Customer Segmentation Using K-Means Clustering Algorithm and RFM Model

Gözde ASLANTAŞ · Mustafacan GENÇGÜL · Merve RUMELLİ · Mustafa ÖZSARAÇ · Gözde Bakırlı

13 citations0/0 files storedDeu Muhendislik Fakultesi Fen ve Muhendislik

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Customer churn and segmentation Customer Service Quality and Loyalty Data Mining and Machine Learning Applications

Abstract

The key points in customer segmentation are determining target customer groups and satisfying their needs. Recency-Frequency-Monetary (RFM) analysis and K-Means clustering algorithm are the popular methods for customer segmentation when analyzing customer behavior. In our study, we adapt the K-means clustering algorithm to RFM model by extracting features that represent RFM aspects of home appliances. Customers with similar RFM-oriented features are assigned to the same clusters, while customers with non-similar RFM-oriented features are assigned to different clusters. In the experiments, clustering achieved the determined threshold for Silhouette Score. The resulting clusters were ranked and named by Customer Lifetime Value (CLV) metric, which measures how valuable a customer is to the business.

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References

2022K-Means Clustering Approach for Intelligent Customer Segmentation Using Customer Purchase Behavior Data 2019RFM model for customer purchase behavior using K-Means algorithm 2011Estimating customer lifetime value based on RFM analysis of customer purchase behavior: Case study 1974Marketing during Periods of Shortage 2007A Model to Determine Customer Lifetime Value in a Retail Banking Context 2020An Empirical Study on Customer Segmentation by Purchase Behaviors Using a RFM Model and K-Means Algorithm 2007Customer retention and price matching: The AFPs case 2003A method for customer lifetime value ranking — Combining the analytic hierarchy process and clustering analysis

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