Choice-Based Conjoint Analysis (CBC) is an integrated component within the Lighthouse Studio platform for conducting choice-based conjoint studies. The main characteristic distinguishing choice-based conjoint analysis from other types is that the respondent expresses preferences by choosing concepts from sets of concepts, rather than by rating or ranking them. This paper discusses the method of choice-based conjoint analysis from a practitioner-oriented point of view, and describes Sawtooth Software's CBC System for choice-based conjoint analysis in some detail. It also provides suggestions about how to select a particular conjoint method from the variety of those available, considering characteristics of the research problem at hand.

The Advanced Simulation Module (ASM) extends the capabilities of the standard Windows-based market simulator to enable product optimization searches, based on the criteria of utility, share, purchase likelihood, revenue, profit or cost minimization. Search routines include hill-climbing methods, exhaustive search, and Genetic Algorithms. Product optimizations are well-suited for finding optimal products considered alone, or relative to a set of competitors. Cost information can be associated with attribute levels in the study. With cost information, the analyst can perform profit maximization searches, or can search for products that maximize some performance threshold relative to a cost limit specified by the user.

Sawtooth Software’s web-based (SaaS--Software as a Service) questionnaire authoring tool is streamlined, attractive, and user-friendly. We’re calling this web-based platform “Discover” and the CBC component within it “Discover-CBC.” Discover-CBC includes the essential aspects of Sawtooth Software’s Lighthouse Studio CBC software and also includes some new features for experimental design. Users will find Discover-CBC easy to use, they’ll be able to collaborate better in teams, and the results should be nearly indistinguishable from our CBC package in Lighthouse Studio. All aspects, from questionnaire authoring, designing CBC tasks, fielding the study, and analyzing the results are managed within the intuitive, browser-based interface.

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.

Latent Class MNL is a utility estimation approach that finds groups (segments) of respondents who have similar preferences as captured via CBC or MaxDiff experiments. Latent Class MNL simultaneously estimates utilities for each segment and the probability that each respondent belongs to each segment. Respondents can be assigned to the group they have the highest probability of belonging to, thus creating a new segmentation variable that you may use in subsequent analyses including cross-tabulations.