Assortment and Price Optimization Under a Multiattribute (Contextual) Choice Model

Context-Dependent Choice and Retail Decisions Traditional assortment models assume that consumers evaluate products independently of the alternatives available (i.e., the “context”). In “Assortment and Price Optimization Under a Multiattribute (Contextual) Choice Model,” the authors challenge this assumption by analyzing assortment and pricing decisions under a context-dependent choice framework known as the contextual concavity (CC) model. The CC model incorporates reference dependence across multiple attributes, such as price and quality, and captures well-documented context effects, including compromise and decoy effects. The study makes several contributions. It characterizes the structure of optimal assortments under multiattribute loss aversion, develops a polynomial-size mixed-integer linear programming formulation for solving the general problem, and analyzes the joint assortment and pricing decision. Numerical experiments show that ignoring context effects, by relying on standard context-independent models such as the multinomial logit, can lead to substantial profit losses, with gaps ranging from 3% to 63%. These findings highlight the strategic importance of incorporating contextual effects into retail decisions.