IDEAS home Printed from https://ideas.repec.org/a/inm/ormksc/v20y2001i1p82-95.html
   My bibliography  Save this article

Customer Referral Management: Optimal Reward Programs

Author

Listed:
  • Eyal Biyalogorsky

    (University of California, Graduate School of Management, Davis, California 95616)

  • Eitan Gerstner

    (University of California, Graduate School of Management, Davis, California 95616)

  • Barak Libai

    (Davidson Faculty of Industrial Engineering and Management, Technion-Israel Institute of Technology, Haifa 32000 Israel)

Abstract

Sellers who plan to capitalize on the lifetime value of customers need to manage the sales potential from customer referrals proactively. To encourage existing customers to generate referrals, a seller can offer exceptional value to current customers through either excellent quality or a very attractive price. Rewards to customers for referring other customers can also encourage referrals. We investigate when referral rewards should be offered to motivate referrals and derive the optimal combination of reward and price that will lead to the most profitable referrals. We define a delighted customer as one who obtains a positive level of surplus above a threshold level and, consequently, recommends the product to another customer. We show that the use of referral rewards depends on how demanding consumers are before they are willing to recommend (i.e., on the delight threshold level). The optimal mix of price and referral reward falls into three regions: (1) When customers are easy to delight, the optimal strategy is to lower the price below that of a seller who ignores the referral effect but not to offer rewards. (2) In an intermediate level of customer delight threshold, a seller should use a reward to complement a low-price strategy. As the delight threshold gets higher in this region, price should be higher and the rewards should be raised. (3) When the delight threshold is even higher, the seller should forsake the referral strategy all together. No rewards should be given, and price reverts back to that of a seller who ignores referrals. These results are consistent with the fact that referral rewards are not offered in all markets. Our analysis highlights the differences between lowering price and offering rewards as tools to motivate referrals. Lowering price is attractive because the seller “kills two birds with one stone”: a lower price increases the probability of an initial purchase and the likelihood of referral. Unfortunately, a low price also creates a “free-riding” problem, because some customers benefit from the low price but do not refer other customers. Free riding becomes more severe with an increasing delight threshold; therefore, motivating referrals through low price is less attractive at high threshold levels. A referral reward helps to alleviate this problem, because of its “pay for performance” incentive (only actual referrals are rewarded.) Unfortunately, rewards can sometimes be given to customers who would have recommended anyway, causing a waste of company resources. The lower the delight threshold level, the bigger the waste and, therefore, motivating referrals through rewards loses attractiveness. Our theory highlights the advantage of using referral rewards in addition to lowering price to motivate referrals. It explains why referral programs are offered sometimes but not always and provides guidelines to managers on how to set the price and reward optimally.

Suggested Citation

  • Eyal Biyalogorsky & Eitan Gerstner & Barak Libai, 2001. "Customer Referral Management: Optimal Reward Programs," Marketing Science, INFORMS, vol. 20(1), pages 82-95, August.
  • Handle: RePEc:inm:ormksc:v:20:y:2001:i:1:p:82-95
    DOI: 10.1287/mksc.20.1.82.10195
    as

    Download full text from publisher

    File URL: http://dx.doi.org/10.1287/mksc.20.1.82.10195
    Download Restriction: no

    File URL: https://libkey.io/10.1287/mksc.20.1.82.10195?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    References listed on IDEAS

