An Explainable AI Framework for Integrated Retail Analytics and Predictive Business Intelligence

Abstract

In today’s retail environment, organizations generate huge amounts of transactional data. However, many companies still use traditional reporting systems that are primarily concerned with historical analysis of sales. Such approaches provide limited insights into future customer behaviour and long term profitability. The paper introduces a Retail Analytics and Customer Intelligence Platform that uses a combination of machine learning techniques to integrate customer segmentation, Customer Lifetime Value (CLV) prediction and sales forecasting. The Online Retail dataset is pre-processed and transformed to generate RFM (Recency, Frequency, Monetary) features to understand customer purchasing behaviour. Machine learning algorithms such as Linear Regression, Random Forest and XG Boost are used to estimate customer lifetime value. XG Boost was the best model with an R2 score of 0.986, indicating good predictive power among these models. Customers are segmented into Low, Medium and High value segments to facilitate targeted marketing campaigns. There’s also a sales forecasting module that predicts revenue trends to help with business planning. Developed an interactive dashboard with Streamlit for data visualization and decision making. The proposed system helps retailers to increase customer retention, optimize marketing efforts, and improve accuracy of revenue prediction.

Introduction

The study focuses on Retail Analytics and Customer Intelligence, aiming to predict Customer Lifetime Value (CLV) from large-scale retail transaction data. It highlights how traditional retail reporting systems are mostly descriptive and fail to predict future customer behavior, making it difficult for businesses to optimize retention and profitability.

To address this, the proposed system uses a machine learning-based pipeline that includes data preprocessing, feature engineering, customer segmentation, and predictive modeling. A key component is the RFM (Recency, Frequency, Monetary) model, which transforms raw transaction data into meaningful behavioral features for analysis.

Three regression models—Linear Regression, Random Forest, and XGBoost—are used to predict CLV. Among them, XGBoost performs best, achieving the highest R² score and lowest error, showing strong predictive capability. Customers are also segmented into low, medium, and high-value groups using clustering techniques.

The system is implemented in Python with tools like Pandas, Scikit-learn, and Streamlit, and includes an interactive dashboard for visualization of predictions, customer segments, and business insights. Results show that the approach effectively converts raw retail data into actionable insights for better decision-making, customer retention, and marketing strategy optimization.

Conclusion

The proposed Retail Analytics and Customer Intelligence Platform successfully shows how machine learning can be used to analyse customer behaviour and improve business decision-making. The system processes retail transaction data, performs customer segmentation, predicts customer lifetime value, and analyses future sales trends. Different machine learning models were trained and compared using performance metrics such as RMSE and R² Score. Among all the models, XG Boost produced the best prediction results with higher accuracy and lower error values. The project also includes an interactive Streamlit dashboard that helps users easily visualize customer insights, sales performance, and prediction results. Explainable AI techniques like SHAP and LIME were used to make the predictions more understandable and transparent. Overall, the system helps businesses identify valuable customers, improve customer retention strategies, and make better data-driven decisions for business growth.

References

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Copyright

Copyright © 2026 Krovvidi Sri Harsha, Dr. B. Kranthi Kiran. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.