The Relevant Length of Customer Event History for Churn Prediction: How long is long enough?

M. Ballings and Dirk Van den Poel

Working Papers of Faculty of Economics and Business Administration, Ghent University, Belgium from Ghent University, Faculty of Economics and Business Administration

Abstract: The key question of this study is: How long should the length of customer event history be for customer churn prediction? While most studies in predictive churn modeling aim to improve models by data augmentation or algorithm improvement, this study focuses on a another dimension: time window optimization with respect to predictive performance. This paper first presents a formalization of the time window selection strategy, along with a literature review. Next, using logistic regression, classification trees and bagging in combination with classification trees, this study analyzes the improvement in churn-model performance by extending customer event history from 1 to 16 years. The results show that, after the 5th additional year, predictive performance is only marginally increased, meaning that the company in this study can discard 69% of its data with almost no decrease in predictive performance. The practical implication is that analysts can substantially decrease datarelated burdens, such as data storage, preparation and analysis. This is particularly valuable in times of big data where computational complexity is paramount.

Keywords: Predictive Analytics; Time window; Length of customer event history; predictive customer churn model (search for similar items in EconPapers)
Pages: 11 pages
Date: 2012-08
New Economics Papers: this item is included in nep-for
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Citations: View citations in EconPapers (10)

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Persistent link: https://EconPapers.repec.org/RePEc:rug:rugwps:12/804

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