    as
    1. Bearden, William O & Etzel, Michael J, 1982. "Reference Group Influence on Product and Brand Purchase Decisions," Journal of Consumer Research, Journal of Consumer Research Inc., vol. 9(2), pages 183-194, September.
    2. Dan Horsky, 1990. "A Diffusion Model Incorporating Product Benefits, Price, Income and Information," Marketing Science, INFORMS, vol. 9(4), pages 342-365.
    3. John R. Hauser & Duncan I. Simester & Birger Wernerfelt, 1994. "Customer Satisfaction Incentives," Marketing Science, INFORMS, vol. 13(4), pages 327-350.
    4. Bruce Robinson & Chet Lakhani, 1975. "Dynamic Price Models for New-Product Planning," Management Science, INFORMS, vol. 21(10), pages 1113-1122, June.
    5. Claes Fornell & Birger Wernerfelt, 1988. "A Model for Customer Complaint Management," Marketing Science, INFORMS, vol. 7(3), pages 287-298.
    6. Shlomo Kalish, 1983. "Monopolist Pricing with Dynamic Demand and Production Cost," Marketing Science, INFORMS, vol. 2(2), pages 135-159.
    7. Wujin Chu & Preyas S. Desai, 1995. "Channel Coordination Mechanisms for Customer Satisfaction," Marketing Science, INFORMS, vol. 14(4), pages 343-359.
    8. Brown, Jacqueline Johnson & Reingen, Peter H, 1987. "Social Ties and Word-of-Mouth Referral Behavior," Journal of Consumer Research, Journal of Consumer Research Inc., vol. 14(3), pages 350-362, December.
    9. Bearden, William O & Netemeyer, Richard G & Teel, Jesse E, 1989. "Measurement of Consumer Susceptibility to Interpersonal Influence," Journal of Consumer Research, Journal of Consumer Research Inc., vol. 15(4), pages 473-481, March.
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Wei-yu Kevin Chiang, 2012. "Supply Chain Dynamics and Channel Efficiency in Durable Product Pricing and Distribution," Manufacturing & Service Operations Management, INFORMS, vol. 14(2), pages 327-343, April.
    2. Yadav, Manjit S. & de Valck, Kristine & Hennig-Thurau, Thorsten & Hoffman, Donna L. & Spann, Martin, 2013. "Social Commerce: A Contingency Framework for Assessing Marketing Potential," Journal of Interactive Marketing, Elsevier, vol. 27(4), pages 311-323.
    3. Adil Baykasoğlu & İlker Gölcük & Derya Eren Akyol, 2017. "A fuzzy multiple-attribute decision making model to evaluate new product pricing strategies," Annals of Operations Research, Springer, vol. 251(1), pages 205-242, April.
    4. Landsman, Vardit & Nitzan, Irit, 2020. "Cross-decision social effects in product adoption and defection decisions," International Journal of Research in Marketing, Elsevier, vol. 37(2), pages 213-235.
    5. Chaab, Jafar & Zaccour, Georges, 2024. "Dynamic pricing in the presence of social externalities and reference-price effect," Omega, Elsevier, vol. 122(C).
    6. Marius F. Niculescu & Hyoduk Shin & Seungjin Whang, 2012. "Underlying Consumer Heterogeneity in Markets for Subscription-Based IT Services with Network Effects," Information Systems Research, INFORMS, vol. 23(4), pages 1322-1341, December.
    7. Laurent Bertrandias & Paul-Emmanuel Pichon, 2004. "Enrichissements De La Conceptualisation Du Risque Social En Marketing Et Construction D'Une Echelle De Mesure," Post-Print hal-04097759, HAL.
    8. Jaeki Song & Fatemeh Mariam Zahedi, 2005. "A Theoretical Approach to Web Design in E-Commerce: A Belief Reinforcement Model," Management Science, INFORMS, vol. 51(8), pages 1219-1235, August.
    9. Zhang, Jie & Chiang, Wei-yu Kevin, 2020. "Durable goods pricing with reference price effects," Omega, Elsevier, vol. 91(C).
    10. Michael Braun & André Bonfrer, 2011. "Scalable Inference of Customer Similarities from Interactions Data Using Dirichlet Processes," Marketing Science, INFORMS, vol. 30(3), pages 513-531, 05-06.
    11. Régis Chenavaz & Corina Paraschiv & Gabriel Turinici, 2017. "Dynamic Pricing of New Products in Competitive Markets: A Mean-Field Game Approach," Working Papers hal-01592958, HAL.
    12. Payal S. Kapoor & K.R. Jayasimha & Ashish Sadh, 2013. "Brand-related, Consumer to Consumer, Communication via Social Media," IIM Kozhikode Society & Management Review, , vol. 2(1), pages 43-59, January.
    13. Yan, Xiaoming & Liu, Ke, 2009. "Optimal control problems for a new product with word-of-mouth," International Journal of Production Economics, Elsevier, vol. 119(2), pages 402-414, June.
    14. Ostovan, Nima & Khalili Nasr, Arash, 2022. "The manifestation of luxury value dimensions in brand engagement in self-concept," Journal of Retailing and Consumer Services, Elsevier, vol. 66(C).
    15. Harikesh Nair, 2007. "Intertemporal price discrimination with forward-looking consumers: Application to the US market for console video-games," Quantitative Marketing and Economics (QME), Springer, vol. 5(3), pages 239-292, September.
    16. Jalees, Tariq & Tariq, Huma & Zaman, Syed Imran & Alam Kazmi, Syed Hasnain, 2015. "Social Media in Virtual Marketing," MPRA Paper 69868, University Library of Munich, Germany, revised 10 Apr 2015.
    17. Régis Chenavaz & Corina Paraschiv & Gabriel Turinici, 2021. "Dynamic Pricing of New Products in Competitive Markets: A Mean-Field Game Approach," Dynamic Games and Applications, Springer, vol. 11(3), pages 463-490, September.
    18. 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.
    19. Neil A. Morgan & Lopo Leotte Rego, 2006. "The Value of Different Customer Satisfaction and Loyalty Metrics in Predicting Business Performance," Marketing Science, INFORMS, vol. 25(5), pages 426-439, September.
    20. Sheng-Hsiung Chang, 2015. "The Influence of Green Viral Communications on Green Purchase Intentions: The Mediating Role of Consumers’ Susceptibility to Interpersonal Influences," Sustainability, MDPI, vol. 7(5), pages 1-21, April.

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:inm:ormksc:v:20:y:2001:i:1:p:82-95. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Chris Asher (email available below). General contact details of provider: https://edirc.repec.org/data/inforea.html .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.