A PREDICTIVE CRM ANALYTICS FRAMEWORK FOR MERCHANT RETENTION: APPLYING RFM SEGMENTATION, CUSTOMER PROFILING, AND BEHAVIORAL ANALYTICS IN THE B2B PAYMENT GATEWAY COMPANY DOKU

• Main Author: Abdinegara Kabut, Stenaldo
• Format: Theses
• Language: Indonesia
• Subjects: Manajemen umum
• Online Access: https://digilib.itb.ac.id/gdl/view/83209
• Institution: Institut Teknologi Bandung

This study investigates the application of predictive CRM analytics to enhance merchant retention within DOKU, a leading Indonesian B2B payment gateway provider. The primary research objective is to develop a comprehensive understanding of merchant behavior and to create a framework for improving retention strategies. To achieve this, the methodology involves analyzing a sample of 6,000 active merchants from a database of over 50,000. The research utilizes RFM (Recency, Frequency, Monetary) segmentation, customer profiling (industry type, company size, location), and behavioral analytics (payment preferences, transaction trends). The research design is primarily deductive, aiming to test and validate existing theoretical frameworks and hypotheses regarding CRM practices and merchant retention in the operational context of a payment gateway company. The data collection process involves both primary and secondary sources. Primary data is obtained directly from DOKU's CRM systems, transactional data systems, and Merchant Data Base Center, focusing on current merchant interactions, engagement, and payment behaviors. Secondary data includes academic literature and company records to provide theoretical context and historical insights. A stratified random sampling strategy is employed to ensure that the sample is representative of the diverse merchant population. This approach involves dividing the entire merchant population into homogeneous strata based on criteria such as merchant size, industry type, or transaction volume, ensuring comprehensive coverage of the various subgroups within DOKU’s merchant base. Key findings reveal ten distinct merchant segments, each with unique characteristics. High-value segments like Champions (7.23%, CLV Rp 175,820,705) and Loyal Customers (13.55%, CLV Rp 127,694,535) demonstrate a preference for both traditional (bank transfers: 682,711 transactions, credit cards: 281,208 transactions) and digital payment methods (QRIS: 1,165,045 transactions). In contrast, lower-CLV segments like New Customers (3.92%, CLV Rp 10,480,812) primarily rely on traditional methods, highlighting opportunities for targeted education and promotion of digital payments. The study culminates in the development of a Customer Segmentation Dashboard (CSD), a dynamic tool integrated with the CRM system. The CSD provides realtime visualizations of merchant segments, payment preferences, CLV, and other key metrics. This enables DOKU to implement personalized engagement strategies, such as targeted promotions for digital payment adoption in specific segments or win-back campaigns for at-risk customers. The CSD's predictive capabilities allow the company to anticipate future trends and proactively adapt its offerings, ensuring sustained growth in the competitive B2B payment gateway market. The recommendations focus on maintaining high-value segments by offering exclusive rewards, personalized offers, and early access to new features. For segments with high growth potential, such as Potential Loyalists, OTA, and Hospitality, the introduction of QRIS, loyalty programs, and advanced analytics is suggested. Additionally, enhancing engagement and retention for Promising and New Customers through QRIS, basic support services, and educational resources is recommended. Reactivation and retention strategies for At Risk, Can't Lose Them, About to Sleep, Needs Attention, and Hibernating segments include providing QRIS, reactivation programs, and targeted discounts. Integrating automation and up-to-date data analytics through the CSD will enhance these efforts, providing a dynamic and responsive approach to managing the diverse needs of DOKU’s merchant base.