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In pursuit of enhanced customer retention management : Review, key issues, and future directions

Author

Listed:
  • Ascarza,
  • Neslin,
  • Netzer,
  • Lemmens, Aurélie

    (Tilburg University, School of Economics and Management)

  • Anderson, Zachery
  • Fader, Peter S.
  • Gupta, S.
  • Hardie, B.G.S.
  • Libai, Barak
  • Neal, David
  • Provost, Foster

Abstract

No abstract is available for this item.

Suggested Citation

  • Ascarza, & Neslin, & Netzer, & Lemmens, Aurélie & Anderson, Zachery & Fader, Peter S. & Gupta, S. & Hardie, B.G.S. & Libai, Barak & Neal, David & Provost, Foster, 2018. "In pursuit of enhanced customer retention management : Review, key issues, and future directions," Other publications TiSEM 28a90d28-6daf-42f1-bd8e-e, Tilburg University, School of Economics and Management.
    • Handle: RePEc:tiu:tiutis:28a90d28-6daf-42f1-bd8e-e26cf11ac0b9
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    References listed on IDEAS

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    1. Nitzan Irit & Libai Barak, 2013. "If You Go, I Will Follow … Social Effects on the Decision to Terminate a Service," GfK Marketing Intelligence Review, Sciendo, vol. 5(2), pages 40-45, November.
    2. Haenlein, Michael, 2013. "Social interactions in customer churn decisions: The impact of relationship directionality," International Journal of Research in Marketing, Elsevier, vol. 30(3), pages 236-248.
    3. John D. C. Little, 1970. "Models and Managers: The Concept of a Decision Calculus," Management Science, INFORMS, vol. 16(8), pages 466-485, April.
    4. Simon, Noah & Friedman, Jerome H. & Hastie, Trevor & Tibshirani, Rob, 2011. "Regularization Paths for Cox's Proportional Hazards Model via Coordinate Descent," Journal of Statistical Software, Foundation for Open Access Statistics, vol. 39(i05).
    5. Gianna Giudicati & Massimo Riccaboni & Anna Romiti, 2013. "Experience, socialization and customer retention: Lessons from the dance floor," Marketing Letters, Springer, vol. 24(4), pages 409-422, December.
    6. Peter S. Fader & Bruce G. S. Hardie & Ka Lok Lee, 2005. "“Counting Your Customers” the Easy Way: An Alternative to the Pareto/NBD Model," Marketing Science, INFORMS, vol. 24(2), pages 275-284, August.
    7. Andrés Musalem & Yogesh V. Joshi, 2009. "—How Much Should You Invest in Each Customer Relationship? A Competitive Strategic Approach," Marketing Science, INFORMS, vol. 28(3), pages 555-565, 05-06.
    8. Anton Ovchinnikov & Béatrice Boulu-Reshef & Phillip E. Pfeifer, 2014. "Balancing Acquisition and Retention Spending for Firms with Limited Capacity," Post-Print hal-01261385, HAL.
    9. Peter S. Fader & Bruce G. S. Hardie & Jen Shang, 2010. "Customer-Base Analysis in a Discrete-Time Noncontractual Setting," Marketing Science, INFORMS, vol. 29(6), pages 1086-1108, 11-12.
    10. David A. Schweidel & George Knox, 2013. "Incorporating Direct Marketing Activity into Latent Attrition Models," Marketing Science, INFORMS, vol. 32(3), pages 471-487, May.
    11. Datta, H. & Foubert, B. & van Heerde, H.J., 2014. "The challenge of retaining customers acquired with free trials," Other publications TiSEM 7bc7f195-f655-43cd-9232-7, Tilburg University, School of Economics and Management.
    12. Anton Ovchinnikov & Béatrice Boulu-Reshef & Phillip E. Pfeifer, 2014. "Balancing Acquisition and Retention Spending for Firms with Limited Capacity," Management Science, INFORMS, vol. 60(8), pages 2002-2019, August.
    13. K. Coussement & D. Van Den Poel, 2008. "Improving Customer Attrition Prediction by Integrating Emotions from Client/Company Interaction Emails and Evaluating Multiple Classifiers," Working Papers of Faculty of Economics and Business Administration, Ghent University, Belgium 08/527, Ghent University, Faculty of Economics and Business Administration.
    14. Peter S. Fader & Bruce G. S. Hardie, 2010. "Customer-Base Valuation in a Contractual Setting: The Perils of Ignoring Heterogeneity," Marketing Science, INFORMS, vol. 29(1), pages 85-93, 01-02.
    15. Bogomolova, Svetlana, 2010. "Life after death? Analyzing post-defection consumer brand equity," Journal of Business Research, Elsevier, vol. 63(11), pages 1135-1141, November.
    16. Doreén Pick & Jacquelyn S. Thomas & Sebastian Tillmanns & Manfred Krafft, 2016. "Customer win-back: the role of attributions and perceptions in customers’ willingness to return," Journal of the Academy of Marketing Science, Springer, vol. 44(2), pages 218-240, March.
    17. Lemmens, A. & Croux, C., 2006. "Bagging and boosting classification trees to predict churn," Other publications TiSEM d5cb664d-5859-44db-a621-e, Tilburg University, School of Economics and Management.
    18. Hui Zou & Trevor Hastie, 2005. "Addendum: Regularization and variable selection via the elastic net," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 67(5), pages 768-768, November.
    19. Homburg, Christian & Hoyer, Wayne & Stock, Ruth, 2007. "How to Get Lost Customers Back? A Study of Antecedents of Relationship Revival," Publications of Darmstadt Technical University, Institute for Business Studies (BWL) 60474, Darmstadt Technical University, Department of Business Administration, Economics and Law, Institute for Business Studies (BWL).
    20. Fader, Peter S. & Hardie, Bruce G.S., 2009. "Probability Models for Customer-Base Analysis," Journal of Interactive Marketing, Elsevier, vol. 23(1), pages 61-69.
    21. David C. Schmittlein & Donald G. Morrison & Richard Colombo, 1987. "Counting Your Customers: Who-Are They and What Will They Do Next?," Management Science, INFORMS, vol. 33(1), pages 1-24, January.
    22. Hui Zou & Trevor Hastie, 2005. "Regularization and variable selection via the elastic net," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 67(2), pages 301-320, April.
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    Cited by:

    1. Koen W. de Bock & Arno de Caigny, 2021. "Spline-rule ensemble classifiers with structured sparsity regularization for interpretable customer churn modeling," Post-Print hal-03391564, HAL.
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    3. Aurélie Lemmens & Sunil Gupta, 2020. "Managing Churn to Maximize Profits," Marketing Science, INFORMS, vol. 39(5), pages 956-973, September.
    4. Elliot Shin Oblander & Sunil Gupta & Carl F. Mela & Russell S. Winer & Donald R. Lehmann, 2020. "The past, present, and future of customer management," Marketing Letters, Springer, vol. 31(2), pages 125-136, September.
    5. Liu-Thompkins, Yuping & Khoshghadam, Leila & Attar Shoushtari, Arjang & Zal, Saeed, 2022. "What drives retailer loyalty? A meta-analysis of the role of cognitive, affective, and social factors across five decades," Journal of Retailing, Elsevier, vol. 98(1), pages 92-110.
    6. von Mutius, Bernhard & Huchzermeier, Arnd, 2021. "Customized Targeting Strategies for Category Coupons to Maximize CLV and Minimize Cost," Journal of Retailing, Elsevier, vol. 97(4), pages 764-779.

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