Skip to main content
SpringerLink
Log in

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

  • Research Article
  • Published:
Customer Needs and Solutions Aims and scope Submit manuscript

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.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3

Similar content being viewed by others

Notes

  1. We recommend to use the median in the recency/inter-purchase-time ratio when the distribution of inter-purchase time is skewed or when the number of observations is small.

References

  1. Ali ÖG, Aritürk U (2014) Dynamic churn prediction framework with more effective use of rare event data: the case of private banking. Expert Syst Appl 41(17):7889–7903

    Article  Google Scholar 

  2. Ascarza E (2017) Retention futility: targeting high risk customers might be ineffective. Forthcoming at the J Mark Res

  3. Ascarza E, Hardie BGS (2013) A joint model of usage and churn in contractual settings. Mark Sci 32(4):570–590

    Article  Google Scholar 

  4. Ascarza E, Iyengar R, Schleicher M (2016) The perils of proactive churn prevention using plan recommendations: evidence from a field experiment. J Mark Res 53(1):46–60

    Article  Google Scholar 

  5. Ascarza E, Ebbes P, Netzer O, Danielson M (2017) Beyond the target customer: social effects of CRM campaigns. J Mark Res 54(3):347–363

    Article  Google Scholar 

  6. Ascarza E, Fader PS, Hardie BGS (2017) Marketing models for the customer-centric firm. In: Wierenga B, van der Lans R (eds) Handbook of marketing decision models. Springer

  7. Ascarza E, Netzer O, Hardie BGS (2017) Some customers would rather leave without saying goodbye. Forthcoming at Mark Sci

  8. Athey S, Imbens GW (2016) Recursive partitioning for heterogeneous causal effects. Proc Natl Acad Sci 113(27):7353–7360

    Article  Google Scholar 

  9. Bell DR, Ho TH, Tang CS (1998) Determining where to shop: fixed and variable costs of shopping. J Mark Res 35(3):352–369

    Article  Google Scholar 

  10. Benedek G, Lublóy Á, Vastag Y (2014) The importance of social embeddedness: churn models at mobile providers. Decis Sci 45(1):175–201

    Article  Google Scholar 

  11. Berger PD, Nasr NI (1998) Customer lifetime value: marketing models and applications. J Interact Mark 12(1):17–30

    Article  Google Scholar 

  12. Berson A, Smith S, Thearling K (2000) Building data mining applications for CRM. McGraw-Hill, New York

    Google Scholar 

  13. Blattberg RC, Deighton J (1996) Manage marketing by the customer equity test. Harv Bus Rev 74(3):136–144

    Google Scholar 

  14. Blattberg RC, Getz G, Thomas JS (2001) Customer equity: building and managing relationships as valuable assets. Harvard Business Press, Boston

    Google Scholar 

  15. Blattberg RC, Kim B-D, Neslin SA (2008) Database marketing: analyzing and managing customers. Springer, New York

    Book  Google Scholar 

  16. Blei DM, Ng AY, Jordan MI (2003) Latent Dirichlet allocation. J Mach Learn Res 3(Jan):993–1022

    Google Scholar 

  17. Braun M, Schweidel DA (2001) Modeling customer lifetimes with multiple causes of churn. Mark Sci 30(5):881–902

    Article  Google Scholar 

  18. Bult JR, Wansbeek T (1995) Optimal selection for direct mail. Mark Sci 14(4):378–394

    Article  Google Scholar 

  19. Castanedo F, Valverde G, Zaratiegui J, Vazquez A (2014) Using deep learning to predict customer churn in a mobile telecommunication network. Available at www.wiseathena.com/pdf/wa_dl.pdf. Accessed 4 Nov 2016

  20. Chen K, Hu Y-H, Hsieh Y-C (2015) Predicting customer churn from valuable B2B customers in the logistics industry: a case study. IseB 13(3):475–494

    Article  Google Scholar 

  21. Coussement K, Van den Poel D (2009) Improving customer attrition prediction by integrating emotions from client/company interaction emails and evaluating multiple classifiers. Expert Syst Appl 36(3):6127–6134

    Article  Google Scholar 

  22. Crammer K, Kulesza A, Dredze M (2009) Adaptive regularization of weight vectors. Adv Neural Inf Proces Syst 22:414–422

