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Customer Referral Incentives and Social Media

Author

Listed:
  • Ilan Lobel

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

  • Evan Sadler

    (Harvard University, Cambridge, Massachusetts 02138)

  • Lav R. Varshney

    (Department of Electrical and Computer Engineering, University of Illinois at Urbana–Champaign, Urbana, Illinois 61801)

Abstract

We study how to optimally attract new customers using a referral program. Whenever a consumer makes a purchase, the firm gives her a link to share with friends, and every purchase coming through that link generates a referral payment. The firm chooses the referral payment function and consumers play an equilibrium in response. The optimal payment function is nonlinear and not necessarily monotonic in the number of successful referrals. If we approximate the optimal policy using a linear payment function, the approximation loss scales with the square root of the average consumer degree. Using a threshold payment, the approximation loss scales proportionally to the average consumer degree. Combining the two, using a linear payment function with a threshold bonus, we can achieve a constant bound on the approximation loss.

Suggested Citation

  • Ilan Lobel & Evan Sadler & Lav R. Varshney, 2017. "Customer Referral Incentives and Social Media," Management Science, INFORMS, vol. 63(10), pages 3514-3529, October.
  • Handle: RePEc:inm:ormnsc:v:63:y:2017:i:10:p:3514-3529
    DOI: 10.1287/mnsc.2016.2476
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    References listed on IDEAS

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    Cited by:

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    3. Choi, Tsan-Ming & Guo, Shu & Luo, Suyuan, 2020. "When blockchain meets social-media: Will the result benefit social media analytics for supply chain operations management?," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 135(C).
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    5. Zhan, Yuanzhu & Xiong, Yu & Xing, Xinjie, 2023. "A conceptual model and case study of blockchain-enabled social media platform," Technovation, Elsevier, vol. 119(C).
    6. Yang Zhang & Ying-Ju Chen, 2020. "Optimal Nonlinear Pricing in Social Networks Under Asymmetric Network Information," Operations Research, INFORMS, vol. 68(3), pages 818-833, May.
    7. Li, Jian & Zhou, Junjie & Chen, Ying-Ju, 2021. "The Limit of Targeting in Networks," ISU General Staff Papers 202112081957590000, Iowa State University, Department of Economics.
    8. Vahideh Manshadi & Sidhant Misra & Scott Rodilitz, 2020. "Diffusion in Random Networks: Impact of Degree Distribution," Operations Research, INFORMS, vol. 68(6), pages 1722-1741, November.
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    11. Yan Song & Xin Tian, 2020. "Managerial Responses and Customer Engagement in Crowdfunding," Sustainability, MDPI, vol. 12(8), pages 1-13, April.

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