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Dynamic Customer Management and the Value of One-to-One Marketing

Author

Listed:
  • Romana Khan

    () (McCombs School of Business, University of Texas at Austin, Austin, Texas 78713)

  • Michael Lewis

    () (Olin Business School, Washington University in St. Louis, St. Louis, Missouri 63130)

  • Vishal Singh

    () (Stern School of Business, New York University, New York, New York 10012)

Abstract

The concept of one-to-one marketing is intuitively appealing, but there is little research that investigates the value of individual-level marketing relative to segment-level or mass marketing. In this paper, we investigate the financial benefits of and computational challenges involved in one-to-one marketing. The analysis uses data from an online grocery and drug retailer. Like many retailers, this firm uses multiple promotional instruments including discount coupons, free shipping offers, and a loyalty program. We investigate the impact of customizing these promotions on the two most important consumer decisions: the decision to buy from the store and expenditure. Our modeling approach accounts for two sources of heterogeneity in consumers' responsiveness to various marketing mix elements: cross-sectional differences across consumers and temporal differences within consumers based on the purchase cycle. The model parameter estimates are fed into a dynamic programming model that determines the optimal number, sequence, and timing of promotions to maximize retailer profits. A series of policy simulations show that customizing promotions leads to a significant increase in profits relative to the firm's current practice of uniform promotions. However, the effectiveness of various promotions varies because of both cross-sectional differences in consumers as well within consumer heterogeneity due to purchase cycle factors. For instance, we find that free shipping tends to be the preferred instrument for re-acquiring lapsed customers, whereas an across-the-board price cut (via a discount coupon) is the most effective tool for managing the segment of most active customers. Interestingly, we find that customizing based on within-customer temporal heterogeneity contributes more to profitability than exploiting variations across consumers. This is important because the computational burden of implementing the dynamic optimization to account for cross-sectional heterogeneity is far greater than accounting for temporal heterogeneity. Furthermore, targeting promotions based only on timing rather than the nature and magnitude of the offers across consumers alleviates the public relations risks of price discrimination. Implications for marketing managers are also discussed.

Suggested Citation

  • Romana Khan & Michael Lewis & Vishal Singh, 2009. "Dynamic Customer Management and the Value of One-to-One Marketing," Marketing Science, INFORMS, vol. 28(6), pages 1063-1079, 11-12.
  • Handle: RePEc:inm:ormksc:v:28:y:2009:i:6:p:1063-1079
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    File URL: http://dx.doi.org/10.1287/mksc.1090.0497
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    References listed on IDEAS

