Discrete Choice Modeling

Also known as: Discrete Choice, Choice Modeling, Logit Model, Random Utility Model

Statistical models predicting choice between discrete options, simulating consumer decision-making behavior.

Discrete Choice Modeling is a set of statistical techniques—primarily multinomial Logit and Probit—that models and predicts how consumers choose between discrete alternatives (A, B, C) based on the characteristics of the options and the individuals.

It is the statistical foundation of Choice-Based Conjoint Analysis. Its applications in research include: market share simulation under different price and product scenarios, prediction of the impact of competitor entries, and product portfolio optimization.

Advanced models include Mixed Logit, Latent Class Logit (which allows identifying preference segments within the data), and random utility models. With AI, it is possible to integrate contextual and emotional variables that classical models didn't capture.