Segmentation of business saving customer to improve average balance based on structural equation modeling (SEM) and recency, frequency, monetary (RFM): Case study Bank XYZ in Indonesia

Abstract

As business players, entrepreneurs certainly need bank products and supports that provide fast and easy services with wide-spread network in Indonesia. RFM is a segmentation method based on past data and create an index on a client where the high loyalty and assume the behaviour of customers in the index will be the same in the future. Certainly, customers with high RFM score were customers who become the target of the Bank because these customers have high loyalty and valuable for the Bank. In this study, segmentation performed based on transactions which affect the increase in average balance using Structural Equation Model (SEM). The objects of RFM segmentation is to identify the customer in order to build a marketing strategy for each segment with different levels of loyalty. As the segmentation results we found three driver categories, High Recency, Middle Recency and Low Recency customer category. High Recency is considered Active customer where campaign category can be cross/up-selling and promotional accordingly with their Frequency and Monetary. Middle Recency category is considered Risky customer where campaign category can be retention program accordingly with their Frequency and Monetary. Last, Low Recency is considered Churn customer where campaign category is reactivation