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    1. Duncan I. Simester & Peng Sun & John N. Tsitsiklis, 2006. "Dynamic Catalog Mailing Policies," Management Science, INFORMS, vol. 52(5), pages 683-696, May.
    2. Dipak C. Jain & Naufel J. Vilcassim, 1991. "Investigating Household Purchase Timing Decisions: A Conditional Hazard Function Approach," Marketing Science, INFORMS, vol. 10(1), pages 1-23.
    3. Keane, Michael P & Wolpin, Kenneth I, 1994. "The Solution and Estimation of Discrete Choice Dynamic Programming Models by Simulation and Interpolation: Monte Carlo Evidence," The Review of Economics and Statistics, MIT Press, vol. 76(4), pages 648-672, November.
    4. Jie Zhang & Lakshman Krishnamurthi, 2004. "Customizing Promotions in Online Stores," Marketing Science, INFORMS, vol. 23(4), pages 561-578, June.
    5. Kim, Jin Gyo & Menzefricke, Ulrich & Feinberg, Fred M., 2005. "Modeling Parametric Evolution in a Random Utility Framework," Journal of Business & Economic Statistics, American Statistical Association, vol. 23, pages 282-294, July.
    6. Kristiaan Helsen & David C. Schmittlein, 1993. "Analyzing Duration Times in Marketing: Evidence for the Effectiveness of Hazard Rate Models," Marketing Science, INFORMS, vol. 12(4), pages 395-414.
    7. Vishal P. Singh & Karsten T. Hansen & Robert C. Blattberg, 2006. "Market Entry and Consumer Behavior: An Investigation of a Wal-Mart Supercenter," Marketing Science, INFORMS, vol. 25(5), pages 457-476, September.
    8. Peter E. Rossi & Robert E. McCulloch & Greg M. Allenby, 1996. "The Value of Purchase History Data in Target Marketing," Marketing Science, INFORMS, vol. 15(4), pages 321-340.
    9. Gabriel R. Bitran & Susana V. Mondschein, 1996. "Mailing Decisions in the Catalog Sales Industry," Management Science, INFORMS, vol. 42(9), pages 1364-1381, September.
    10. Füsun Gönül & Meng Ze Shi, 1998. "Optimal Mailing of Catalogs: A New Methodology Using Estimable Structural Dynamic Programming Models," Management Science, INFORMS, vol. 44(9), pages 1249-1262, September.
    11. Michael Lewis, 2005. "Research Note: A Dynamic Programming Approach to Customer Relationship Pricing," Management Science, INFORMS, vol. 51(6), pages 986-994, June.
    12. Jan Roelf Bult & Tom Wansbeek, 1995. "Optimal Selection for Direct Mail," Marketing Science, INFORMS, vol. 14(4), pages 378-394.
    13. Roland T. Rust & Peter C. Verhoef, 2005. "Optimizing the Marketing Interventions Mix in Intermediate-Term CRM," Marketing Science, INFORMS, vol. 24(3), pages 477-489, December.
    14. Michael Lewis & Vishal Singh & Scott Fay, 2006. "An Empirical Study of the Impact of Nonlinear Shipping and Handling Fees on Purchase Incidence and Expenditure Decisions," Marketing Science, INFORMS, vol. 25(1), pages 51-64, 01-02.
    15. Jinhong Xie & Steven M. Shugan, 2001. "Electronic Tickets, Smart Cards, and Online Prepayments: When and How to Advance Sell," Marketing Science, INFORMS, vol. 20(3), pages 219-243, June.
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    1. repec:eee:jouret:v:89:y:2013:i:3:p:231-245 is not listed on IDEAS
    2. repec:eee:ijrema:v:31:y:2014:i:2:p:168-177 is not listed on IDEAS
    3. repec:eee:jouret:v:89:y:2013:i:3:p:263-280 is not listed on IDEAS
    4. Mercedes Esteban-Bravo & Jose M. Vidal-Sanz & Gökhan Yildirim, 2014. "Valuing Customer Portfolios with Endogenous Mass and Direct Marketing Interventions Using a Stochastic Dynamic Programming Decomposition," Marketing Science, INFORMS, vol. 33(5), pages 621-640, September.
    5. Reimer, Kerstin & Albers, Sönke, 2011. "Modeling Repeat Purchases in the Internet when RFM Captures Past Influence of Marketing," EconStor Preprints 50730, ZBW - German National Library of Economics.
    6. Thomas Reutterer & Kurt Hornik & Nicolas March & Kathrin Gruber, 2017. "A data mining framework for targeted category promotions," Journal of Business Economics, Springer, vol. 87(3), pages 337-358, April.
    7. Yi Qian & Hui Xie, 2011. "No Customer Left Behind: A Distribution-Free Bayesian Approach to Accounting for Missing Xs in Marketing Models," Marketing Science, INFORMS, vol. 30(4), pages 717-736, July.
    8. Alexandre Belloni & Mitchell J. Lovett & William Boulding & Richard Staelin, 2012. "Optimal Admission and Scholarship Decisions: Choosing Customized Marketing Offers to Attract a Desirable Mix of Customers," Marketing Science, INFORMS, vol. 31(4), pages 621-636, July.

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