Customer SCustomer Segmentation Using an Enhanced RFM–K-Means Framework on The Online Retail Datasetegmentation Using Enhanced K – Means Clustering

Authors

  • Isnandar Agus Institute Informatics and Business Darmajaya, Indonesia, Jln ZA Pagar Alam 93 A, Bandar Lampung and 35136, Indonesia
  • MS Hasibuan Institute Informatics and Business Darmajaya, Indonesia, Jln ZA Pagar Alam 93 A, Bandar Lampung and 35136, Indonesia

DOI:

https://doi.org/10.47738/ijiis.v8i4.289

Keywords:

Customer Segmentation, Online Retail Transactions, RFM Analysis, K-Means Clustering, Data Preprocessing, Cluster Optimization, Targeted Marketing, Retail Analytics

Abstract

Effective customer segmentation is crucial for online retailers to enhance marketing strategies and boost profitability. However, analyzing transactional data often reveals challenges, such as noisy records and incomplete temporal patterns, which hinder accurate customer profiling. This paper proposes a robust methodology combining RFM (Recency, Frequency, Monetary) analysis with enhanced K-means clustering to segment customers of a UK-based online retailer, using data from December 2010 to December 2011. We preprocess the data to handle anomalies, engineer RFM features, and optimize cluster selection using the Elbow Method and Davies-Bouldin score, identifying four distinct segments: Best Customers, Loyal Customers, Almost Lost, and Lost Cheap Customers. Results show a 5% improvement in segmentation accuracy compared to baseline methods, with actionable insights for targeted marketing. This approach not only advances customer segmentation techniques but also offers practical value for retail businesses aiming to improve customer retention and sales.

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Published

2025-12-24

Issue

Vol. 8 No. 4: December 2025

Section

Articles

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