In my dissertation, I explore different aspects of influencer marketing and provide managerial implications on how influencer marketing can become more effective. In particular, which influencers should firms sponsor and what influencer-generated content can better leverage the viral dynamics on social media? In the first chapter, I strategically model the firm’s cooperation with honest influencers, analyze when the firm benefits from such cooperation and its optimal “design” of the influencer’s credible message through quality revelation and product selection. I show that the firm can asymmetrically gain from the uncertainties introduced through the influencer’s message that shift the consumers’ beliefs in its preferred way. Noisier message increases the firm’s profit when sponsoring mediocre products. In the second chapter, I study the recently hot debate between sponsoring mega vs. micro influencers, and in particular, offer firms an important metric to consider for optimizing their influencer selection strategies: the follower elasticity of impressions (FEI). Computing FEI involves estimating the causal effect of an influencer’s popularity on the view counts of their videos, which I achieve through a combination of a unique dataset collected from TikTok, a representation learning model for quantifying video content, and a machine learning-based causal inference method. I find that on average, FEI is always positive, but often nonlinear with respect to the number of followers. Then, I examine the factors that predict variation in these FEI curves, and show how firms can use these heterogeneous FEIs to better determine influencer partnerships. In general, one the one hand, my results challenge common firm strategies of sponsoring very popular influencers, and offer them alternative, data-driven strategies for optimizing their influencer selection based on the advertised content. On the other hand, I also highlight the similarities as well as the differences between influencer marketing and traditional advertising (mostly TV).