Revolutionizing Retail Analytics: Advancing Inventory and Customer Insight with AI

TL;DR

This paper presents an advanced retail analytics system leveraging AI, combining object detection, tracking, and predictive modeling to improve inventory management and customer insights.

Contribution

It introduces a hybrid architecture integrating YOLOV8, BOT-SORT, ByteTrack, and GRU models for enhanced customer tracking and demand forecasting in retail environments.

Findings

YOLOV8 fine-tuning achieved high performance on retail footage.

GRU model outperformed others in demand prediction metrics.

The system enables accurate visitor counts and heat maps.

Abstract

In response to the significant challenges facing the retail sector, including inefficient queue management, poor demand forecasting, and ineffective marketing, this paper introduces an innovative approach utilizing cutting-edge machine learning technologies. We aim to create an advanced smart retail analytics system (SRAS), leveraging these technologies to enhance retail efficiency and customer engagement. To enhance customer tracking capabilities, a new hybrid architecture is proposed integrating several predictive models. In the first stage of the proposed hybrid architecture for customer tracking, we fine-tuned the YOLOV8 algorithm using a diverse set of parameters, achieving exceptional results across various performance metrics. This fine-tuning process utilized actual surveillance footage from retail environments, ensuring its practical applicability. In the second stage, we explored integrating two sophisticated object-tracking models, BOT-SORT and ByteTrack, with the labels detected by YOLOV8. This integration is crucial for tracing customer paths within stores, which facilitates the creation of accurate visitor counts and heat maps. These insights are invaluable for understanding consumer behavior and improving store operations. To optimize inventory management, we delved into various predictive models, optimizing and contrasting their performance against complex retail data patterns. The GRU model, with its ability to interpret time-series data with long-range temporal dependencies, consistently surpassed other models like Linear Regression, showing 2.873% and 29.31% improvements in R2-score and mAPE, respectively.