Essays on Social Media and Digital Marketing

Digital technology is rapidly reshaping the way how brands interact with consumers. More and more marketers are shifting their focus from traditional marketing channels (e.g., TV) to digital channels (e.g., social media platforms). Effective targeting is key to successful social media and digital marketing campaigns. This dissertation seeks to shed light on who and how to target on social media platforms. The first chapter aims to provide insights on how to target customers who are connected to each other on social media platforms. We investigate how the network embeddedness (i.e., number of common followees, common followers, and common mutual followers) between two users impacts information diffusion from one (sender) to another (receiver). By analyzing the sharing of sponsored ads on Digg and brand-authored tweets on Twitter, we find that the effect of embeddedness in directed networks varies across different types of “neighbors”. A receiver is more likely to share content from a sender if they share more common followees. A receiver is also more likely to share content if she shares more common followers and common mutual followers with the sender. However, this effect is moderated by the novelty of information. The second chapter strives to understand what affects paid endorsers’ participation and effectiveness in social advertising campaigns. We conduct a field experiment with an invitation design in which we manipulate both incentives and a soft eligibility requirement to participate in the campaign. There are three main findings from our analysis. (1) Payments higher than the average reward a potential endorser received in the past (gains) do not increase participation, whereas lower payments (losses) decrease participation. Neither gains nor losses compared to past reward affect performance. (2) Potential endorsers who are more likely to participate tend to be less effective. (3) Which characteristics are associated with effectiveness depends on whether success is measured in likes, comments, or retweets. For marketing managers, our findings provide insights on how to target customers in a directed network at a micro level and how to improve social advertising campaigns by better targeting and incenting potential endorsers.