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

Abstract

ABSTRACT This practitioner note proposes a new approach considering two-stage clustering and LRFMP model (Length, Recency, Frequency, Monetary and Periodicity) simultaneously for customer segmentation and behavior analysis and applies it among the Iranian Fintech companies. In this practitioner note, 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 a better interpretation of different customer segments. Customer segments, consisting of 23524 business customers are analysed based on their characteristics and appropriate strategies are recommended accordingly. The first stage clustering result shows that customers are best segmented into four groups. The first and fourth segments are clustered again and the final 11 groups of customers are determined. This note provides a systematic and practical approach for researchers and practitioners for segmentation, interpretation, and targeting of customers especially in the B2B setting and the Fintech industry and helps managers to make effective marketing strategies and enhance customer relationship and marketing intelligence.