Data-driven optimization with behavioral considerations : applications to pricing

Abstract

This thesis aims to introduce descriptive and predictive models that guide more informed pricing strategies in practice, drawing from interdisciplinary work of current OM, behavioral theories and recent machine learning advances. In chapter 2, we integrate a consumer purchase experiment and an analytical model to investigate how consumers' price-based quality perception, expected markdown, and a product's availability information influence a retailer's markdown pricing strategy. We subsequently develop a consumer model that incorporates consumers' price-based quality perception observed from the experimental data and consumers' potential loss aversion. We embed this consumer model into the retailer's markdown optimization and examine the impact of these behavioral factors on the retailer's optimal strategy.

In chapter 3, we study a retailer's optimal promotion strategy when demand is affected by different classes of customers' status in the rewards program and their heterogeneous redemption behavior. We formulate the retailer's problem as a dynamic program and prove a unique optimal threshold discounting policy. We also propose an approximation algorithm of the optimal price as a convex combination of the optimal prices for each class separately. Using data from a fast food chain, we assess the performance of the algorithm and the optimal pricing compared to current practice. In chapter 4, we are concerned with accurately estimating price sensitivity for listed tickets in the secondary market. In the presence of endogeneity, binary outcomes and non-linear interactions between ticket features, we introduce a novel loss function which can be solved using several off-the-shelf machine learning methods.

On a wide range of synthetic data sets, we show that our approach beats state-of-the-art machine learning and causal inference approaches for estimating treatment effects in the classification setting. In chapter 5, we consider an optimization problem with a random forest objective function and general polyhedral constraints. We formulate this problem using Mixed Integer Optimization techniques and show it can be solved to optimality efficiently using Pareto-optimal Benders cuts. We prove analytical guarantees for a random forest approximation that consists of only a subset of trees. We also propose heuristics inspired by cross-validation and assess their performance on two real-world case