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.
Cantu Medellin, Eduardo Carlos; Parikh, Mihir; Graves, Christopher; and Jones, Brendon
"Identifying At-Risk Clients for XYZ Packaging, Co.,"
SMU Data Science Review: Vol. 2:
3, Article 7.
Available at: https://scholar.smu.edu/datasciencereview/vol2/iss3/7
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