SMU Data Science Review
Abstract
This paper presents a comparative study on machine learning methods as they are applied to product associations, future purchase predictions, and predictions of customer churn in aftermarket operations. Association rules are used help to identify patterns across products and find correlations in customer purchase behaviour. Studying customer behaviour as it pertains to Recency, Frequency, and Monetary Value (RFM) helps inform customer segmentation and identifies customers with propensity to churn. Lastly, Flowserve’s customer purchase history enables the establishment of churn thresholds for each customer group and assists in constructing a model to predict future churners. The aim of this model is to assist Flowserve in creating a targeted retention strategy to individual customers, with respect to individual customers and priority. Each aspect of the analysis requires a specialized set of tools that will be discussed in detail throughout the paper. The study is based on data from Flowserve’s product sales history spanning years from 2014 through 2019. The data includes date of purchase, price, quantity, item description and location of customers.
Recommended Citation
Briker, Vitaly; Farrow, Richard; Trevino, William; and Allen, Brent
(2019)
"Identifying Customer Churn in After-market Operations using Machine Learning Algorithms,"
SMU Data Science Review: Vol. 2:
No.
3, Article 6.
Available at:
https://scholar.smu.edu/datasciencereview/vol2/iss3/6
Creative Commons License
This work is licensed under a Creative Commons Attribution-Noncommercial 4.0 License
Included in
Other Computer Engineering Commons, Other Statistics and Probability Commons, Statistical Models Commons
