Smart Next-Generation Revenue Growth: A Methodology for Partitioning Customers Utilizing the K-Means Algorithm and RFM Model

Abstract

Customer segmentation & pattern extraction are pivotal elements in a business's decision-making process, particularly in highly competitive industries. Swiftly identifying & understanding the potential of customers is crucial for a successful expansion strategy. This paper introduces an innovative, integrated approach for pinpointing target customers & optimizing revenue generation for organizations. The primary objective of this study is to categorize customer data into distinct groups based on their unique characteristics, employing the K-means algorithm. Additionally, the RFM (Recency, Frequency, Monetary) model is applied to rank & group customers according to their recent transaction behaviors. To assign new customers to appropriate clusters, a mean value is utilized as the primary indicator.The paper employs the Elbow method for an initial selection of the number of clusters & the Davies-Bouldin score to determine the optimal cluster count. The results are visualized through flattened graphs & Snake plots for different K values, enabling a comprehensive analysis of the clusters and aiding in the finalization of the optimal K value. This research presents a powerful framework for customer segmentation, ultimately supporting managers in making informed strategic decisions and enhancing an organization's revenue potential.

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