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Improving Customer Churn Prediction by Data Augmentation Using Pictorial Stimulus-Choice Data

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Management Intelligent Systems

Part of the book series: Advances in Intelligent Systems and Computing ((AISC,volume 171))

Abstract

The purpose of this paper is to determine the added value of pictorial stimulus-choice data in customer churn prediction. Using Random Forests and 5 times 2 fold cross-validation, this study analyzes how much pictorial stimulus – choice data and survey data increase the AUC of a churn model over and above administrative, operational and complaints data. The finding is that pictorial-stimulus choice data significantly increases AUC of models with administrative and operational data. The practical implication of this finding is that companies should start considering mining pictorial data from social media sites (e.g. Pinterest), in order to augment their internal customer database. This study is original in that it is the first that assesses the added value of pictorial stimulus-choice data in predictive models. This is important because more and more social media websites are focusing on pictures.

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Author information

Authors and Affiliations

  1. Faculty of Economics and Business Administration, Department of Marketing, Ghent University, Tweekerkenstraat 2, B-9000, Ghent, Belgium

    Michel Ballings & Dirk Van den Poel

  2. Psilogy, Leopold De Waelstraat 17a, 2000, Antwerp, Belgium

    Emmanuel Verhagen

Authors
  1. Michel Ballings
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  2. Dirk Van den Poel
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  3. Emmanuel Verhagen
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Corresponding author

Correspondence to Michel Ballings .

Editor information

Editors and Affiliations

  1. Dept. Computer Science & A.I., University of Granada, Granada, 18071, Spain

    Jorge Casillas

  2. Dept. of Management, University of Granada, Granada, 18071, Spain

    Francisco J. Martínez-López

  3. Department of Computing Science, and Control, Universidad Salamanca, Plaza de la Merced, s/n s/n, Salamanca, 37008, Spain

    Juan Manuel Corchado Rodríguez

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Ballings, M., Van den Poel, D., Verhagen, E. (2012). Improving Customer Churn Prediction by Data Augmentation Using Pictorial Stimulus-Choice Data. In: Casillas, J., Martínez-López, F., Corchado Rodríguez, J. (eds) Management Intelligent Systems. Advances in Intelligent Systems and Computing, vol 171. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-30864-2_21

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  • DOI: https://doi.org/10.1007/978-3-642-30864-2_21

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-642-30863-5

  • Online ISBN: 978-3-642-30864-2

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