My dissertation extends the traditional fields of revenue management and dynamic pricing to newer markets. Specifically, my first two chapters explore the revenue management strategies and their impacts in the airline industry in the presence of loyalty programs. The first chapter solves the optimal revenue management algorithms when the firm is rewarding frequent customers with free capacity. Using a game-theoretic Littlewood model, we show that limiting award capacity can increase profits by enhancing loyalty award values; airlines can benefit from transitioning from mileage-based programs to revenue-based programs by simplifying its revenue management algorithm and allowing 100% award availability. The second chapter investigates customers' evaluations of loyalty program points. By fitting a Multinomial Logit model on DB1B data set, we calibrate customers' valuations for loyalty points at the issuance and redemption. We have two main conclusions: consumers are rational about the value of miles at issuance, but underestimate and overspend miles at redemption; higher award availability and more award choices lead to higher values of Loyalty points. Finally, my third chapter examines the impact of dynamic pricing in the ride-sharing economy. By using actual Uber pricing and partner data, the paper shows that ride-sharing platforms can efficiently signal market conditions, stimulate desirable agents' behavior, and reduce marketplace frictions through dynamic pricing.