Research On The Optimal Reward Size Problem In The Referral Reward Program

In recent years,referral reward program(RRP)gains widely attention from the corporate and academic worlds as a way to acquire new customers by offering rewards.In the RRP formulating process,reward size design is a critical issue.Previous studies mostly conducted from a single perspective such as the current customer perspective or the new customer perspective,but few involves comprehensive consideration of both new and current customers.Due to analysis results differences from different perspectives,it is difficult for companies to determine the optimal reward size based on previous researches.The purpose of this paper is to study the effect of different sized referral rewards from a more comprehensive perspective concluding both current and new customers.The first research of this paper put forward a analysis framework based on data envelopment analysis(DEA),propensity score matching(PSM),and ordinary least squares(OLS).This paper evaluated the different sized referral reward for a domestic digital content platform based on this analysis framework.Results show that larger sized referral rewards do not always means better effect.Besides,companies need to use various operation strategies or activities to increase customers’ product understanding for the better referral reward program effect.Referral rewards evaluation compared different sized referral reward but the optimal referral reward size was still unknown.Thus the second research of this paper analyzed the game between the company and the customers based on a Starkelberg game,as well as analyzed the optimal referral reward structure and relevant influencing factors.Analysis shows that there are four main referral reward strategies:no reward,only rewarding current customers,only rewarding new customers,and rewarding both current and new customers.The company’s rewarding strategy is influenced by many factors such as the expectation on new customers,product price and elasticity,and current customer referral ability.Low price usually means restricted profit for companies,thus the company generally does not provide referral rewards unless there are a high referral ability of current customer makes the expected profit good.The high expectation on new customers can make firms inclined to reward current customers or new customers one side.High product elasticity gives firms more motivation to utilize referral reward to capture profits from the current customers side.Compared with previous studies,this paper takes more current customer role into account in the referral reward program.This is more in line with the situation of demographic dividend disappearing and internet users saturation approaching.Based on the findings of this paper,we recommend that companies should focus more on current customers and new customer long-term value in referral reward programs.Besides,companies should differentiate the rewards for new and old customers to improve the referral reward program effect.