    Google Scholar 

  23. Datta H, Foubert B, Van Heerde HJ (2015) The challenge of retaining customers acquired with free trials. J Mark Res 52(2):217–234

    Article  Google Scholar 

  24. de Fortuny J, Enric DM, Provost F (2013) Predictive modeling with big data: is bigger really better? Big Data 1(4):215–226

    Article  Google Scholar 

  25. Delanote S, Leus R, Nobibon FT (2013) Optimization of the annual planning of targeted offers in direct marketing. J Oper Res Soc 64(12):1770–1779

    Article  Google Scholar 

  26. Dodson JA, Tybout AM, Sternthal B (1978) Impact of deals and deal retraction on brand switching. J Mark Res 15(1):72–81

    Article  Google Scholar 

  27. Fader P (2012) Customer centricity: focus on the right customers for strategic advantage. Wharton Digital Press, Philadelphia

    Google Scholar 

  28. Fader PS, Hardie BGS (2009) Probability models for customer-base analysis. J Interact Mark 23(1):61–69

    Article  Google Scholar 

  29. Fader PS, Hardie BGS (2010) Customer-base valuation in a contractual setting: the perils of ignoring heterogeneity. Mark Sci 29(1):85–93

    Article  Google Scholar 

  30. Fader PS, Hardie BGS (2012) Reconciling and clarifying CLV formulas. Available at http://brucehardie.com/notes/024. Accessed 4 Nov 2016

  31. Fader PS, Hardie BGS, Lee KL (2005) ‘Counting your customers’ the easy way: an alternative to the Pareto/NBD model. Mark Sci 24(2):275–284

    Article  Google Scholar 

  32. Fader PS, Hardie BGS, Shang J (2010) Customer-base analysis in a discrete-time noncontractual setting. Mark Sci 29(6):1086–1108

    Article  Google Scholar 

  33. Forbes Insights (2014) Companies struggling to win customers for life, says new study by Forbes Insights and Sitecore. Available at http://www.forbes.com/sites/forbespr/2014/09/10/companies-struggling-to-win-customers-for-life-says-new-study-by-forbes-insights-and-sitecore/#7ac948205759. Accessed 2 Dec 2016

  34. Gedenk K, Neslin SA (1999) The role of retail promotion in determining future brand loyalty: its effect on purchase event feedback. J Retail 75(4):433–459

    Article  Google Scholar 

  35. Giudicati G, Riccaboni M, Romiti A (2013) Experience, socialization and customer retention: lessons from the dance floor. Mark Lett 24(4):409–422

    Article  Google Scholar 

  36. Guelman L, Guillén M, Pérez-Marín AM (2015) Uplift random forests. Cybern Syst 46(3–4):230–248

    Article  Google Scholar 

  37. Gupta S, Lehmann DR, Stuart JA (2004) Valuing customers. J Mark Res 41(1):7–18

    Article  Google Scholar 

  38. Haenlein M (2013) Social interactions in customer churn decisions: the impact of relationship directionality. Int J Res Mark 30(3):236–248

    Article  Google Scholar 

  39. Haenlein M, Libai B (2013) Targeting revenue leaders for a new product. J Mark 77(3):65–80

    Article  Google Scholar 

  40. Handley L (2013) Customer retention: brave new world of consumer dynamics. Mark Week Online Ed 21. Available at https://www.marketingweek.com/2013/03/20/customer-retention-brave-new-world-of-consumer-dynamics. Accessed 4 Nov 2017

  41. Hill S, Provost F, Volinsky C (2006) Network-based marketing: identifying likely adopters via consumer networks. Stat Sci 21(2):256–276

    Article  Google Scholar 

  42. Hogan JE, Lemon KN, Libai B (2003) What is the true value of a lost customer? J Serv Res 5(3):196–208

    Article  Google Scholar 

  43. Huang B, Kechadi MT, Buckley B (2012) Customer churn prediction in telecommunications. Expert Syst Appl 39(1):1414–1425

    Article  Google Scholar 

  44. Kosinski M, Stillwell D, Graepel T (2013) Private traits and attributes are predictable from digital records of human behavior. Proc Natl Acad Sci 110(15):5802–5805

    Article  Google Scholar 

  45. Kumar V, Bhagwat Y, Zhang X (2015) Regaining ‘lost’ customers: the predictive power of first-lifetime behavior, the reason for defection, and the nature of the win-back offer. J Mark 79(4):34–55

