Identifying At-Risk Clients for XYZ Packaging, Co.

Authors

Eduardo Carlos Cantu Medellin, Southern Methodist UniversityFollow
Mihir Parikh, Southern Methodist UniversityFollow
Christopher GravesFollow
Brendon Jones, XYZ PackagingFollow

Abstract

We present a multi-algorithmic modeling approach for the identification of at-risk customers for XYZ Packaging Inc. We define at-risk customers as those having declining seasonally adjusted gross income forecasts which are a strong indicator of impending customer churn. Customer retention is an area of interest regardless of industry but is especially vital in commodity-based low margin industries. We employ traditional Autoregressive Integrated Moving Average (ARIMA) and Anomaly Detection algorithms for discriminating changes in customer revenue patterns. Ultimately, we identify a meaningful proportion of clients whose forward-looking quarterly demand can be predicted within an actionable degree of accuracy.

Recommended Citation

Cantu Medellin, Eduardo Carlos; Parikh, Mihir; Graves, Christopher; and Jones, Brendon (2019) "Identifying At-Risk Clients for XYZ Packaging, Co.," SMU Data Science Review: Vol. 2: No. 3, Article 7.
Available at: https://scholar.smu.edu/datasciencereview/vol2/iss3/7

Creative Commons License

This work is licensed under a Creative Commons Attribution-Noncommercial 4.0 License