Overcoming the “recency trap” in customer relationship management

  • Scott A. NeslinEmail author
  • Gail Ayala Taylor
  • Kimberly D. Grantham
  • Kimberly R. McNeil
Original Empirical Research


Purchase likelihood typically declines as the length of time since the customer’s previous purchase (“recency”) increases. As a result, firms face a “recency trap,” whereby recency increases for customers who do not purchase in a given period, making it even less likely they will purchase in the next period. Eventually the customer is effectively lost to the firm. We develop and illustrate a modeling approach to target a firm’s marketing efforts, keeping in mind the customer’s recency state. This requires an empirical model that predicts purchase likelihood as a function of recency and marketing, and a dynamic optimization that prescribes the most profitable way to target customers. In our application we find that customers’ purchase likelihoods as well as response to marketing depend on recency. These results are used to show that the targeting of email and direct mail should depend on the customer’s recency and that the optimal decision policy enables the average high recency customer, who currently is virtually worthless to the firm, to become profitable.


Customer relationship management Customer lifetime value Optimization Migration model Customer recency 


  1. Ackoff, R. L., & Emshoff, J. R. (1975). Advertising research at Anheuser-Busch, Inc (1963–68). Sloan Management Review, 16(2), 1–15.Google Scholar
  2. Ansari, A., Mela, C. F., & Neslin, S. A. (2008). Customer channel migration. Journal of Marketing Research, 45(1), 60–76.CrossRefGoogle Scholar
  3. Berger, P. D., & Nasr, N. I. (1998). Customer lifetime value: marketing models and applications. Journal of Interactive Marketing (John Wiley & Sons), 12(1), 17–30.CrossRefGoogle Scholar
  4. Beswick, C. A. (1977). Allocating selling effort via dynamic programming. Management Science, 23(7), 667–678.CrossRefGoogle Scholar
  5. Bitran, G. R., & Mondschein, S. V. (1996). Mailing decisions in the catalog sales industry. Management Science, 42(9), 1364–1381.CrossRefGoogle Scholar
  6. Blattberg, R. C., Byung-Do, K., & Neslin, S. A. (2008). Database marketing analyzing and managing customers. New York: Springer.CrossRefGoogle Scholar
  7. Bult, J. R., & Wansbeek, T. (1995). Optimal selection for direct mail. Marketing Science, 14(4), 378–394.CrossRefGoogle Scholar
  8. Dreze, X., & Bonfrer, A. (2008). An empirical investigation of the impact of communication timing on customer equity. Journal of Interactive Marketing, 22(1), 36–50.CrossRefGoogle Scholar
  9. Elsner, R., Krafft, M., & Huchzermeier, A. (2003). Optimizing rhenania’s mail-order business through dynamic multilevel modeling (DMLM). Interfaces, 33(1), 50–66.CrossRefGoogle Scholar
  10. Elsner, R., Krafft, M., & Huchzermeier, A. (2004). The 2003 ISMS practice prize winner-optimizing rhenania’s direct marketing business through dynamic multilevel modeling (DMLM) in a multicatalog-brand environment. Marketing Science, 23(2), 192–206.CrossRefGoogle Scholar
  11. Fader, P. S., Hardie, B. G. S., & Lee, K. L. (2005). RFM and CLV: using iso-value curves for customer base analysis. Journal of Marketing Research, 42(4), 415–430.CrossRefGoogle Scholar
  12. Fader, P. S., Hardie, B. G. S., & Jerath, K. (2007). “Estimating CLV using aggregated data: the tuscan lifestyles case revisited. Journal of Interactive Marketing, 21(3), 55–71.CrossRefGoogle Scholar
  13. Freedman, J. L., & Fraser, S. C. (1966). compliance without pressure: the foot-in-the-door technique. Journal of Personality and Social Psychology, 4(2), 195–202.CrossRefGoogle Scholar
  14. Gönül, F., & Shi, M. Z. (1998). Optimal mailing of catalogs: a new methodology using estimable structural dynamic programming models. Management Science, 44(9), 1249–1262.CrossRefGoogle Scholar
  15. Gönül, F., Kim, B.-D., & Shi, M. (2000). Mailing smarter to catalog customers. Journal of Interactive Marketing, 14(2), 2–16.CrossRefGoogle Scholar
  16. Hughes, A. M. (1996). The complete database marketer, revised. New York: McGraw-Hill.Google Scholar
  17. Judd, K. L. (1998). Numerical methods in economics. Cambridge: MIT Press.Google Scholar
  18. Keane, M. P., & Wolpin, K. I. (1994). The solution and estimation of discrete choice dynamic programming models by simulation and interpolation. Review of Economic Statistics, 76(4), 648–672.CrossRefGoogle Scholar
