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International Conference on Industrial, Engineering and Other Applications of Applied Intelligent Systems

IEA/AIE 2013: Recent Trends in Applied Artificial Intelligence pp 411–420Cite as

Customer Churn Detection System: Identifying Customers Who Wish to Leave a Merchant

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Part of the Lecture Notes in Computer Science book series (LNAI,volume 7906)

Abstract

Identifying customers with a higher probability to leave a merchant (churn customers) is a challenging task for sellers. In this paper, we propose a system able to detect churner behavior and to assist merchants in delivering special offers to their churn customers. Two main goals lead our work: on the one hand, the definition of a classifier in order to perform churn analysis and, on the other hand, the definition of a framework that can be enriched with social information supporting the merchant in performing marketing actions which can reduce the probability of losing those customers. Experimental results of an artificial and a real datasets show an increased value of accuracy of the classification when random forest or decision tree are considered.

Keywords

  • Churn Analysis
  • Social commerce
  • Customer Lifetime Value
  • Decision Tree
  • Data Mining

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  • DOI: 10.1007/978-3-642-38577-3_42
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Author information

Authors and Affiliations

  1. Poste Italiane – Innovation and ICT Business Development, Research and Innovation - R&D Center, Piazza Matteotti 3, 80133, Naples, Italy

    Cosimo Birtolo, Vincenzo Diessa & Diego De Chiara

  2. CRMPA – Centro di Ricerca in Matematica Pura ed Applicata c/o DIEII Department of Electronic Engineering and Computer Engineering, University of Salerno, 84084, Fisciano, Italy

    Pierluigi Ritrovato

Authors
  1. Cosimo Birtolo
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  2. Vincenzo Diessa
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  3. Diego De Chiara
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  4. Pierluigi Ritrovato
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Editor information

Editors and Affiliations

  1. Department of Computer Science, Texas State University, 78666, San Marcos, TX, USA

    Moonis Ali

  2. Agent Systems Research Group, Department of Computer Science, Faculty of Sciences, VU University Amsterdam, De Boelelaan 1081, 1081, Amsterdam, HV, The Netherlands

    Tibor Bosse

  3. Interactive Intelligence Group, Department of Intelligent Systems, Faculty of Electrical Engineering, Mathematics and Computer Science, Delft University of Technology, Mekelweg 4, 2628 CD, Delft, The Netherlands

    Koen V. Hindriks & Catholijn M. Jonker & 

  4. Computational Intelligence Group, Department of Computer Science, Faculty of Sciences, VU University Amsterdam, De Boelelaan 1081, 1081 HV, Amsterdam, The Netherlands

    Mark Hoogendoorn

  5. Agent Systems Research Group, Department of Computer Science, Faculty of Sciences, VU University Amsterdam, De Boelelaan 1081, 1081 HV, Amsterdam, The Netherlands

    Jan Treur

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Birtolo, C., Diessa, V., De Chiara, D., Ritrovato, P. (2013). Customer Churn Detection System: Identifying Customers Who Wish to Leave a Merchant. In: Ali, M., Bosse, T., Hindriks, K.V., Hoogendoorn, M., Jonker, C.M., Treur, J. (eds) Recent Trends in Applied Artificial Intelligence. IEA/AIE 2013. Lecture Notes in Computer Science(), vol 7906. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-38577-3_42

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  • DOI: https://doi.org/10.1007/978-3-642-38577-3_42

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-642-38576-6

  • Online ISBN: 978-3-642-38577-3

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