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A Mathematical Model for Customer Lifetime Value Based Offer Management

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Data Management Technologies and Applications (DATA 2017)

Part of the book series: Communications in Computer and Information Science ((CCIS,volume 814))

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Abstract

Customers with prepaid lines possess higher attrition risk compared to postpaid customers, since prepaid customers do not sign long-term obligatory contracts and may churn anytime. For this reason, mobile operators have to offer engaging benefits to keep prepaid subscribers with the company. Since all such offers incur additional cost, mobile operators face an optimization problem while selecting the most suitable offers for customers at risk. In this study, an offer management framework targeting prepaid customers of a telecommunication company is developed. Proposed framework chooses the most suitable offer for each customer through a mathematical model, which utilizes customer lifetime value and churn risk. Lifetime values are estimated using logistic regression and Pareto/NBD models, and several variants of these models are used to predict churn risks using a large number of customer specific features.

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Authors and Affiliations

  1. Department of Industrial Engineering, Ozyegin University, Istanbul, Turkey

    Ahmet Şahin, Zehra Can & Erinc Albey

  2. Turkcell Technology Research and Development Inc., Istanbul, Turkey

    Zehra Can

Authors
  1. Ahmet Şahin
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  2. Zehra Can
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  3. Erinc Albey
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Corresponding authors

Correspondence to Ahmet Şahin , Zehra Can or Erinc Albey .

Editor information

Editors and Affiliations

  1. INSTICC, Polytechnic Institute of Setúbal, Setúbal, Portugal

    Joaquim Filipe

  2. University of Coimbra, Coimbra, Portugal

    Jorge Bernardino

  3. RWTH Aachen University, Aachen, Germany

    Christoph Quix

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Şahin, A., Can, Z., Albey, E. (2018). A Mathematical Model for Customer Lifetime Value Based Offer Management. In: Filipe, J., Bernardino, J., Quix, C. (eds) Data Management Technologies and Applications. DATA 2017. Communications in Computer and Information Science, vol 814. Springer, Cham. https://doi.org/10.1007/978-3-319-94809-6_2

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  • DOI: https://doi.org/10.1007/978-3-319-94809-6_2

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-94808-9

  • Online ISBN: 978-3-319-94809-6

  • eBook Packages: Computer ScienceComputer Science (R0)

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