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Choice-based conjoint (CBC) studies usually specify a fixed number of levels for each attribute. The resulting attribute then becomes categorical. But that doesn’t mean you…

Modern tools for analyzing conjoint analysis, such as hierarchical Bayes, produce rich data showing preferences for each person in a market. The main deliverable from…

Indifference curves are a way of showing relative preferences for quantities of two things (e.g., preferences for price versus delivery times for fast food). This…

In this blog post, I describe the Efficient algorithm for generating choice model designs. This algorithm is used for generating choice model designs with partial profiles,…

While choice-based conjoint analysis represents one of the more sophisticated techniques used in market research, presentation of its results commonly consists only of a simulator,…

In a stated preference discrete choice experiment, respondents are asked a number of questions. Each question asks them to choose between a number of alternatives…

Today, you can produce a wide range of choice model experimental designs with numerous different algorithms. But with all this design diversity, how do you…

Choice experiments, also known as choice-based conjoint (CBC), are widely used for predicting the performance of new products and changes to products’ designs and portfolios.…

This post introduces the key concepts in designing experiments for choice-based conjoint analysis (also known as choice modeling). I use a simple example to describe…