A Case Study of Fintech Industry: A Two-Stage Clustering Analysis for Customer Segmentation in the B2B Setting

Purpose: This study proposes a new approach considering two-stage clustering and LRFMP model (Length, Recency, Frequency, Monetary and Periodicity) simultaneously for customer segmentation and behavior analysis among the Iranian Fintech companies. Methodology/Approach: In this study, the K-means clustering algorithm and LRFMP model are combined in the customer segmentation process. After initial clustering, for a better understanding of valuable customers, additional clustering is implemented in segments that needed further investigation. This approach contributes to the better interpretation of different customer segments. Finally, customer segments, consisting of 23524 B2B customers, are analyzed based on their characteristics and appropriate strategies are recommended accordingly. Findings: The first stage clustering result shows that customers are best segmented into four groups which are named as loyal and valuable customers, old and churned customers, young and churned customers and young and valuable customers. The first and fourth segments are clustered again and the final 11 groups of customers are determined. Originality/Value/Contribution of the Paper: This study contributes to the customer segmentation and customer relationship management literature building on and applying both the RFM and LRFMP models in the B2B setting by proposing a new approach based on a two-stage clustering method which assists to a more in-depth understanding of customer behavior.