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

Arno de Caigny (), Kristof Coussement (), Koen W. de Bock () and Stefan Lessmann
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Arno de Caigny: IESEG - School of Management (LEM), LEM - Lille économie management - UMR 9221 - UA - Université d'Artois - UCL - Université catholique de Lille - Université de Lille - CNRS - Centre National de la Recherche Scientifique
Kristof Coussement: LEM - Lille économie management - UMR 9221 - UA - Université d'Artois - UCL - Université catholique de Lille - Université de Lille - CNRS - Centre National de la Recherche Scientifique
Koen W. de Bock: Audencia Recherche - Audencia Business School

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Abstract: 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.

Keywords: Customer relationship management; Text mining; Predictive modeling; Deep learning; Financial services industry (search for similar items in EconPapers)
Date: 2019-08-21
Note: View the original document on HAL open archive server: https://audencia.hal.science/hal-02275958
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Citations: View citations in EconPapers (3)

Published in International Journal of Forecasting, 2019, ⟨10.1016/j.ijforecast.2019.03.029⟩

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Persistent link: https://EconPapers.repec.org/RePEc:hal:journl:hal-02275958

DOI: 10.1016/j.ijforecast.2019.03.029

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