Optimal referral bonuses with asymmetric information: Firm-offered and interpersonal incentives

Referral bonuses, in which an existing customer gets an in-kind or cash reward for referring a new customer, are a popular way to stimulate word of mouth. In this paper, we examine key firm decisions about such bonuses. Others have studied referral bonus programs; a key difference is that we study the role of recommendations not just in spreading awareness (as they do) but also in providing assessments. We start with the idea that people have a variety of reasons for making product recommendations, including placing a value on a friend's outcome with a product they recommend. We apply that idea in a context of asymmetric information: A customer combines his knowledge about the product and his familiarity with friends' tastes, making him more informed than the friends. Thus, the recommendation is a signal about the value of the product to the friend. In this setting, we consistently find that the greater the concern for others' outcomes, the higher the referral bonus should be, as long as the firm cannot more efficiently motivate recommendations with a lower price. Moreover, if price is the more efficient lever, the optimal bonus is zero, and the optimal price is low. We also show that greater concern tends to reduce firm profit and, in some cases, actually reduces consumer welfare as well.