Enhancing Customer Relationship Management: Integrating Interpurchase Time with RFM Segmentation for Advanced Customer Insights

In the evolving landscape of customer relationship management (CRM), traditional segmentation models such as reliability, frequency, and monetary (RFM) have been instrumental in tailoring business strategies to diverse customer bases. However, these models often fall short of capturing the dynamic nature of customer behaviors and the temporal aspects of their purchasing patterns. Existing segmentation frameworks lack the ability to incorporate the temporal dynamics between customer purchases, leading to a gap in accurately predicting future customer behaviors and effectively personalizing marketing efforts. This study introduces an enhanced RFMT model that extends the traditional RFM segmentation by incorporating interpurchase time (IPT), providing a more nuanced understanding of customer behavior. By integrating this temporal dimension, our model aims to offer a more comprehensive view of customer engagement and purchase patterns. The enhanced RFMT model was empirically tested and demonstrated superior performance compared to traditional segmentation techniques. Specifically, our model achieved a silhouette score of 0.90, significantly higher than the scores obtained by standard RFM and other benchmark models. The improved RFMT model also got a Davies-Bouldin index of 0.40 and a Calinski-Harabasz index of 12,950, which means it found clear, separate clusters that work better than those made by traditional methods. Our findings suggest that incorporating IPT into customer segmentation practices greatly enhances the ability of businesses to understand and predict customer behaviors, thereby facilitating more targeted and effective CRM strategies.