  19. Khan, R., Lewis, M., & Singh, V. (2009). Dynamic customer management and the value of one-to-one marketing. Marketing Science, 28(6), 1063–1079.CrossRefGoogle Scholar
  20. Leeflang, P. S. H., Wittink, D. R., Wedel, M., & Naert, P. A. (2000). Building models for marketing decisions. Boston: Kluwer.Google Scholar
  21. Little, J. D. C. (1970). Models and managers-concept of a decision calculus. Management Science Series B-Application, 16(8), B466–B485.Google Scholar
  22. Miglautsch, J. (2002). Application of RFM principles: what to do with 1-1-1 customers? Journal of Database Marketing, 9(4), 319.CrossRefGoogle Scholar
  23. Montgomery, D. B., Silk, A. J., & Zaragoza, C. E. (1971). A multiple-product sales force allocation model. Management Science, 18(4), P3–P24.Google Scholar
  24. Naik, P. A., & Piersma, N. (2002). Understanding the role of marketing communications in direct marketing. Rotterdam: Econometric Institute.Google Scholar
  25. Neslin, S. A., Gupta, S., Kamakura, W., Junxiang, L., & Mason, C. H. (2006). Defection detection: measuring and understanding the predictive accuracy of customer churn models. Journal of Marketing Research, 43(2), 204–211.CrossRefGoogle Scholar
  26. Neslin, S. A., Novak, T. P., Baker, K. R., & Hoffman, D. L. (2009). An optimal contact model for maximizing online panel response rates. Management Science, 55(5), 727–737.CrossRefGoogle Scholar
  27. Pauwels, K., & Neslin, S. A. (2008). “Building with bricks and mortar: the revenue impact of opening physical stores in multichannel environment.” working paper. Hanover: Tuck School of Business, Dartmouth College.Google Scholar
  28. Pfeifer, P. E., & Carraway, R. L. (2000). Modeling customer relationships as markov chains. Journal of Interactive Marketing (John Wiley & Sons), 14(2), 43–55.CrossRefGoogle Scholar
  29. Ray, M. L., & Sawyer, A. G. (1971). Repetition in media models: a laboratory technique. Journal of Marketing Research, 8(1), 20–29.CrossRefGoogle Scholar
  30. Reimann, M., Schilke, O., & Thomas, J. S. (2010). Customer relationship management and firm performance: the mediating role of business strategy. Journal of the Academy of Marketing Science, 38(3), 326–346.CrossRefGoogle Scholar
  31. Rhee, S., & McIntyre, S. (2008). Including the effects of prior and recent contact effort in a customer scoring model for database marketing. Journal of the Academy of Marketing Science, 36(4), 538–551.CrossRefGoogle Scholar
  32. Rust, R. T., & Verhoef, P. C. (2005). Optimizing the marketing interventions mix in intermediate-term CRM. Marketing Science, 24(3), 477–489.CrossRefGoogle Scholar
  33. Simester, D. I., Sun, P., & Tsitsiklis, J. N. (2006). Dynamic catalog mailing policies. Management Science, 52(5), 683–696.CrossRefGoogle Scholar
  34. Tokman, M., Davis, L. M., & Lemon, K. N. (2007). The WOW factor: creating value through win-back offers to reacquire lost customers. Journal of Retailing, 83(1), 47–64.CrossRefGoogle Scholar
  35. Van den Poel, D., & Leunis, J. (1998). Database marketing modeling for financial services using hazard rate models. International Review of Retail, Distribution & Consumer Research, 8(2), 243–257.CrossRefGoogle Scholar
  36. Van Diepen, M., Donkers, B., & Franses, P. H. (2009). ”Does Irritation Induced by Charitable Direct Mailings Reduce Donations? International Journal of Research in Marketing, 26(3), 180–188.CrossRefGoogle Scholar
  37. Venkatesan, R., Kumar, V., & Bohling, T. (2007). Optimal customer relationship management using bayesian decision theory: an application for customer selection. Journal of Marketing Research, 44(4), 579–594.CrossRefGoogle Scholar
  38. Zoltners, A. A., & Sinha, P. (1980). Integer programming models for sales resource allocation. Management Science, 26(3), 242–260.CrossRefGoogle Scholar

Copyright information

© Academy of Marketing Science 2012

Authors and Affiliations

  • Scott A. Neslin
    • 1
    Email author
  • Gail Ayala Taylor
    • 1
  • Kimberly D. Grantham
    • 2
  • Kimberly R. McNeil
    • 3
  1. 1.Tuck School of BusinessDartmouth CollegeHanoverUSA
  2. 2.Terry College of BusinessUniversity of GeorgiaAthensUSA
  3. 3.School of Business and Economics, Quiester Craig HallNorth Carolina A&T State UniversityGreensboroUSA

Personalised recommendations