Retail Industry Analytics: Unraveling Consumer Behavior through RFM Segmentation and Machine Learning

This study explores the complex dynamics of consumer behavior in the UK retail industry using advanced analytics. We developed a preprocessed retail dataset including 495,478 retail customers. Here, we employ a combination of methods including a marketing analysis method known as RFM (Recency, Frequency, Monetary) segmentation, Box-Cox transformation, and temporal analysis to predict consumer behavior. Further, we use multiple supervised machine learning models (e.g. RandomForest, AdaBoost, ExtraTrees, LGBM, and XGBoost) where XGBClassifier and ET have achieved the highest accuracy in forecasting customer lifetime value. We show that the performance metrics of ML models are impressive: an accuracy of 92.40%, precision of 92.27%, recall of 92.40%, F1 score of 92.28%, and an AUC score of 97.39. We also find that the results not only demonstrated the model's accuracy in forecasting clusters of customer lifetime value but also confirmed the robustness of ML methods. Key findings from RFM analysis indicate its unique merit in providing vital insights into clients and their behavior. This research establishes a new standard in retail analytics, offering a scalable and efficient methodology for future studies focused on utilizing data analytics to comprehend and predict customer behavior in different business entities.