Retail Customer Churn Analysis using RFM Model and K-Means Clustering

Nikita Bagul · Prerana Berad · Chirag Khachane · Priya Surana

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

Abstract

In this current world of business, Customer Churn is one of the major concerns for various business owners or the organizations for maintaining existing and attracting new customers. Analysis of various types of customers can be conducted by researching customer relationship management which in turn provides strong support for business decisions. Customer churn occurs when certain customers are no longer loyal or a part of a particular business. Losing customers will not only result in losses but also develop threat to the organization. Because of multiple competitors in the same business, the re-engagement of customers who are less interested is essential rather than engaging a new one. It is observed that acquiring new buyers is costlier than retaining the present customer. Churn prediction is a new promising method in customer relationship management to analyze customer behavior by identifying customers with a high probability to discontinue the company based on analyzing their past data and also identify strategies for improvement. Once a customer becomes a churn, the loss incurred by the corporate isn't just the lost revenue but also the prices involved in additional marketing in order to attract new customer. Reducing customer churn is a key business goal. In this project, we've taken a dataset from UCI Machine Learning repository. This dataset contains records of transactions that happened between December 1, 2010 and December 1, 2011. This is recorded from a web retail gift store based in the United Kingdom. Here segmentation of customers has been done by using RFM technique and K-means algorithm.

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References

2019Customer churn prediction in telecom using machine learning in big data platform 2016A Survey on Customer Churn Prediction using Machine Learning Techniques 2018The Research of Online Shopping Customer Churn Prediction Based on Integrated Learning 2017Churn prediction analysis using various clustering algorithms in KNIME analytics platform 2018Predictive to Prescriptive Analysis for Customer Churn in Telecom Industry Using Hybrid Data Mining Techniques

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