Claim Missing Document
Check
Articles

Found 1 Documents
Search

Leveraging the RFM Model for Customer Segmentation in a Software-as-a-Service (SaaS) Business Using Python Andy Hermawan; Nila Rusiardi Jayanti; Aji Saputra; Army Putera Parta; Muhammad Abizar Algiffary Thahir; Taufiqurrahman Taufiqurrahman
Maeswara : Jurnal Riset Ilmu Manajemen dan Kewirausahaan Vol. 2 No. 5 (2024): OKTOBER : Maeswara : Jurnal Riset Ilmu Manajemen dan Kewirausahaan
Publisher : Asosiasi Riset Ilmu Manajemen Kewirausahaan dan Bisnis Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.61132/maeswara.v2i5.1283

Abstract

Customer segmentation plays a pivotal role in driving marketing strategies and improving customer retention across various industries. This study explores the application of the RFM (Recency, Frequency, Monetary) model for customer segmentation in a Software-as-a-Service (SaaS) business, using Python for efficient data processing and analysis. By analyzing one year of customer purchase data, we segmented customers into key groups such as "Champions," "Loyal Customers," and "At Risk." The results highlight that targeted discount strategies significantly affect profitability, especially for high-value customer segments. Furthermore, the research builds upon existing methodologies, demonstrating how Python-based implementations streamline RFM analysis and allow for scalable solutions in business contexts, as illustrated in prior works by Hermawan et al. (2024). This study offers actionable recommendations, including tailored discounting, loyalty programs, and personalized engagement strategies, to enhance customer retention and business profitability. The findings underscore the importance of data-driven marketing approaches for customer segmentation and engagement, reinforcing the relevance of the RFM model in modern business environments.