    Article  Google Scholar 

  46. Labrecque JS, Wood W, Neal DT, Harrington N (2016) Habit slips: when consumers unintentionally resist new products. J Acad Mark Sci 45(1):1–15

  47. Lemmens A, Croux C (2006) Bagging and boosting classification trees to predict churn. J Mark Res 43(2):276–286

    Article  Google Scholar 

  48. Lemmens A, Gupta S (2017) Managing churn to maximize profits,” Available at SSRN: https://ssrn.com/abstract=2964906 or https://doi.org/10.2139/ssrn.2964906

  49. Leone R, Rao VR, Keller KL, Luo AM, McAlister L, Srivastava R (2006) Linking brand equity to customer equity. J Serv Res 9(2):125–138

    Article  Google Scholar 

  50. Lewis M (2005) Incorporating strategic consumer behavior into customer valuation. J Mark 69(4):230–238

    Article  Google Scholar 

  51. Lim M, Hastie T (2015) Learning interactions via hierarchical group-lasso regularization. J Comput Graph Stat 24(3):627–654

    Article  Google Scholar 

  52. Little JDC (1970) Models and managers: the concept of a decision calculus. Manag Sci 16(8, Application Series):B466–B485

    Article  Google Scholar 

  53. Martens D, Provost F, Clark J, Fortuny EJ d (2016) Mining massive fine-grained behavior data to improve predictive analytics. MIS Q 40(4):869–888

    Article  Google Scholar 

  54. Matz SC, Netzer O (2017) Using big data as a window into consumers’ psychology. Curr Opin Behav Sci 18:7–12

    Article  Google Scholar 

  55. McCarthy DM, Fader PS, Hardie BGS (2017) Valuing subscription-based businesses using publicly disclosed customer data. J Mark 81(1):17–35

  56. Montoya R, Netzer O, Jedidi K (2010) Dynamic allocation of pharmaceutical detailing and sampling for long-term profitability. Mark Sci 29(5):909–924

    Article  Google Scholar 

  57. Musalem A, Joshi YV (2009) Research note-how much should you invest in each customer relationship? A competitive strategic approach. Mark Sci 28(3):555–565

    Article  Google Scholar 

  58. Neslin SA (2013) Dynamic customer optimization models. In: Coussement K, De Bock KW, Neslin SA (eds) Advanced database marketing. Gower Publishing Limited, Surrey

    Google Scholar 

  59. Neslin SA, Gupta S, Kamakura W, Lu J, Mason CH (2006) Defection detection: improving the predictive accuracy of customer churn models. J Mark Res 43(2):204–211

    Article  Google Scholar 

  60. Nitzan I, Libai B (2011) Social effects on customer retention. J Mark 75(6):24–38

    Article  Google Scholar 

  61. Nitzan I, Libai B (2013) If you go, I will follow … social effects on the decision to terminate a service. GfK Mark Intell Rev 5(2):40–45

    Google Scholar 

  62. Nobibon F, Leus TR, Spieksma FCR (2011) Optimization models for targeted offers in direct marketing: exact and heuristic algorithms. Eur J Oper Res 210(3):670–683

    Article  Google Scholar 

  63. Oliver RL, Rust RT, Varki S (1997) Customer delight: foundations, findings, and managerial insight. J Retail 73(3):311–336

    Article  Google Scholar 

  64. Ovchinnikov A, Boulu-Reshef B, Pfeifer PE (2014) Balancing acquisition and retention spending for firms with limited capacity. Manag Sci 60(8):2002–2019

    Article  Google Scholar 

  65. Pennebaker JW, Francis ME, Booth RJ (2001) Linguistic inquiry and word count: LIWC 2001. Lawrence Erlbaum Associates, Mahway

    Google Scholar 

  66. Peppers D, Rogers M (2004) Managing customer relationships: a strategic framework. John Wiley & Sons, Hoboken

    Google Scholar 

  67. Perlich C, Dalessandro B, Stitelman O, Raeder T, Provost F (2014) Machine learning for targeted display advertising: transfer learning in action. Mach Learn 95(1):103–127

    Article  Google Scholar 

  68. Perro J (2016) Mobile apps: what’s a good retention rate? Localytics, March 28. (Available at http://Info.Localytics.Com/Blog/Mobile-Apps-Whats-A-Good-Retention-Rate. Accessed 4 Nov 2016

