Predictive Modeling and Customer Segmentation Through Data Analytics

Abstract

Background: The rapid proliferation of digital retail channels has generated vast repositories of consumer behavioral data, creating both an opportunity and an imperative for organizations to derive actionable intelligence through advanced analytical frameworks. This study investigates predictive modeling and customer segmentation methodologies applied to retail transactional datasets, drawing upon two years of applied industry experience in retail analytics. Objective: To develop a robust, replicable analytical pipeline integrating K-Means clustering, Random Forest classification, and Recency-Frequency-Monetary (RFM) modeling for precise customer segmentation and purchase behavior prediction in retail environments. Methods: A quantitative research design was adopted using a retail transaction dataset comprising 522,268 clean records across 84,531 unique customers spanning 24 months. Predictive models were trained, validated, and benchmarked against logistic regression baselines using precision, recall, F1-score, and AUC-ROC metrics. Results: The Random Forest model achieved a predictive accuracy of 91.4% and an AUC-ROC score of 0.963. RFM-based segmentation revealed five distinct customer cohorts. Segment-specific marketing interventions improved customer retention by 23.7%. Feature importance analysis identified purchase recency and frequency as the dominant churn predictors. Conclusion: The integrated framework of machine learning-driven segmentation and predictive analytics yields significantly superior retail intelligence compared to traditional methods. The proposed pipeline offers scalable, practical guidance for retail practitioners and advances the theoretical literature on data-driven consumer behavior modeling.

Introduction

It highlights that modern retail generates massive transactional data, but many retailers still fail to fully use predictive analytics. The study addresses this gap by proposing a hybrid framework that combines RFM analysis, K-Means clustering, and Random Forest modeling to better understand customer behavior and predict churn.

The research objectives include building churn and purchase prediction models, improving customer segmentation using RFM and clustering, identifying key behavioral drivers of customer lifetime value, and converting insights into practical business strategies.

The methodology uses a large real-world dataset of over 522,000 transactions from 84,000+ customers. Customers are segmented using RFM scores and K-Means clustering (optimal K=5), resulting in groups like Champions, Loyal, At-Risk, Dormant, and Lapsed customers. A Random Forest model is then trained to predict churn, outperforming logistic regression with 91.4% accuracy and 0.963 AUC-ROC.

Key findings show strong revenue concentration among high-value customers (Champions), confirming the importance of targeted retention strategies. Overall, the study demonstrates that combining clustering with predictive modeling improves both segmentation quality and churn prediction performance, offering a scalable, practical approach for retail decision-making.

Conclusion

This research demonstrates that integrating RFM segmentation, K-Means clustering, and Random Forest prediction constitutes a high-performing, practically deployable framework for retail customer intelligence. The pipeline achieves 91.4% predictive accuracy, identifies five actionable customer cohorts, and enables segment-specific interventions producing a 23.7% retention improvement. The study\'s grounding in two years of retail analytics industry experience ensures that contributions extend beyond theoretical novelty to practical applicability. The revenue concentration finding (Champions: 12.4% of customers, 41.3% of revenue) underscores the commercial urgency of precision segmentation. Future research directions include: (a) multi-channel attribution modeling; (b) LSTM-based sequential purchase prediction; (c) sentiment enrichment from customer review data; and (d) longitudinal validation across multiple retail verticals. Organizations build these analytical competencies now will be disproportionately positioned to capture value in the that data-driven retail landscape of the coming decade.

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Copyright

Copyright © 2026 Mr. Kartik More, Dr. Irfan A. Chaugule. 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.