An RFM Model Using K-Means Clustering to Improve Customer Segmentation and Product Recommendation

Xian Zhao · Pantea Keikhosrokiani · XinYing Chew · Zuoyong Li

25 citations0/0 files storedAdvances in marketing, customer relationship management, and e-services book series

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Customer churn and segmentation Recommender Systems and Techniques Data Mining and Machine Learning Applications

Abstract

The COVID-19 pandemic instigated thousands of companies' closures and affected offline retail shops. Thus, online B2C business models enable traditional offline stores to boost their sales. This study aims to explore the use of historical sales and behavioral data analytics to construct a recommendation model. A process model is proposed, which is the combination of recency, frequency, and monetary (RFM) analysis method and the k-means clustering algorithm. RFM analysis is used to segment customer levels in the company while the association rule theory and the apriori algorithm are utilized for completing the shopping basket analysis and recommending products based on the results. The proposed recommendation model provides a good marketing mix to improve sales and market responsiveness. In addition, it recommends specific products to new customers as well as specific groups of target customers. This study offered a practical business transformation case that can assist companies in a similar situation to transform their business model and improve their profits.

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2008Application of data mining techniques in customer relationship management: A literature review and classification 2001Knowledge management and data mining for marketing 1995Optimal Selection for Direct Mail 2008Classifying the segmentation of customer value via RFM model and RS theory 2005Customer segmentation and strategy development based on customer lifetime value: A case study 2009Cluster analysis using data mining approach to develop CRM methodology to assess the customer loyalty 2008Database Marketing 2004A purchase-based market segmentation methodology

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2023Customer Segmentation Using K-Means Clustering Algorithm and RFM Model

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