RFM model for customer purchase behavior using K-Means algorithm

• Published in: Journal of King Saud University. Computer and information sciences Vol. 34; no. 5; pp. 1785 - 1792
• Main Authors: Anitha, P., Patil, Malini M.
• Format: Journal Article
• Language: English
• Published: Elsevier B.V 01-05-2022; Elsevier
• Subjects: Business intelligence; Frequency; Monetary; Recency; Segmentation; Silhouette coefficient; Silhouette coefficient; Frequency; Recency; Segmentation; Business intelligence; Monetary
• ISSN: 1319-1578; 2213-1248
• DOI: 10.1016/j.jksuci.2019.12.011

The objective of this study is to apply business intelligence in identifying potential customers by providing relevant and timely data to business entities in the Retail Industry. The data furnished is based on systematic study and scientific applications in analyzing sales history and purchasing behavior of the consumers. The curated and organized data as an outcome of this scientific study not only enhances business sales and profit, but also equips with intelligent insights in predicting consumer purchasing behavior and related patterns. In order to execute and apply the scientific approach using K-Means algorithm, the real time transactional and retail dataset are analyzed. Spread over a specific duration of business transactions, the dataset values and parameters provide an organized understanding of the customer buying patterns and behavior across various regions. This study is based on the RFM (Recency, Frequency and Monetary) model and deploys dataset segmentation principles using K-Means Algorithm. A variety of dataset clusters are validated based on the calculation of Silhouette Coefficient. The results thus obtained with regard to sales transactions are compared with various parameters like Sales Recency, Sales Frequency and Sales Volume.