Customer analytics for online retailers using weighted k-means and RFM analysis

Ahmed Mohamed Ahmed Serwah · Khai Wah Khaw · Cheang Peck Yeng Sharon · Alhamzah Alnoor

7 citations0/0 files storedData Analytics and Applied Mathematics (DAAM)

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Customer churn and segmentation Consumer Market Behavior and Pricing Data Mining and Machine Learning Applications

Abstract

In recent years, there has been a significant trend toward data-driven enterprises in the business world. This trend is exemplified by the frustration reported by 74% of customers when they encounter ads that are not relevant to them, as reported by Infosys. This emphasizes the importance of personalization in marketing efforts. In order to effectively personalize marketing efforts, businesses often track and analyze the actions of consumers when they interact with websites or click on ads. However, creating completely personalized content for every individual is not practical due to the vast number of people and limited resources and time. In this study, a new approach has been used to segment customers based on the combination of RFM analysis and weighted k-means clustering to help an online retailer better target its customers. The results with weighted k-means are significantly higher with a silhouette score of 0.40 compared to 0.30 of the traditional k-means.

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2022K-Means Clustering Approach for Intelligent Customer Segmentation Using Customer Purchase Behavior Data 2012Data mining for the online retail industry: A case study of RFM model-based customer segmentation using data mining 2018RFM ranking – An effective approach to customer segmentation 2020An Empirical Study on Customer Segmentation by Purchase Behaviors Using a RFM Model and K-Means Algorithm 2020Clustering optimization in RFM analysis Based on k-Means 2018Customer Segmentation in XYZ Bank Using K-Means and K-Medoids Clustering 2018Market Segmentation Analysis and Visualization using K-Mode Clustering Algorithm for E-Commerce Business 2020Customer Segmentation Based on RFM Model Using K-Means, K-Medoids, and DBSCAN Methods

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