Social referral programs for freemium platforms

Rodrigo Belo, Ting Li

Research output: Contribution to journalArticlepeer-review

1 Citation (Scopus)

Abstract

We examine how freemium platforms can design social referral programs to encourage growth and engagement without sacrificing revenue. On the one hand, social referral programs generate new referrals from users that would not have paid for the premium features. On the other hand, they also attract new referrals from users that would have paid but prefer to invite others---resulting in more referrals but fewer paying users. We use data from a large-scale randomized field experiment in an online dating platform to assess the effects of adding referrals programs to freemium platforms and changing the referral requirements on users' behavior, namely on their decisions to invite, pay and engage with the platform. We find that introducing referral programs in freemium platforms can significantly contribute to increasing the number of referrals at the expense of revenue. Platforms can avoid the loss in revenue by reserving some premium features exclusively for paying users. We also find that increasing referral requirements in social referral programs can work as a double-edged sword. Increasing the referral threshold results in more referrals and in higher total revenue. Yet, these benefits appear to come at a cost. Users become less engaged, decreasing the value of the platform for all users. We explore two mechanisms that help to explain the differences in users' social engagement. Finally, and contrary to prior findings, we find that the quality of the referrals is not affected by the referral requirements. We discuss the theoretical and practical implications of our research.
Original languageEnglish
Pages (from-to)8933-8962
Number of pages30
JournalManagement Science
Volume68
Issue number12
Early online date5 Apr 2022
DOIs
Publication statusPublished - Dec 2022

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