Title :

Incorporating textual information in customer churn prediction models based on a convolutional neural network

Author(s) :

De Caigny, Arno [Auteur]
Lille économie management - UMR 9221 [LEM]
Coussement, Kristof [Auteur]
Lille économie management - UMR 9221 [LEM]
de Bock, Koen W. [Auteur]
Audencia Recherche
Lessmann, Stefan [Auteur]

Journal title :

International Journal of Forecasting

Publisher :

Elsevier

Publication date :

2019-08-21

ISSN :

0169-2070

English keyword(s) :

Customer relationship management
Text mining
Predictive modeling
Deep learning
Financial services industry

HAL domain(s) :

Sciences de l'Homme et Société/Gestion et management
Sciences de l'Homme et Société/Méthodes et statistiques
Statistiques [stat]/Machine Learning [stat.ML]

English abstract : [en]

This study investigates the value added by incorporating textual data into customer churn prediction (CCP) models. It extends the previous literature by benchmarking convolutional neural networks (CNNs) against current ...
Show more >This study investigates the value added by incorporating textual data into customer churn prediction (CCP) models. It extends the previous literature by benchmarking convolutional neural networks (CNNs) against current best practices for analyzing textual data in CCP, and, using real life data from a European financial services provider, validates a framework that explains how textual data can be incorporated in a predictive model. First, the results confirm previous research showing that the inclusion of textual data in a CCP model improves its predictive performance. Second, CNNs outperform current best practices for text mining in CCP. Third, textual data are an important source of data for CCP, but unstructured textual data alone cannot create churn prediction models that are competitive with models that use traditional structured data. A calculation of the additional profit obtained from a customer retention campaign through the inclusion of textual information can be used by practitioners directly to help them make more informed decisions on whether to invest in text mining.Show less >

Language :

Anglais

Popular science :

Non

Collections :

• Lille Économie Management (LEM) - UMR 9221

Source :

Harvested from HAL