A Predictive CRM Analytics Framework For Merchant Retention: Applying RFM Segmentation, Customer Profiling, and Behavioral Analytics In The B2B Payment Gateway Company - Stenaldo Abdinegara Kabut; Nila Armelia Windasari (PT. Publikasiku Academic Solution, 2024)
The history of payment systems, especially in the digital world, tells an engaging story of technological breakthroughs, economic growth, and shifts in society. The purpose of this study is to find out a Predictive CRM analytics framework for merchant retention: integrating RFM Segmentation, customer profiling, and dynamic insights in the B2B payment gateway company. This research outlines the research methodology, which links the theoretical concepts discussed earlier with the empirical evidence to be analyzed. Result diverse merchant base, spanning enterprises and SMEs across a wide range of industries, presents a unique set of challenges for retention. With over 50,000 merchants active and a complex landscape of transaction patterns and payment preferences, a one-size-fits-all approach is insufficient. A robust, adaptive, and data-driven framework is essential to effectively address these challenges and drive long-term merchant loyalty. The conclusion is This comprehensive analysis of the merchant base through the lens of enriched RFM segmentation, transactional trends, payment preferences, and CLV reveals a dynamic and nuanced landscape.