IDEAS home Printed from https://ideas.repec.org/a/spr/custns/v5y2018i1d10.1007_s40547-017-0080-0.html
   My bibliography  Save this article

In Pursuit of Enhanced Customer Retention Management: Review, Key Issues, and Future Directions

Author

Listed:
  • Eva Ascarza

    () (Columbia Business School)

  • Scott A. Neslin

    () (Tuck School of Business)

  • Oded Netzer

    () (Columbia Business School)

  • Zachery Anderson

    () (Electronic Arts)

  • Peter S. Fader

    () (The Wharton School)

  • Sunil Gupta

    () (Harvard Business School)

  • Bruce G. S. Hardie

    () (London Business School)

  • Aurélie Lemmens

    () (Tilburg School of Economics and Management)

  • Barak Libai

    () (Arison School of Business)

  • David Neal

    () (Catalyst Behavioral Sciences and Duke University)

  • Foster Provost

    () (New York University)

  • Rom Schrift

    () (The Wharton School)

Abstract

In today’s turbulent business environment, customer retention presents a significant challenge for many service companies. Academics have generated a large body of research that addresses part of that challenge—with a particular focus on predicting customer churn. However, several other equally important aspects of managing retention have not received similar level of attention, leaving many managerial problems not completely solved, and a program of academic research not completely aligned with managerial needs. Therefore, our goal is to draw on previous research and current practice to provide insights on managing retention and identify areas for future research. This examination leads us to advocate a broad perspective on customer retention. We propose a definition that extends the concept beyond the traditional binary retain/not retain view of retention. We discuss a variety of metrics to measure and monitor retention. We present an integrated framework for managing retention that leverages emerging opportunities offered by new data sources and new methodologies such as machine learning. We highlight the importance of distinguishing between which customers are at risk and which should be targeted—as they are not necessarily the same customers. We identify trade-offs between reactive and proactive retention programs, between short- and long-term remedies, and between discrete campaigns and continuous processes for managing retention. We identify several areas of research where further investigation will significantly enhance retention management.

Suggested Citation

  • Eva Ascarza & Scott A. Neslin & Oded Netzer & Zachery Anderson & Peter S. Fader & Sunil Gupta & Bruce G. S. Hardie & Aurélie Lemmens & Barak Libai & David Neal & Foster Provost & Rom Schrift, 2018. "In Pursuit of Enhanced Customer Retention Management: Review, Key Issues, and Future Directions," Customer Needs and Solutions, Springer;Institute for Sustainable Innovation and Growth (iSIG), vol. 5(1), pages 65-81, March.
  • Handle: RePEc:spr:custns:v:5:y:2018:i:1:d:10.1007_s40547-017-0080-0
    DOI: 10.1007/s40547-017-0080-0
    as

    Download full text from publisher

    File URL: http://link.springer.com/10.1007/s40547-017-0080-0
    File Function: Abstract
    Download Restriction: Access to the full text of the articles in this series is restricted.

    As the access to this document is restricted, you may want to search for a different version of it.

    References listed on IDEAS

    as
    1. Rand, William & Rust, Roland T., 2011. "Agent-based modeling in marketing: Guidelines for rigor," International Journal of Research in Marketing, Elsevier, vol. 28(3), pages 181-193.
    2. S Delanote & R Leus & F Talla Nobibon, 2013. "Optimization of the annual planning of targeted offers in direct marketing," Journal of the Operational Research Society, Palgrave Macmillan;The OR Society, vol. 64(12), pages 1770-1779, December.
    3. 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.
    4. Ricardo Montoya & Oded Netzer & Kamel Jedidi, 2010. "Dynamic Allocation of Pharmaceutical Detailing and Sampling for Long-Term Profitability," Marketing Science, INFORMS, vol. 29(5), pages 909-924, 09-10.
    5. David A. Schweidel & Eric T. Bradlow & Peter S. Fader, 2011. "Portfolio Dynamics for Customers of a Multiservice Provider," Management Science, INFORMS, vol. 57(3), pages 471-486, March.
    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. Anton Ovchinnikov & Béatrice Boulu-Reshef & Phillip E. Pfeifer, 2014. "Balancing Acquisition and Retention Spending for Firms with Limited Capacity," Post-Print hal-01261385, HAL.
    8. 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.
    9. David A. Schweidel & George Knox, 2013. "Incorporating Direct Marketing Activity into Latent Attrition Models," Marketing Science, INFORMS, vol. 32(3), pages 471-487, May.
    10. 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.
    11. 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.
    12. K. Coussement & D. van den Poel, 2009. "Improving customer attrition prediction by integrating emotions from client/company interaction emails and evaluating multiple classifiers," Post-Print halshs-00581595, HAL.
    13. 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.
    14. Eric M. Schwartz & Eric T. Bradlow & Peter S. Fader, 2014. "Model Selection Using Database Characteristics: Developing a Classification Tree for Longitudinal Incidence Data," Marketing Science, INFORMS, vol. 33(2), pages 188-205, March.
    15. Jan Roelf Bult & Tom Wansbeek, 1995. "Optimal Selection for Direct Mail," Marketing Science, INFORMS, vol. 14(4), pages 378-394.
    16. 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.
    17. Eva Ascarza & Bruce G. S. Hardie, 2013. "A Joint Model of Usage and Churn in Contractual Settings," Marketing Science, INFORMS, vol. 32(4), pages 570-590, July.
    18. 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.
    19. 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.
    20. 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.
    21. Talla Nobibon, Fabrice & Leus, Roel & Spieksma, Frits C.R., 2011. "Optimization models for targeted offers in direct marketing: Exact and heuristic algorithms," European Journal of Operational Research, Elsevier, vol. 210(3), pages 670-683, May.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Lie Heng & Nur Afifah, 2020. "Entrepreneurial Orientation for Enhancement of Marketing Performance," International Review of Management and Marketing, Econjournals, vol. 10(3), pages 46-53.
    2. Aurélie Lemmens & Sunil Gupta, 2020. "Managing Churn to Maximize Profits," Marketing Science, INFORMS, vol. 39(5), pages 956-973, September.
    3. Hasnan Baber, 2020. "FinTech, Crowdfunding and Customer Retention in Islamic Banks," Vision, , vol. 24(3), pages 260-268, 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.

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:spr:custns:v:5:y:2018:i:1:d:10.1007_s40547-017-0080-0. See general information about how to correct material in RePEc.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: (Sonal Shukla) or (Springer Nature Abstracting and Indexing). General contact details of provider: http://www.springer.com .

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service hosted by the Research Division of the Federal Reserve Bank of St. Louis . RePEc uses bibliographic data supplied by the respective publishers.