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Incorporating Satisfaction into Customer Value Analysis: Optimal Investment in Lifetime Value


  • Teck-Hua Ho

    () (Haas School of Business, University of California, Berkeley, California 94720-1900)

  • Young-Hoon Park

    () (Johnson Graduate School of Management, Cornell University, Ithaca, New York 14853-6201)

  • Yong-Pin Zhou

    () (Business School, University of Washington, Seattle, Washington 98195-3200)


We extend the Schmittlein et al. model (1987) of customer lifetime value to include satisfaction. Customer purchases are modeled as Poisson events, and their rates of occurrence depend on the satisfaction of the most recent purchase encounter. Customers purchase at a higher rate when they are satisfied than when they are dissatisfied. A closed-form formula is derived for predicting total expected dollar spending from a customer base over a time period (0, ]. This formula reveals that approximating the mixture arrival processes by a single aggregate Poisson process can systematically underestimate the total number of purchases and revenue. Interestingly, the total revenue is increasing and convex in satisfaction. If the cost is sufficiently convex, our model reveals that the aggregate model leads to an overinvestment in customer satisfaction. The model is further extended to include three other benefits of customer satisfaction: (1) satisfied customers are likely to spend more per trip on average than dissatisfied customers, (2) satisfied customers are less likely to leave the customer base than dissatisfied customers, and (3) previously satisfied customers can be more (or less) likely to be satisfied in the current visit than previously dissatisfied customers. We show that all the main results carry through to these general settings.

Suggested Citation

  • Teck-Hua Ho & Young-Hoon Park & Yong-Pin Zhou, 2006. "Incorporating Satisfaction into Customer Value Analysis: Optimal Investment in Lifetime Value," Marketing Science, INFORMS, vol. 25(3), pages 260-277, 05-06.
  • Handle: RePEc:inm:ormksc:v:25:y:2006:i:3:p:260-277

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    References listed on IDEAS

    1. Kapil Bawa & Robert Shoemaker, 2004. "The Effects of Free Sample Promotions on Incremental Brand Sales," Marketing Science, INFORMS, vol. 23(3), pages 345-363, November.
    2. Eyal Biyalogorsky & Eitan Gerstner & Barak Libai, 2001. "Customer Referral Management: Optimal Reward Programs," Marketing Science, INFORMS, vol. 20(1), pages 82-95, August.
    3. Fitzsimons, Gavan J, 2000. " Consumer Response to Stockouts," Journal of Consumer Research, Oxford University Press, vol. 27(2), pages 249-266, September.
    4. Yuxin Chen & Ganesh Iyer, 2002. "Research Note Consumer Addressability and Customized Pricing," Marketing Science, INFORMS, vol. 21(2), pages 197-208, November.
    5. Eugene W. Anderson & Mary W. Sullivan, 1993. "The Antecedents and Consequences of Customer Satisfaction for Firms," Marketing Science, INFORMS, vol. 12(2), pages 125-143.
    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. Peter S. Fader & Bruce G. S. Hardie & Chun-Yao Huang, 2004. "A Dynamic Changepoint Model for New Product Sales Forecasting," Marketing Science, INFORMS, vol. 23(1), pages 50-65, October.
    8. Steven M. Shugan, 2005. "Brand Loyalty Programs: Are They Shams?," Marketing Science, INFORMS, vol. 24(2), pages 185-193.
    9. Bruce G. S. Hardie & Peter S. Fader & Robert Zeithammer, 2003. "Forecasting new product trial in a controlled test market environment," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 22(5), pages 391-410.
    10. J. Miguel Villas-Boas, 2004. "Consumer Learning, Brand Loyalty, and Competition," Marketing Science, INFORMS, vol. 23(1), pages 134-145, December.
    11. Young-Hoon Park & Peter S. Fader, 2004. "Modeling Browsing Behavior at Multiple Websites," Marketing Science, INFORMS, vol. 23(3), pages 280-303, May.
    12. Teck-Hua Ho & Christopher S. Tang & David R. Bell, 1998. "Rational Shopping Behavior and the Option Value of Variable Pricing," Management Science, INFORMS, vol. 44(12-Part-2), pages 145-160, December.
    13. Donald G. Morrison & David C. Schmittlein, 1981. "Predicting Future Random Events Based on Past Performance," Management Science, INFORMS, vol. 27(9), pages 1006-1023, September.
    14. 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.
    15. 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.
    16. David C. Schmittlein & Robert A. Peterson, 1994. "Customer Base Analysis: An Industrial Purchase Process Application," Marketing Science, INFORMS, vol. 13(1), pages 41-67.
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    1. repec:eee:joinma:v:27:y:2013:i:3:p:185-208 is not listed on IDEAS
    2. Steven M. Shugan, 2007. "—It's the Findings, Stupid, Not the Assumptions," Marketing Science, INFORMS, vol. 26(4), pages 449-459, 07-08.
    3. Eric T. Anderson & Gavan J. Fitzsimons & Duncan Simester, 2006. "Measuring and Mitigating the Costs of Stockouts," Management Science, INFORMS, vol. 52(11), pages 1751-1763, November.
    4. Ekinci, Yeliz & Ülengin, Füsun & Uray, Nimet & Ülengin, Burç, 2014. "Analysis of customer lifetime value and marketing expenditure decisions through a Markovian-based model," European Journal of Operational Research, Elsevier, vol. 237(1), pages 278-288.
    5. Shaohui Ma & Joachim Büschken, 2011. "Counting your customers from an “always a share” perspective," Marketing Letters, Springer, vol. 22(3), pages 243-257, September.
    6. Andrés Musalem & Yogesh V. Joshi, 2009. "—How Much Should You Invest in Each Customer Relationship? A Competitive Strategic Approach," Marketing Science, INFORMS, vol. 28(3), pages 555-565, 05-06.
    7. James G. Maxham, III & Richard G. Netemeyer & Donald R. Lichtenstein, 2008. "The Retail Value Chain: Linking Employee Perceptions to Employee Performance, Customer Evaluations, and Store Performance," Marketing Science, INFORMS, vol. 27(2), pages 147-167, 03-04.
    8. Albert C. Bemmaor & Nicolas Glady, 2012. "Modeling Purchasing Behavior with Sudden "Death": A Flexible Customer Lifetime Model," Management Science, INFORMS, vol. 58(5), pages 1012-1021, May.


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