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Customer Influence Value and Purchase Acceleration in New Product Diffusion

Author

Listed:
  • Teck-Hua Ho

    (National University of Singapore, Singapore 119077; and University of California, Berkeley, Berkeley, California 94720)

  • Shan Li

    (Philips Research North America, Briarcliff Manor, New York 10510)

  • So-Eun Park

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

  • Zuo-Jun Max Shen

    (Department of Industrial Engineering and Operations Research, University of California, Berkeley, Berkeley, California 94720)

Abstract

When social influence plays a key role in the diffusion of new product, the value of a customer often goes beyond her own product purchase. We posit that a customer's value (CV) comes not only from her purchase value (PV) but also from her influence value (IV) (i.e., CV = PV + IV). Therefore, a customer's value can be far greater than her purchase value if she exerts a considerable influence on others. Building on a two-segment influential-imitator asymmetric influence model, we develop a model framework to derive closed-form expressions for PV, IV, and CV by customer segment as well as time of adoption, and we examine their comparative statics with respect to the diffusion parameters. A key parameter of our model framework is the social apportioning parameter, delta, which determines the credit a customer receives by influencing other potential adopters. We develop an endogenous method for determining delta as a function of the new product diffusion parameters. Our model framework allows us to investigate how a firm might accelerate product purchases by providing introductory discount offers to a targeted group of potential adopters at product launch. We find that purchase acceleration frequently leads to a significant increase in total customer value.

Suggested Citation

  • Teck-Hua Ho & Shan Li & So-Eun Park & Zuo-Jun Max Shen, 2012. "Customer Influence Value and Purchase Acceleration in New Product Diffusion," Marketing Science, INFORMS, vol. 31(2), pages 236-256, March.
  • Handle: RePEc:inm:ormksc:v:31:y:2012:i:2:p:236-256
    DOI: 10.1287/mksc.1110.0701
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    References listed on IDEAS

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    1. Ann van Ackere & Diane J. Reyniers, 1995. "Trade-ins and Introductory Offers in a Monopoly," RAND Journal of Economics, The RAND Corporation, vol. 26(1), pages 58-74, Spring.
    2. 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.
    3. Peres, Renana & Muller, Eitan & Mahajan, Vijay, 2010. "Innovation diffusion and new product growth models: A critical review and research directions," International Journal of Research in Marketing, Elsevier, vol. 27(2), pages 91-106.
    4. Teck-Hua Ho & Sergei Savin & Christian Terwiesch, 2002. "Managing Demand and Sales Dynamics in New Product Diffusion Under Supply Constraint," Management Science, INFORMS, vol. 48(2), pages 187-206, February.
    5. Donald Lehmann & Mercedes Esteban-Bravo, 2006. "When giving some away makes sense to jump-start the diffusion process," Marketing Letters, Springer, vol. 17(4), pages 243-254, December.
    6. Frank M. Bass, 1969. "A New Product Growth for Model Consumer Durables," Management Science, INFORMS, vol. 15(5), pages 215-227, January.
    7. David Godes & Dina Mayzlin, 2009. "Firm-Created Word-of-Mouth Communication: Evidence from a Field Test," Marketing Science, INFORMS, vol. 28(4), pages 721-739, 07-08.
    8. Christophe Van den Bulte & Yogesh V. Joshi, 2007. "New Product Diffusion with Influentials and Imitators," Marketing Science, INFORMS, vol. 26(3), pages 400-421, 05-06.
    9. Rabik Ar Chatterjee & Jehoshua Eliashberg, 1990. "The Innovation Diffusion Process in a Heterogeneous Population: A Micromodeling Approach," Management Science, INFORMS, vol. 36(9), pages 1057-1079, September.
    10. Sergei Savin & Christian Terwiesch, 2005. "Optimal Product Launch Times in a Duopoly: Balancing Life-Cycle Revenues with Product Cost," Operations Research, INFORMS, vol. 53(1), pages 26-47, February.
    11. Jacob Goldenberg & Oded Lowengart & Daniel Shapira, 2009. "Zooming In: Self-Emergence of Movements in New Product Growth," Marketing Science, INFORMS, vol. 28(2), pages 274-292, 03-04.
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