  69. Provost F, Fawcett T (2013) Data science for business: what you need to know about data mining and data analytic thinking. O’Reilly Media

  70. Provost F, Dalessandro B, Hook R, Zhang X, Murray A (2009) Audience selection for on-line brand advertising: privacy-friendly social network targeting. In: Proceedings of the Fifteenth ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, 707–716

  71. Putter H, Fiocco M, Geskus RB (2007) Tutorial in biostatistics: competing risks and multi-state models. Stat Med 26(11):2389–2430

    Article  Google Scholar 

  72. Rand W, Rust RT (2011) Agent-based modeling in marketing: guidelines for rigor. Int J Res Mark 28(3):181–193

    Article  Google Scholar 

  73. Reinartz W, Thomas JS, Kumar V (2005) Balancing acquisition and resources to maximize customer profitability. J Mark 69(1):63–79

    Article  Google Scholar 

  74. Rust RT, Oliver RL (2000) Should we delight the customer? J Acad Mark Sci 28(1):86–94

    Article  Google Scholar 

  75. Rust RT, Zeithaml VA, Lemon KN (2001) Driving customer equity: how customer lifetime value is reshaping corporate strategy. Simon and Schuster, New York

    Google Scholar 

  76. Rust RT, Kim J, Dong Y, Kim TJ, Lee S (2015) Drivers of customer equity. Handbook of research on customer equity in marketing. Edward Elgar, pp 17–43

  77. Schmittlein DC, Morrison DG, Colombo R (1987) Counting your customers: who are they and what will they do next? Manag Sci 33(1):1–24

    Article  Google Scholar 

  78. Schrift RY, Parker JR (2014) Staying the course: the option of doing nothing and its impact on postchoice persistence. Psychol Sci 25(3):772–780

    Article  Google Scholar 

  79. Schwartz EM, Bradlow ET Fader PS (2014) Model selection using database characteristics: Developing a classification tree for longitudinal incidence data. Mark Sci 33(2):188–205

  80. Schweidel DA, Knox G (2013) Incorporating direct marketing activity into latent attrition models. Mark Sci 32(3):471–487

    Article  Google Scholar 

  81. Schweidel DA, Bradlow ET, Fader PS (2011) Portfolio dynamics for customers of a multiservice provider. Manag Sci 57(3):471–486

    Article  Google Scholar 

  82. Seitz P (2015) Apple music facing subscriber retention problems. Investor’s business daily. Available at https://www.investors.com/apple-music-hitting-some-sour-notes. Accessed 4 Nov 2017

  83. Springer T, Kim C, Azzarello D, Melton J (2014) Breaking the back of customer churn. Available at http://www.bain.com/publications/articles/breaking-the-back-of-customer-churn.aspx. Accessed 4 Nov 2016

  84. Stahl F, Heitmann M, Lehmann DR, Neslin SA (2012) The impact of brand equity on customer acquisition, retention, and profit margin. J Mark 76(4):44–63

    Article  Google Scholar 

  85. Statista (2016) Average monthly churn rate for wireless carriers in the United States from 1st quarter 2013 to 1st quarter 2016. Available at http://www.statista.com/statistics/283511/average-monthly-churn-rate-top-wireless-carriers-us/. Accessed 4 Nov 2016

  86. Stauss B, Friege C (1999) Regaining service customers: costs and benefits of regain management. J Serv Res 1(4):347–361

    Article  Google Scholar 

  87. Tamaddoni A, Stakhovych S, Ewing M (2016) Comparing churn prediction techniques and assessing their performance: a contingent perspective. J Serv Res 19(2):123–141

    Article  Google Scholar 

  88. Taylor J, Tibshirani RJ (2015) Statistical learning and selective inference. Proc Natl Acad Sci 112(25):7629–7634

  89. Thomas JS, Blattberg RC, Fox EJ (2004) Recapturing lost customers. J Mark Res 41(1):31–45

    Article  Google Scholar 

  90. Tibshirani RJ (1996) Regression shrinkage and selection via the lasso. J R Stat Soc B Methodol 267–288

  91. Vafeiadis T, Diamantaras KI, Sarigiannidis G, Chatzisavvas KC (2015) A comparison of machine learning techniques for customer churn prediction. Simul Model Pract Theory 55:1–9

