Benoit, Dries

Coussement, Kristof

Van den Poel, Dirk

English

Ghent University Academic Bibliography

  PREVENTING CUSTOMERS FROM RUNNING AWAY!  EXPLORING GENERALIZED ADDITIVE MODELS FOR CUSTOMER CHURN PREDICTION   Kristof Coussement,  IESEG School of Management, 3 Rue de la Digue, F-59000 Lille, France.  Dries Frederik Benoit, Ghent University, Tweekerkenstraat 2, B-9000 Ghent, Belgium. Dirk Van den Poel, Ghent University, Tweekerkenstraat 2, B-9000 Ghent, Belgium.  ABSTRACT  Nowadays, companies invest in a well-considered Customer Relationship Management strategy. One of the cornerstones of CRM is customer churn prediction, where one tries to predict whether or not a customer will leave the company. This study focuses on how better to support marketing decision makers in identifying risky customers by using Generalized Additive Models (GAM). Compared with logistic regression, a GAM relaxes the linearity constraint that allows for complex non-linear fits to the data. The contributions to the literature are threefold: (i) it is shown that a GAM is able to improve marketing decision making by better identifying risky customers; (ii) it is shown that a 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 increase business value significantly by applying a GAM in this churn prediction context.  References Available on Request. 238

Preventing customers from running away! Exploring generalized additive models for customer churn prediction

Kristof Coussement

Dries Frederik Benoit

Dirk Van den Poel

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Preventing Customers from Running Away! Exploring Generalized Additive Models for Customer Churn Prediction

https://biblio.ugent.be/publication/5749329/file/5749977

Preventing customers from running away! Exploring generalized additive models for customer churn prediction

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Ghent University Academic Bibliography

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