Will they stay or will they go? Predicting customer churn in the energy sector

Michela Vezzoli, Cristina Zogmaister and Dirk Van den Poel
Additional contact information
Michela Vezzoli: Postdoctoral Research Fellow, University of Milano Bicocca, Italy
Cristina Zogmaister: Associate Professor in Psychometrics, University of Milano Bicocca, Italy

Applied Marketing Analytics: The Peer-Reviewed Journal, 2020, vol. 6, issue 2, 136-150

Abstract: The liberalisation of the European energy market has driven changes in the way firms approach marketing, both for the acquisition of new consumers and for retaining existing ones. To retain consumers, practitioners aim to predict which consumers intend to churn (ie leave), and to understand the reasons behind this intention. To address this need, this study uses data-mining techniques to develop a churn prediction model. The study aims to identify the information that is predictive of churn and, consequently, to shed light on the psychological reasons behind churn. The authors built eight predictive models using decision trees, random forest and logistic regression on a dataset composed of 81,813 consumers of an energy provider, each with one residential electricity contract. The logistic regression was found to outperform the other methods. The discussion focuses on the relevant predictors of churn by addressing a posteriori psychological explanations of consumers’ churn behaviour. The study provides new insights on the reasons why customers churn and, by addressing theoretical psychological explanations, provides a data-mining model with robustness to contextual changes.

Keywords: churn prediction model; customer churn; consumer psychology; machine learning; energy market (search for similar items in EconPapers)
JEL-codes: M3 (search for similar items in EconPapers)
Date: 2020
References: Add references at CitEc
Citations:

Downloads: (external link)
https://hstalks.com/article/5967/download/ (application/pdf)
https://hstalks.com/article/5967/ (text/html)
Requires a paid subscription for full access.

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:aza:ama000:y:2020:v:6:i:2:p:136-150

Access Statistics for this article

More articles in Applied Marketing Analytics: The Peer-Reviewed Journal from Henry Stewart Publications
Bibliographic data for series maintained by Henry Stewart Talks ().