Paper Number
1223
Paper Type
Complete
Description
Referral reward programs are a popular word-of-mouth marketing strategy, but determining the optimal reward size remains a challenge. While larger rewards incentivize referrals, they can negatively impact new customer profitability. This study addresses this trade-off by examining the combined effect of reward size on both new and existing customer profitability. We conducted a field experiment and applied a novel DEA-PSM-OLS framework to analyze data from a digital content company. Our findings reveal that a moderate reward size outperforms both small and large rewards, suggesting that maximizing referral program effectiveness requires a balanced approach that considers the value generated by both new and existing customers.
Recommended Citation
zhou, BAOHUAN; ZHU, YAOZONG; Liang, Liang; WANG, PINGFAN; and Zhu, Jiantao, "Reward More Can Referral Better? Combined New and Current Customers" (2024). ICIS 2024 Proceedings. 7.
https://aisel.aisnet.org/icis2024/digital_emergsoc/digital_emergsoc/7
Reward More Can Referral Better? Combined New and Current Customers
Referral reward programs are a popular word-of-mouth marketing strategy, but determining the optimal reward size remains a challenge. While larger rewards incentivize referrals, they can negatively impact new customer profitability. This study addresses this trade-off by examining the combined effect of reward size on both new and existing customer profitability. We conducted a field experiment and applied a novel DEA-PSM-OLS framework to analyze data from a digital content company. Our findings reveal that a moderate reward size outperforms both small and large rewards, suggesting that maximizing referral program effectiveness requires a balanced approach that considers the value generated by both new and existing customers.
Comments
01-DigitalPlatforms