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

  • 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)

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    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.

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    Article provided by INFORMS in its journal Marketing Science.

    Volume (Year): 28 (2009)
    Issue (Month): 6 (11-12)
    Pages: 1063-1079

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    Handle: RePEc:inm:ormksc:v:28:y:2009:i:6:p:1063-1079
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