In this thesis, we study three revenue management problems where we propose simple algorithms with provable guarantees. While online marketplaces provide retailers with tremendous flexibility, they are often large, noisy, have multiple stakeholders, and could be more challenging to characterize. These complexities give rise to a preference for simple, interpretable policies. Further, traditional marketplaces such as brick-and-mortar stores cannot always leverage tools designed for online environments due to physical constraints, higher latency, etc. With these motivations in mind, we develop algorithms for assortment optimization and pricing that are easy to implement in practice and have theoretical justifications for their performance. In Chapter 1, we consider a dynamic assortment optimization problem where the seller has a fixed inventory of multiple substitutable products to sell over a fixed time horizon. We consider two modifications to the traditional problem. First, we simplify the assortment planning by restricting assortment changes to "product retirements". When a product is retired, it becomes unavailable to all future customers. Second, we assume the seller has flexibility regarding which customers to approach. In each period, the seller chooses which subset of products to retire and selects a customer to visit. The selected customer then receives an option to purchase one of the available products, i.e., non-retired products with positive remaining inventory. We provide two policies for this problem. Our first policy guarantees a constant fraction of the best possible revenue. Our second policy is near-optimal but requires the problem to have a specific structure. In Chapter 2, we study the fundamental joint pricing and inventory management problem. The optimal policy for the model we consider is known to be an (s, S, p) policy: when the inventory level drops to s units, the seller immediately places an order to replenish the inventory to S units. Specifically, the optimal pricing policy p has a different price for every inventory state. We proposed simple policies requiring no more than three prices and prove that these policies are near-optimal compared to optimal policies which require more prices and are less robust. In particular, when orders cannot be backlogged, we show that a single price is sufficient for good performance. In Chapter 3, we analyze assortment optimization and pricing with opaque products. An opaque product is one for which only partial information is available to the buyer at the time of purchase. When a customer selects the opaque product, the seller can fulfill the purchase using any of the offered products. Opaque products can help sellers boost total sales. We propose simple policies for assortment optimization with provable constant factor guarantees, which are near-optimal in numerical experiments. We also provide upper bounds for the advantage of selling opaque products.