Retail store customer behavior analysis system: Design and Implementation

TL;DR

This paper presents a comprehensive system for analyzing retail store customer behavior using deep learning, including modeling, analysis, and visualization, validated with real store data.

Contribution

It introduces a novel framework combining mathematical modeling, deep learning analysis, and visualization for customer behavior in retail stores, addressing limitations of prior methods.

Findings

Deep learning-based behavior recognition outperforms conventional methods.

The system effectively visualizes individual and group behaviors.

Validation with real store data demonstrates practical applicability.

Abstract

Understanding customer behavior in retail stores plays a crucial role in improving customer satisfaction by adding personalized value to services. Behavior analysis reveals both general and detailed patterns in the interaction of customers with a store items and other people, providing store managers with insight into customer preferences. Several solutions aim to utilize this data by recognizing specific behaviors through statistical visualization. However, current approaches are limited to the analysis of small customer behavior sets, utilizing conventional methods to detect behaviors. They do not use deep learning techniques such as deep neural networks, which are powerful methods in the field of computer vision. Furthermore, these methods provide limited figures when visualizing the behavioral data acquired by the system. In this study, we propose a framework that includes three primary parts: mathematical modeling of customer behaviors, behavior analysis using an efficient deep learning based system, and individual and group behavior visualization. Each module and the entire system were validated using data from actual situations in a retail store.