi-class classification task, and LSTM and XGBoost algorithms were applied for comparative analysis. Experiments were conducted on both the original dataset and a cleaned version, utilizing two distinct data modeling approaches: one based solely on sequential department visits and another incorporating visit duration. The results showed that both models performed similarly on the noisy dataset, indicating that adding duration information did not improve learning. However, when trained on the cleaned dataset where short- duration visits were removed, LSTM models outperformed XGBoost, demonstrating a stronger ability to capture meaningful sequential patterns. These findings highlight the potential of BLE beacon technology in retail analytics, offering deeper insights into customer behavior and informing data-driven decision-making for store optimization and personalized marketing. Future work will focus on expanding the dataset and refining predictive models to further enhance the accuracy and applicability of in-store behavior analysis.