Three Essays on Data-driven Revenue Management and Pricing

In this thesis we study data-driven approaches in the context of revenue management and pricing. In Chapter 2 we study the problem when a firm sets prices for products based on the transaction data, i.e., which product past customers chose from an assortment and what were the historical prices that they observed. Our approach does not impose a model on the distribution of the customers’ valuations and only assumes, instead, that purchase choices satisfy incentive-compatible constraints. The individual valuation of each past customer can then be encoded as a polyhedral set, and our approach maximizes the worst-case revenue assuming that new customers’ valuations are drawn from the empirical distribution implied by the collection of such polyhedra. We study the single-product case analytically and relate it to the traditional model-based approach. Moreover, we show that the optimal prices in the general case can be approximated at any arbitrary precision by solving a compact mixed-integer linear program. We also design three approximation strategies that are of low computational complexity and interpretable. In particular, the cut-off pricing heuristic has a competent provable performance guarantee. Comprehensive numerical studies based on synthetic and real data suggest that our pricing approach is uniquely beneficial when the historical data has a limited size or is susceptible to model misspecification. In Chapter 3 we study the potential negative impact of imbalanced compensation schemes on firm performance. We use a dataset from a radiology workflow platform that connects off-site radiologists with hospitals. These radiologists select tasks from a common pool, while service level is defined by priority-specific turnaround time targets. However, imbalances between pay and workload of different tasks could result in higher priority tasks with low pay-to-workload ratio receiving poorer service. We investigate this hypothesis, showing turnaround time is decreasing in pay-to-workload for lower priority tasks, whereas it is increasing in workload for high-priority tasks. Crucially, we find evidence of a spillover effect: Having many economically attractive tasks with low priority can lead to longer turnaround times for higher priority tasks, increasing the likelihood their likelihood of delay. In Chapter 4 we propose to use Deep Neural Networks to solve data-driven stochastic optimization problems. Given the historical data of the observed covariate, taken decision, and the realized cost in past periods, we train a neural network to predict the objective value as a function of the decision and the covariate. Once trained, for a given covariate, we optimize the neural network over the decision variable using gradient-based methods because the gradient and the Hessian matrix can be analytically computed. We characterize the performance our methodology based the generalization bound of the neural network. We show strong performance on two signature problems in operations management, the newsvendor problem and the assortment pricing problem.

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