Retail store customer behavior analysis system: Design and Implementation (2309.03232v1)

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.

• The paper presents a system that models customer movements using finite-state machines to capture key behavior transitions.
• It applies multi-layered deep learning for precise detection of human poses, object interactions, and group formations in real-world retail environments.
• The system demonstrates scalability and practical effectiveness through distributed deployment and robust performance metrics in state recognition.

Retail Store Customer Behavior Analysis System: Design and Implementation

The paper "Retail Store Customer Behavior Analysis System: Design and Implementation" presents a comprehensive framework aimed at analyzing customer behavior within retail environments. This framework leverages advanced deep learning methods to improve existing approaches and addresses limitations in traditional customer behavior analysis systems.

Framework Overview

The proposed system consists of three primary components:

1. Mathematical Modeling of Customer Behaviors:
   • The researchers model individual customers as finite-state machines, which allows for a structured representation of various customer states such as Idle (I), Approach (A), Pick (P), and Leave (L).
   • State transitions are determined by attributes like physical location, head pose, and interactions, thus enabling a fine-grained analysis of customer activities.
2. Deep Learning-Based Behavior Analysis System:
   • The system utilizes a multi-layered architecture to process sensor data, specifically from cameras equipped with depth data.
   • Modules within the system employ state-of-the-art computer vision techniques such as deep neural networks for detecting and tracking humans, object classification, and pose estimation.
3. Behavior Visualization:
   • The framework visualizes both individual and group behaviors, providing insights into personal actions and interactions within group settings, classified under configurations such as L-shaped, Vis-Vis, and Side-by-Side formations.

Strong Methodological Advances

• Layer-Based Architecture:

The architecture is designed in layers: Sensor, Base, Advanced, and State. Each layer addresses different components of behavior analysis, from raw data capture to state management and transition logging.

• Integration of Deep Learning Techniques:

By integrating deep learning into modules such as human pose estimation and object detection, the system achieves high accuracy in identifying customer states and actions.

• Distributed and Scalable System:

The system operates using a message-based approach facilitating distributed deployment across multiple devices, thus optimizing speed and computational load.

Validation and Results

The system was validated in a real-world retail environment, demonstrating the feasibility and efficiency of the proposed framework. Key performance metrics include:

• Approach and Pick State Recognition: Achieved reasonable precision and recall, demonstrating the system’s capability to track customer engagement with products.
• F-Formation Group Recognition: Displayed robust recall, indicating effective detection and classification of group interactions.

Implications and Future Directions

This research offers valuable implications for both theoretical exploration and practical application in retail environments:

• Enhanced Customer Insights: The system’s ability to visualize detailed behaviors allows for richer insights into customer preferences and interaction dynamics.
• Potential for Behavioral Influence Enhancement: Understanding state transitions and group interactions enables retailers to tailor marketing strategies and optimize store layouts.
• Future Research Avenues: Future developments could focus on incorporating more sophisticated probabilistic models such as Dynamic Bayesian Networks to better handle uncertainty and improve behavioral predictions.

Conclusion

In conclusion, the paper provides a well-rounded approach to understanding customer behavior using advanced AI techniques. The combination of mathematical modeling and deep learning ensures a detailed capture and analysis of customer interactions. This research lays a foundation for further exploration into behavior analytics, promising enhancements in both customer experience and retail strategy implementation.