Customer-Product Matches in Online Social Referrals: A Graph Embedding Approach

Abstract

New social media has been enlarging the impact of social referrals on product diffusion. There is, however, no direct evidence on how effectively referrers match products with referred customers that, in turn, leads to high returns for online social referrals. To answer this question, we analyzed 137,622 referrals of 20,169 instant apps from 11,668 referrers to 84,166 recipients (those referred), and 1,141,363 (100 per referrer) unique contacts randomly selected from referrers' local social networks excluding the recipients. We leveraged a state-of-the-art graph embedding framework to estimate users' latent preferences for the apps based on a massive amount of user-app historical usage sequences. We find that referrers recommend products to contacts with substantially stronger preferences for the referred products than the non-referred contacts have. Referrers' contacts have significantly greater preferences for the referred products than do their non-contacts. These results confirm the effectiveness of active (actively screening and selecting contacts to match products) and passive (homophily-based) matching in online social referrals. We also find that referrers exhibit the strongest preferences for the referred products. The matching outperforms among narrow-appeal products and for more engaged referrers and those with a smaller local social network. Our findings shed light on the mechanisms and management of social referrals.

Keywords: Social referral, social networks, matching, graph embedding, product preferences