Optimizing e-referral reward programs in the presence of uncertain conversion rate

Despite the widespread adoption of e-referral reward programs by enterprises, existing research provides limited insights into their optimal design, particularly in the context of recommendation uncertainty. This study develops a unified framework to investigate the design of optimal referral programs among three strategies: the no-referral-consideration strategy, the no-reward-offering strategy, and the referral reward strategy. Specifically, we consider conversion rate uncertainty as the driving mechanism of strategic consumer behavior. Counter-intuitively, we find that the no-referral-consideration strategy is not always detrimental to firms. When consumers’ psychological cost for failed recommendations is relatively high, the no-referral-consideration strategy outperforms the no-reward-offering strategy. Only when the discount factor of perceived value is high, and the psychological cost is moderate, does the e-referral reward strategy emerge as the optimal strategy. Overall, this study highlights the effect of conversion rate uncertainty on consumer behavior and provides valuable guidance for firms in designing optimal referral programs.