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Profit driven decision trees for churn prediction

Profit driven decision trees for churn prediction
Profit driven decision trees for churn prediction

Customer retention campaigns increasingly rely on predictive models to detect potential churners in a vast customer base. From the perspective of machine learning, the task of predicting customer churn can be presented as a binary classification problem. Using data on historic behavior, classification algorithms are built with the purpose of accurately predicting the probability of a customer defecting. The predictive churn models are then commonly selected based on accuracy related performance measures such as the area under the ROC curve (AUC). However, these models are often not well aligned with the core business requirement of profit maximization, in the sense that, the models fail to take into account not only misclassification costs, but also the benefits originating from a correct classification. Therefore, the aim is to construct churn prediction models that are profitable and preferably interpretable too. The recently developed expected maximum profit measure for customer churn (EMPC) has been proposed in order to select the most profitable churn model. We present a new classifier that integrates the EMPC metric directly into the model construction. Our technique, called ProfTree, uses an evolutionary algorithm for learning profit driven decision trees. In a benchmark study with real-life datasets from various telecommunication service providers, we show that ProfTree achieves significant profit improvements compared to classic accuracy driven tree-based methods.

Artificial intelligence, Classification, Customer churn prediction, Evolutionary algorithm, Profit-based model evaluation
0377-2217
Höppner, Sebastiaan
26ec8e7e-f6ef-49e7-84fc-a01943ba6a46
Stripling, Eugen
10c20791-45b8-48da-941f-3b3afb926fa9
Baesens, Bart
f7c6496b-aa7f-4026-8616-ca61d9e216f0
Broucke, Seppe vanden
0b17d31c-7378-4aa6-a1a8-715ddd08b3b5
Verdonck, Tim
8558b8f8-d412-4fb9-9784-9aba1d7323b6
Höppner, Sebastiaan
26ec8e7e-f6ef-49e7-84fc-a01943ba6a46
Stripling, Eugen
10c20791-45b8-48da-941f-3b3afb926fa9
Baesens, Bart
f7c6496b-aa7f-4026-8616-ca61d9e216f0
Broucke, Seppe vanden
0b17d31c-7378-4aa6-a1a8-715ddd08b3b5
Verdonck, Tim
8558b8f8-d412-4fb9-9784-9aba1d7323b6

Höppner, Sebastiaan, Stripling, Eugen, Baesens, Bart, Broucke, Seppe vanden and Verdonck, Tim (2018) Profit driven decision trees for churn prediction. European Journal of Operational Research. (doi:10.1016/j.ejor.2018.11.072).

Record type: Article

Abstract

Customer retention campaigns increasingly rely on predictive models to detect potential churners in a vast customer base. From the perspective of machine learning, the task of predicting customer churn can be presented as a binary classification problem. Using data on historic behavior, classification algorithms are built with the purpose of accurately predicting the probability of a customer defecting. The predictive churn models are then commonly selected based on accuracy related performance measures such as the area under the ROC curve (AUC). However, these models are often not well aligned with the core business requirement of profit maximization, in the sense that, the models fail to take into account not only misclassification costs, but also the benefits originating from a correct classification. Therefore, the aim is to construct churn prediction models that are profitable and preferably interpretable too. The recently developed expected maximum profit measure for customer churn (EMPC) has been proposed in order to select the most profitable churn model. We present a new classifier that integrates the EMPC metric directly into the model construction. Our technique, called ProfTree, uses an evolutionary algorithm for learning profit driven decision trees. In a benchmark study with real-life datasets from various telecommunication service providers, we show that ProfTree achieves significant profit improvements compared to classic accuracy driven tree-based methods.

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ProfTree - Accepted Manuscript
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More information

Accepted/In Press date: 28 November 2018
e-pub ahead of print date: 30 November 2018
Keywords: Artificial intelligence, Classification, Customer churn prediction, Evolutionary algorithm, Profit-based model evaluation

Identifiers

Local EPrints ID: 427452
URI: http://eprints.soton.ac.uk/id/eprint/427452
ISSN: 0377-2217
PURE UUID: c764bff1-e358-43c9-81e8-eac56a6f3e3e
ORCID for Bart Baesens: ORCID iD orcid.org/0000-0002-5831-5668

Catalogue record

Date deposited: 16 Jan 2019 17:30
Last modified: 28 Apr 2022 05:37

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Contributors

Author: Sebastiaan Höppner
Author: Eugen Stripling
Author: Bart Baesens ORCID iD
Author: Seppe vanden Broucke
Author: Tim Verdonck

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