The University of Southampton
University of Southampton Institutional Repository

Profit maximizing logistic model for customer churn prediction using genetic algorithms

Profit maximizing logistic model for customer churn prediction using genetic algorithms
Profit maximizing logistic model for customer churn prediction using genetic algorithms

To detect churners in a vast customer base, as is the case with telephone service providers, companies heavily rely on predictive churn models to remain competitive in a saturated market. In previous work, the expected maximum profit measure for customer churn (EMPC) has been proposed in order to determine the most profitable churn model. However, profit concerns are not directly integrated into the model construction. Therefore, we present a classifier, named ProfLogit, that maximizes the EMPC in the training step using a genetic algorithm, where ProfLogit's interior model structure resembles a lasso-regularized logistic model. Additionally, we introduce threshold-independent recall and precision measures based on the expected profit maximizing fraction, which is derived from the EMPC framework. Our proposed technique aims to construct profitable churn models for retention campaigns to satisfy the business requirement of profit maximization. In a benchmark study with nine real-life data sets, ProfLogit exhibits the overall highest, out-of-sample EMPC performance as well as the overall best, profit-based precision and recall values. As a result of the lasso resemblance, ProfLogit also performs a profit-based feature selection in which features are selected that would otherwise be excluded with an accuracy-based measure, which is another noteworthy finding.

Customer churn prediction, Data mining, Lasso-regularized logistic regression model, Profit-based model evaluation, Real-coded genetic algorithm
2210-6502
116-130
Stripling, Eugen
10c20791-45b8-48da-941f-3b3afb926fa9
vanden Broucke, Seppe
7f35cbae-f279-4aa5-ad57-bcb94ed8784e
Antonio, Katrien
21b868fb-debc-495c-87bb-f31f9ae2e79a
Baesens, Bart
f7c6496b-aa7f-4026-8616-ca61d9e216f0
Snoeck, Monique
9aee96bc-8a57-4c37-bcd7-e83f0b173ee1
Stripling, Eugen
10c20791-45b8-48da-941f-3b3afb926fa9
vanden Broucke, Seppe
7f35cbae-f279-4aa5-ad57-bcb94ed8784e
Antonio, Katrien
21b868fb-debc-495c-87bb-f31f9ae2e79a
Baesens, Bart
f7c6496b-aa7f-4026-8616-ca61d9e216f0
Snoeck, Monique
9aee96bc-8a57-4c37-bcd7-e83f0b173ee1

Stripling, Eugen, vanden Broucke, Seppe, Antonio, Katrien, Baesens, Bart and Snoeck, Monique (2018) Profit maximizing logistic model for customer churn prediction using genetic algorithms. Swarm and Evolutionary Computation, 40, 116-130. (doi:10.1016/j.swevo.2017.10.010).

Record type: Article

Abstract

To detect churners in a vast customer base, as is the case with telephone service providers, companies heavily rely on predictive churn models to remain competitive in a saturated market. In previous work, the expected maximum profit measure for customer churn (EMPC) has been proposed in order to determine the most profitable churn model. However, profit concerns are not directly integrated into the model construction. Therefore, we present a classifier, named ProfLogit, that maximizes the EMPC in the training step using a genetic algorithm, where ProfLogit's interior model structure resembles a lasso-regularized logistic model. Additionally, we introduce threshold-independent recall and precision measures based on the expected profit maximizing fraction, which is derived from the EMPC framework. Our proposed technique aims to construct profitable churn models for retention campaigns to satisfy the business requirement of profit maximization. In a benchmark study with nine real-life data sets, ProfLogit exhibits the overall highest, out-of-sample EMPC performance as well as the overall best, profit-based precision and recall values. As a result of the lasso resemblance, ProfLogit also performs a profit-based feature selection in which features are selected that would otherwise be excluded with an accuracy-based measure, which is another noteworthy finding.

Text
Profit Maximizing Logistic Model for Customer Churn Prediction Using Genetic Algorithms - Accepted Manuscript
Download (1MB)

More information

Accepted/In Press date: 19 October 2017
e-pub ahead of print date: 21 December 2017
Published date: 1 June 2018
Keywords: Customer churn prediction, Data mining, Lasso-regularized logistic regression model, Profit-based model evaluation, Real-coded genetic algorithm

Identifiers

Local EPrints ID: 421399
URI: http://eprints.soton.ac.uk/id/eprint/421399
ISSN: 2210-6502
PURE UUID: bef0f746-7b8b-4ea2-ab2b-7d3421cc9aa2
ORCID for Bart Baesens: ORCID iD orcid.org/0000-0002-5831-5668

Catalogue record

Date deposited: 08 Jun 2018 16:30
Last modified: 07 Oct 2020 06:32

Export record

Altmetrics

Contributors

Author: Eugen Stripling
Author: Seppe vanden Broucke
Author: Katrien Antonio
Author: Bart Baesens ORCID iD
Author: Monique Snoeck

University divisions

Download statistics

Downloads from ePrints over the past year. Other digital versions may also be available to download e.g. from the publisher's website.

View more statistics

Atom RSS 1.0 RSS 2.0

Contact ePrints Soton: eprints@soton.ac.uk

ePrints Soton supports OAI 2.0 with a base URL of http://eprints.soton.ac.uk/cgi/oai2

This repository has been built using EPrints software, developed at the University of Southampton, but available to everyone to use.

We use cookies to ensure that we give you the best experience on our website. If you continue without changing your settings, we will assume that you are happy to receive cookies on the University of Southampton website.

×