METARFM: A META-LEARNING FRAMEWORK FOR THE ADAPTIVE SELECTION OF RFM MODEL ARIANTS IN CUSTOMER SEGMENTATION

Abstract

The Recency-Frequency-Monetary (RFM) model is a widely used method for customer segmentation, but its effectiveness depends on selecting the appropriate variant (e.g., weighted or entropy-based) for a given dataset. This selection process is typically manual and task-specific, leading to inconsistent results and limited generalizability. To address this issue, we present MetaRFM, a novel automated framework for selecting optimal RFM variants. MetaRFM mines a set of meta-features—such as sparsity, diversity, and skewness—extracted from customer transaction datasets, including both personal transaction data and product purchase information. These meta-features characterize the dataset at a high level, enabling the framework to predict which RFM variant would perform best. A meta-learner is trained to map these meta-features to the performance of different RFM variants, which are evaluated using both cluster quality metrics (Silhouette Score, Davies-Bouldin Index) and business-relevant metrics (predictive lift, churn prediction accuracy). Extensive experiments conducted on real-world datasets from retail, e-commerce, and subscription services show that MetaRFM consistently outperforms static and single-variant models. On average, MetaRFM improves cluster separation by 15.7% and campaign lift by 22.3%. This framework provides a systematic, scalable solution for selecting the most appropriate RFM model, improving segmentation robustness and business relevance. The results highlight the substantial potential of meta-learning for adaptive, context-aware analytics in marketing, offering a more effective approach to customer segmentation and optimizing marketing strategies.