Should Referral Programs Reward Customers for the Short-Term Performance of Their Referrals?
COM3 Level 1
SR14, COM3 01-23
Abstract:
Referral programs are widely used by firms as a tool for new customer acquisition. In practice, most referral programs reward existing customers for the acquisition of their referrals (i.e., referred customers). Although such acquisition-based referral rewards incentivize existing customers to refer new customers, they can be ineffective in generating high-value referrals. To increase the value created by referral programs, we propose that firms complement their acquisition-based referral reward with an additional reward to existing customers contingent on the short-term performance of their referrals. We tested our proposal using a randomized field experiment conducted at a Chinese firm offering financial deposit services. During a 30-day experimental campaign, existing customers in both the control and treatment conditions were offered the same acquisition-based referral reward, while only those in the treatment condition could additionally receive a performance-based referral reward for each referral whose total investment made during the campaign in selected financial deposits met a predefined threshold. Assessing the value of referred customers acquired during the campaign based on their investment behavior over a 480-day period, we find that the introduction of the performance-based referral reward increases the total value of referrals by more than 110%, and this effect is driven primarily by the acquisition of higher-value referred customers rather than more referred customers. We propose two mechanisms for the acquisition of higher-value referred customers, including the performance-based referral reward (1) motivating existing customers to screen their friends and refer good matches to the firm and (2) providing referred customers with an additional incentive to invest. Our data provide suggestive evidence for both mechanisms.
Bio:
Yupeng Chen is an Assistant Professor of Marketing at Nanyang Technological University in Singapore. His research focuses on developing novel machine learning and optimization models for preference estimation and dynamic decision making. He also conducts field experiments to study consumer behavior and evaluate marketing strategies. Yupeng received his Ph.D. in Marketing from the Wharton School.