Profit-driven churn prediction for the mutual fund industry: A multisegment approach

Sebastian Maldonado, Gonzalo Dominguez, Diego Olaya, Wouter Verbeke

Research output: Contribution to journalArticle

Abstract

This paper proposes a novel approach for profit-based classification for churn prediction in the mutual fund industry. The maximum profit measure is redefined to address multiple segments that differ strongly in the average customer lifetime values (CLVs). The proposed multithreshold framework for churn prediction aims to maximize the profit of retention campaigns in binary classification settings. The multithreshold framework is empirically tested on data from a Chilean mutual fund company with varying and heterogeneous individual CLVs. Our results demonstrate the virtues of the proposed approach in achieving the best profit when compared to other metrics. Although presented in the context of investment companies, our framework can be implemented in any churn prediction task, representing an important contribution for decision-making in business analytics.
Original languageEnglish
Article number102380
Pages (from-to)1-28
Number of pages28
JournalOmega
Volume100
DOIs
Publication statusPublished - Apr 2021

Keywords

  • Profit metrics
  • Churn prediction
  • Mutual funds
  • Analytics
  • Finance

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