Improved Marketing Decision Making in a Customer Churn Prediction Context Using Generalized Additive Models
Kristof Coussement (),
Dries Frederik Benoit () and
Dirk Van den Poel ()
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Dries Frederik Benoit: Ghent University, Faculty of Economics and Business Administration, Department of Marketing, B-9000 Ghent, Belgium.
No 2009/18, Working Papers from Hogeschool-Universiteit Brussel, Faculteit Economie en Management
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.
Keywords: customer relationship management (CRM); churn modeling; marketing decision making; generalized additive models (GAM) (search for similar items in EconPapers)
Pages: 35 page
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Working Paper: Improved marketing decision making in a customer churn prediction context using generalized additive models (2010)
Working Paper: Improved Marketing Decision Making in a Customer Churn Prediction Context Using Generalized Additive Models (2009)
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