Deal selection on Groupon represents a typical social learning and decision-making process, where the quality of a deal is usually unknown to the customers. The customers must acquire this knowledge through social learning from other social media, such as reviews on Yelp. Additionally, the quality of a deal depends on both the state of the vendor and the decisions of other customers on Groupon. How social learning and network externality affect the decisions of customers in deal selection on Groupon is the main focus of this chapter. We develop a data-driven game-theoretic framework to understand rational deal selection behaviors across social media. The sufficient condition of the Nash equilibrium is identified. A value-iteration algorithm is utilized to find the optimal deal selection strategy. We utilize the Groupon–Yelp data set to analyze the deal selection game in a realistic setting. Finally, the performance of the social learning framework is evaluated using real data. The results suggest that customers make decisions in a rational way instead of following naive strategies, and there is still room to improve their decisions with assistance from a game-theoretic framework.