Revolutionizing Retail Analytics: Advancing Inventory and Customer Insight with AI (2405.00023v1)

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.

• The paper demonstrates a hybrid model that integrates YOLOv8 for real-time object detection and BOT-SORT for accurate tracking to overcome retail challenges.
• The paper employs a GRU forecasting model that improves demand prediction accuracy with a 2.87% R2-score boost and nearly 29.31% enhancement in mAPE over simpler methods.
• The paper highlights that combining advanced computer vision with robust time-series forecasting significantly elevates operational efficiency and customer flow management in retail environments.

Advanced Retail Analytics through AI: A Technical Overview

The paper "Revolutionizing Retail Analytics: Advancing Inventory and Customer Insight with AI" provides an in-depth exploration of the use of ML and computer vision models in enhancing operational efficiencies within the retail industry. The paper introduces a Smart Retail Analytics System (SRAS) leveraging methodologies like YOLOV8 for object detection, coupled with tracking algorithms such as BOT-SORT and ByteTrack, to resolve challenges prevalent in retail sectors. Addressing issues like inefficient queue management and demand forecasting, the authors propose a hybrid model enhancing customer engagement and store productivity through improved data analytics.

Methodologies and Experimental Design

Object Detection and Tracking: The research incorporates the YOLOV8 architecture, fine-tuning it for real-time person detection. This model was tested against the MMPTRACK dataset, achieving notable Mean Average Precision (mAP) scores, with a focus on enhancing identification through optimized parameter tuning. Complementary to detection, object tracking was explored with BOT-SORT outperforming ByteTrack, especially in minimizing false positives and maintaining track stability, integral for effective customer flow analysis.

The architectural evolution of YOLO from v1 to v8 is highlighted in the paper, with YOLOv8 showing an optimal balance between precision and processing efficiency, achieving a notable mAP of 90% on the COCO dataset. This single-stage network processes expansive image data in minimal time (3 milliseconds latency), making it suitable for dynamic retail contexts. Experimental results from manipulating YOLO's layers emphasize the significant improvements in detection accuracy when employing a one-layer tuning strategy.

Demand Forecasting: Time-series analyses using models such as GRU, LSTM, XGBoost, CNN, and Linear Regression are conducted to predict inventory loads accurately. The GRU model consistently advanced in performance metrics, such as a 2.873% improvement in R2-score and nearly a 29.31% increase in mAPE over simpler models like Linear Regression. This demonstrates GRU's adeptness at handling complex retail patterns and long-term temporal dependencies within sales data.

Results and Implications

The integration of these advanced models led to promising operational outcomes in real-world retail environments. By improving customer flow management and optimizing inventory, the system reduces stockouts and minimizes overstock scenarios. The precision in tracking and forecasting facilitates resourceful staffing, aligning employee presence with customer demand, thus enhancing service efficiency while reducing associated costs. The paper reveals the potential for such systems to drive comprehensive, data-driven decision-making processes within retail sectors.

Future Directions

The implications of this research extend to the broader adoption of AI systems in retail analytics, emphasizing the need for continued refinement of ML models in practical applications. Future research might explore more granular integration of machine learning models with IoT sensors within smart retail setups, enhancing predictive accuracy and operational robustness. Additionally, adapting these models to incorporate evolving customer data privacy regulations could be a significant avenue of exploration.

In conclusion, the paper presents a robust framework for implementing AI technologies in retail analytics, promising substantial enhancements in efficiency and customer experience. By advancing methodologies in demand prediction and object tracking, the proposed SRAS can serve as an adept solution to persistent challenges in the retail industry.