We propose a new model to describe consideration, consisting of a multivariate probit model component for consideration and a multinomial probit model component for choice, given consideration. The approach allows one to analyze stated consideration set data, revealed consideration set (choice)...

In this paper various ensemble learning methods from machine learning and statistics are considered and applied to the customer choice modeling problem. The application of ensemble learning usually improves the prediction quality of flexible models like decision trees and thus leads to improved...

We propose a new reference price framework for brand choice. In this framework, we employ a Markov-switching process with an absorbing state to model unobserved price recall of households. Reference prices result from the prices households are able to remember. Our model can be used to learn how...