Modeling, Prediction, Assortment and Price Optimization Under Consumer Choice Behavior presents a comprehensive overview of the theoretical framework, model estimation techniques, challenges, recent advances, and future directions in the area of discrete consumer choice modeling. By dissecting these models and the mechanics behind them, this monograph aims to illuminate the methodological underpinnings and practical applications of discrete consumer choice modeling, offering researchers, practitioners, and policymakers valuable insights into this rich and evolving field.

Section 1 provides a historical overview of the evolution of discrete consumer choice modeling and highlights key studies and models in this field. Section 2 delves into the theoretical framework of the most important discrete choice model, the Multinomial Logit (MNL) choice model. Section 3 covers interesting extensions built upon the classic MNL model. Section 4 presents other discrete choice models including the Nested Logit and Mixed Logit models. Section 5 focuses on the pricing problems under the discrete choice models. Section 6 identifies and discusses the challenges of the assortment optimization problems under various consumer choice models. Section 7 focuses on model estimation techniques, such as maximum likelihood estimation and expectation-maximization (EM) algorithm. It also explores the recent advances in discrete consumer choice modeling, including the integration of artificial intelligence and machine learning. Finally, Section 8 points out opportunities for further research and then concludes the work with a summary of the key points and concluding remarks.