Hierarchical Bayes is an advanced technique for computing individual- level part worths from CBC data. HB has been described favorably in numerous journal articles. Its strongest point of differentiation is its ability to provide estimates of individual part worths given only a few choices by each individual. It does this by "borrowing" information from other individuals. This technical paper describes the intuition and math behind HB, including results that suggest that HB is generally superior relative to aggregate approaches for estimating individual's choices and aggregate share predictions.

CCEA (Convergent Cluster & Ensemble Analysis) is Sawtooth Software’s standalone desktop product for k-means cluster analysis (CCA) and cluster ensemble analysis. It is appropriate for finding clusters of respondents using metric-scaled basis variables. When applying cluster ensembles, CCEA can automatically build an ensemble of dozens of candidate solutions, or the user may specify in a .CSV file a custom ensemble of candidate solutions.

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.

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 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.

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.

Adaptive Choice-Based Conjoint (ACBC) is a new approach for adaptive choice-based conjoint studies. The interview has three main phases: 1) BYO (configuration) phase, 2) Consideration phase, 3) Choice phase. This follows the common proposition that buyers develop consideration sets and then choose a final product from within the consideration set. The interview adapts to each respondent, giving a more relevant, interactive experience. ACBC is recommended for experienced conjoint analysts and for projects involving about 5 or more attributes. Traditional brand-package-price studies should continue to be conducted under the standard (non-adaptive) CBC software.

Hierarchical Bayes is an advanced technique for computing individual- level estimates of regression coefficients or part worths. HB has been described favorably in numerous journal articles. Its strongest point of differentiation is its ability to provide estimates of individual parameters given only a few observations by each individual. It does this by "borrowing" information from other individuals.

HB-Reg is a generalized software program for running Regression-based HB. The user provides the data in an Excel-compatible .csv file. Potential uses for HB-Reg include traditional ratings-based conjoint experiments, customer satisfaction studies, or price elasticity measurement from scanner data.

This technical paper describes the intuition and math behind HB, including results that suggest that HB is generally superior relative to aggregate approaches for estimating individual's regression coefficients or part worths for conjoint experiments.

This paper describes the technical procedures used in the MaxDiff System. MaxDiff (best-worst) scaling is a trade-off method for measuring the importance or preference for multiple items, such as brands, product features, political platforms, advertising claims, etc. Any time you are considering using a rating scale, ranking scale, or constant sum scale for multiple items, you can consider using MaxDiff.

The MaxDiff methodology, originally invented by researcher and academic Jordan Louviere, has gained in popularity over the last five years. Papers on MaxDiff have won "best presentation" awards at recent ESOMAR and Sawtooth Software research conferences. It has many similarities to, but is distinctively different, from conjoint methodology and is appropriate for a wider range of research opportunities.

Sawtooth Software’s MaxDiff System may be used for conducting web-based, CAPI, or paper-based MaxDiff studies. The software also supports asking the "best" half of the question only (not requiring respondents to identify the "worst" item in each set). The software may also be used for Method of Paired Comparisons research. Individual-level estimation of item scores employs Sawtooth Software’s popular hierarchical Bayes (HB) engine. Results may also be analyzed with the integrated Latent Class procedure for segmentation analysis.

Some CBC projects do not fit the traditional mold (full-profile, common attributes, limited attributes and levels). The Advanced Design Module (ADM) for CBC gives the researcher additional capabilities:

• Alternative-specific plans
• Partial-profile interviewing format
• Capacity extended to 250 attributes (CBC/Web v8 only)
• Capacity extended to 254 levels per attribute, and 100 concepts per task (CBC/Web only)
• Shelf-facing display (CBC/Web only)

This paper covers the intuition and quantitative concepts behind these more advanced approaches. With the Advanced Design Module, researchers are better equipped to handle a variety of challenging requests and modeling opportunities.

Adaptive Conjoint Analysis (ACA) is software for conjoint (trade-off) analysis. The term "adaptive" refers to the fact that the computer-administered interview is customized for each respondent. Data are analyzed as the interview progresses, and we choose questions likely to reveal the most about the respondent's values in the shortest time. ACA is classic conjoint technique first released in the 1980s. Although it was the most popular conjoint methodology during the 1990s, it is less often used today. This paper provides a description of the adaptive technique, including technical details.