Decision Tree: Customer churn analysis for a loyalty program using data mining algorithm

Lee, Angela Siew Hoong * and Zuraini Zainol, and Ng, Claudia and Chan, Khin Whai (2019) Decision Tree: Customer churn analysis for a loyalty program using data mining algorithm. In: Soft Computing in Data Science: 5th International Conference, SCDS 2019 Proceedings, August 28–29, 2019, Iizuka, Japan.

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Abstract

In the world of retailer, customers typically patronize multiple shops thus making loyalty programs a favorite among retailer to retain their customers. Loyalty programs are utilized across many different businesses as a marketing strategy to encourage customers to continuously shop or patronize the services provided by a certain organization. However, one of the biggest problem faced by these businesses is customer churn. The purpose of this research was to build a predictive model, which could predict customer churn, where visualization of data was generated to better understand the existing members and see the patterns and behavior demonstrated by members of the loyalty program. Through these, meaningful insights about the businesses’ analysis on customers could be gathered and utilized for better actions which could be taken to address the issues which the company faces. At the end, based on the issues found, strategies were proposed to address the issues found. For this research, SAS Enterprise Miner was used to perform predictive analysis while Tableau was used to perform descriptive analysis.

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