With the deluge of big data, many retailers are experimenting with rich, data-driven pricing strategies. In this dissertation we study three emerging pricing strategies: (i) Opaque pricing, the pricing of products where some feature is hidden from the customer until after purchase. In a general model we give a sharp characterization for when opaque selling outperforms traditional forms of differentiated pricing. (ii) Personalized pricing, i.e. pricing strategies that predict an individual customer's valuation for a product and then offers them a customized price. Leveraging natural statistics of the valuation distribution, we prove tight upper and lowers on the ratio between personalized pricing strategies and simpler selling strategies, which, among other things, yields insight into which markets personalized pricing is most valuable. (iii) Loot box pricing, the pricing of (random) bundles of virtual items, the contents of which are revealed after purchase. In an asymptotic regime we compare and contrast the revenue of different forms of loot box pricing with traditional selling models, and give theory to explain the recent proliferation of loot boxes in mobile gaming markets.