Designing Price Incentives in a Network with Social Interactions

The recent ubiquity of social networks allows firms to collect unprecedented volumes of data about their customers, their buying behavior, their social interaction with other customers and on the structure of the network. The challenge, which confronts every firm, from big to small, is how to process this data and turn it into actionable policies so as to increase profitability. In this paper, we focus on how a firm that is selling indivisible goods can tap into the wealth of data potentially available through social networks (such as Twitter and Facebook) and design effective pricing strategies to improve profitability. According to a recent study, 74% of consumers rely on social networks to guide their purchase decisions (SproutSocial). It is clear that people influence each other willingness to pay for an item. For example, when an individual buys an item and posts a positive review on his Facebook page or tweets this information to his followers, he can influence his friends to also purchase the same item or service. According to other recent surveys, consumers are 71% more likely to make a purchase based on social media referrals (Hubspot) and 81% of US respondents indicated that friends’ social media posts directly influenced their purchase decision (Forbes). As a result, we believe that by tapping into the wealth of data on social interactions, we can improve pricing strategies for retailers and consequently increase profitability. Our goal is to propose a tractable model that incorporates social interactions between agents within a network and design efficient algorithms that compute optimal price incentives for each buyer. We also show how this improves the retailer’s profitability relative to the case where the social interactions are ignored. Based on our models and algorithms, we also want to numerically understand the value of incorporating social connections, the impact of the network topology and develop some insights on pricing influencers.