    Article  Google Scholar 

  92. van Baal S, Dach C (2005) Free riding and customer retention across retailers’ channels. J Interact Mark 19(2):75–85

    Article  Google Scholar 

  93. Venkatesan R, Kumar V (2004) A customer lifetime value framework for customer selection and resource allocation strategy. J Mark 68(4):106–125

    Article  Google Scholar 

  94. Verbeke W, Martens D, Mues C, Baesens B (2011) Building comprehensible customer churn prediction models with advanced rule induction techniques. Expert Syst Appl 38(3):2354–2364

    Article  Google Scholar 

  95. Verbeke W, Martens D, Baesens B (2014) Social network analysis for customer churn prediction. Appl Soft Comput 14:431–446

    Article  Google Scholar 

  96. Verhoef PC (2003) Understanding the effect of customer relationship management efforts on customer retention and customer share development. J Mark 67(4):30–45

    Article  Google Scholar 

  97. Verhoef PC, Donkers B (2005) The effect of acquisition channels on customer loyalty and cross-buying. J Interact Mark 19(2):31–43

    Article  Google Scholar 

  98. Voss GB, Voss ZG (2008) Competitive density and the customer acquisition-retention trade-off. J Mark 72(6):3–18

    Article  Google Scholar 

  99. Wang JC, Hastie T (2014) Boosted varying-coefficient regression models for product demand prediction. J Comput Graph Stat 23(2):361–382

    Article  Google Scholar 

  100. Webb (2016) Apple to Revamp Streaming Music Service After Mixed Reviews, Departures. Available at http://www.bloomberg.com/news/articles/2016-05-04/apple-to-revamp-streaming-music-service-after-mixed-reviews-departures. Accessed 4 Nov 2016

  101. Wood W, Neal DT (2009) The habitual consumer. J Consum Psychol 19:579–592

    Article  Google Scholar 

  102. Zou H, Zhang HH (2009) On the adaptive elastic-net with a diverging number of parameters. Ann Stat 37(4):1733–1751

    Article  Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Columbia Business School, New York, NY, USA

    Eva Ascarza & Oded Netzer

  2. Tuck School of Business, Hanover, NH, USA

    Scott A. Neslin

  3. Electronic Arts, Redwood City, CA, USA

    Zachery Anderson

  4. The Wharton School, Philadelphia, PA, USA

    Peter S. Fader & Rom Schrift

  5. Harvard Business School, Boston, MA, USA

    Sunil Gupta

  6. London Business School, London, UK

    Bruce G. S. Hardie

  7. Tilburg School of Economics and Management, Tilburg, Netherlands

    Aurélie Lemmens

  8. Arison School of Business, Hertsliya, Israel

    Barak Libai

  9. Catalyst Behavioral Sciences and Duke University, Durham, NC, USA

    David Neal

  10. New York University, New York, NY, USA

    Foster Provost

Authors
  1. Eva Ascarza
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Scott A. Neslin
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Oded Netzer
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Zachery Anderson
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Peter S. Fader
    View author publications

    You can also search for this author in PubMed Google Scholar

  6. Sunil Gupta
    View author publications

    You can also search for this author in PubMed Google Scholar

  7. Bruce G. S. Hardie
    View author publications

    You can also search for this author in PubMed Google Scholar

  8. Aurélie Lemmens
    View author publications

    You can also search for this author in PubMed Google Scholar

  9. Barak Libai
    View author publications

    You can also search for this author in PubMed Google Scholar

  10. David Neal
    View author publications

    You can also search for this author in PubMed Google Scholar

  11. Foster Provost
    View author publications

    You can also search for this author in PubMed Google Scholar

  12. Rom Schrift
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Eva Ascarza.

Additional information

This paper is the outcome of a workshop on “Customer Retention” as part of the 10th Triennial Invitational Choice Symposium, University of Alberta, 2016.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Ascarza, E., Neslin, S.A., Netzer, O. et al. In Pursuit of Enhanced Customer Retention Management: Review, Key Issues, and Future Directions. Cust. Need. and Solut. 5, 65–81 (2018). https://doi.org/10.1007/s40547-017-0080-0

Download citation

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s40547-017-0080-0

Keywords

Search

Navigation