Increasingly, stated preference choice projects involve Menu-Based Choice scenarios (MBC) where respondents can select one to multiple options from a menu. This is not surprising, given the fact that buyers commonly are allowed to customize products and services (mass customization). The software takes a data processing and analysis process that can take experienced analysts from one to two weeks to do and compresses the timeline to 1 to 2 days.MBC requires more expertise to use properly than our other conjoint analysis tools. The user should have solid background in CBC and multivariate statistical modeling, especially in terms of building models (regression, MNL) and the theory behind coding independent variables. While the software manages most details involving the data processing, independent variable coding, model estimation and simulations, the user must understand and direct the process intelligently.

The author discusses practical concerns in analyzing MBC studies. He highlights the value of using randomized designs for MBC questionnaires, as they allow for straightforward counting analysis. Counting analysis helps the analyst decide which effects (own and cross-effects) are important to include in subsequent modeling. Three different approaches for building logit-based simulators are discussed: 1) Volumetric CBC Model, 2) Exhaustive Alternatives Model, and 3) Serial Cross-Effects Model. Both aggregate logit and HB models are estimated. All approaches worked quite well for the data set described here. The Volumetric CBC Model is the easiest to perform, but has weaknesses and is not a theoretically sound approach. The Exhaustive Alternatives model is more theoretically pleasing, but practically is limited to use with smaller menu problems that don't include very many options on the menu. The Serial Cross-Effects model strikes a good balance between flexibility and soundness.

This article discusses Menu-Based Choice (MBC), and how task position affects the results. Specifically, later tasks show greater price sensitivity than earlier tasks.