Improved marketing decision making in a customer churn prediction context using generalized additive models

Authors:
Kristof Coussement;Dries F. Benoit;Dirk Van den Poel
Affiliations:
University College HUBrussel, Faculty of Economics and Management, Stormstraat 2, B-1000 Brussels, Belgium and Ghent University, Faculty of Economics and Business Administration, Department of Mar ...;Ghent University, Faculty of Economics and Business Administration, Department of Marketing, Tweekerkenstraat 2, B-9000 Ghent, Belgium;Ghent University, Faculty of Economics and Business Administration, Department of Marketing, Tweekerkenstraat 2, B-9000 Ghent, Belgium
Venue:
Expert Systems with Applications: An International Journal
Year:
2010

Citing 10
Cited 9

Knowledge management and data mining for marketing

Decision Support Systems - Knowledge management support of decision making
Evaluation of prediction models for marketing campaigns

Proceedings of the seventh ACM SIGKDD international conference on Knowledge discovery and data mining
Logistic Regression Using the SAS System: Theory and Application

Logistic Regression Using the SAS System: Theory and Application
A Nonparametric Approach to Identifying Latent Relationships in Hierarchical Models

Marketing Science
An introduction to variable and feature selection

The Journal of Machine Learning Research
Competitive One-to-One Promotions

Management Science
Toward a successful CRM: variable selection, sampling, and ensemble

Decision Support Systems
Identifying issues in customer relationship management at Merck-Medco

Decision Support Systems
Towards a holistic perspective of customer relationship management (CRM) implementation: a case study of the housing and development board, Singapore

Decision Support Systems
Churn prediction in subscription services: An application of support vector machines while comparing two parameter-selection techniques

Expert Systems with Applications: An International Journal

An empirical evaluation of rotation-based ensemble classifiers for customer churn prediction

Expert Systems with Applications: An International Journal
Time-varying effects in the analysis of customer loyalty: A case study in insurance

Expert Systems with Applications: An International Journal
Reconciling performance and interpretability in customer churn prediction using ensemble learning based on generalized additive models

Expert Systems with Applications: An International Journal
Insolvency modeling in the cellular telecommunication industry

Expert Systems with Applications: An International Journal
Improving customer retention in financial services using kinship network information

Expert Systems with Applications: An International Journal
Including spatial interdependence in customer acquisition models: A cross-category comparison

Expert Systems with Applications: An International Journal
Customer event history for churn prediction: How long is long enough?

Expert Systems with Applications: An International Journal
Determination of Algorithms Making Balance Between Accuracy and Comprehensibility in Churn Prediction Setting

International Journal of Information Retrieval Research
Mobile phone customer retention strategies and Chinese e-commerce

Electronic Commerce Research and Applications

Quantified Score

Hi-index	12.06

Visualization

Abstract

Nowadays, companies are investing in a well-considered CRM strategy. One of the cornerstones in CRM is customer churn prediction, where one tries to predict whether or not a customer will leave the company. This study focuses on how to better support marketing decision makers in identifying risky customers by using Generalized Additive Models (GAM). Compared to Logistic Regression, GAM relaxes the linearity constraint which allows for complex non-linear fits to the data. The contributions to the literature are three-fold: (i) it is shown that GAM is able to improve marketing decision making by better identifying risky customers; (ii) it is shown that GAM increases the interpretability of the churn model by visualizing the non-linear relationships with customer churn identifying a quasi-exponential, a U, an inverted U or a complex trend and (iii) marketing managers are able to significantly increase business value by applying GAM in this churn prediction context.