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ACA Technical Paper
"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 an excellent alternative to full-profile conjoint when you have a large number of attributes. This paper provides a description of the adaptive technique, including technical details
ACBC Technical Paper (Adaptive Choice)
"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.
ACA/HB Technical Paper
Hierarchical Bayes is a relatively new technique that can be used in estimating part worths for conjoint analysis experiments. HB has been described favorably in several recent journal articles. Its strongest point of differentiation is its ability to provide estimates of individual part worths given limited information from 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 ACA utilities computed using HB are generally superior to those generated by the ACA system under OLS. ACA/HB utilities generally produce better hit rates and more accurate share predictions of holdout validation data. Furthermore, ACA/HB provides a more theoretically sound way to combine information from ACA's priors and pairs.
We at Sawtooth Software are not experts in Bayesian data analysis. In producing this software we have been helped by several sources. We have benefited particularly from the materials provided by Professor Greg Allenby in connection with his tutorials at the American Marketing Association's Advanced Research Techniques Forum.
Advanced Simulation Module (ASM) Technical Paper
The Advanced Simulation Module (ASM) extends the capabilities of the standard market simulator software for ACA, CBC, and CVA 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.
CBC Technical Paper
"Choice-Based Conjoint" Analysis (CBC) is a PC-based software 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.
CBC Advanced Design Module Technical Paper
Some CBC projects do not fit the traditional mold (full-profile, common attributes, limited attributes and levels). The Advanced Design Module for CBC gives the researcher additional capabilities:
- Alternative-specific plans
- Partial-profile interviewing format
- Capacity extended to 30 attributes
- 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.
CBC Latent Class Technical Paper
The CBC Latent Class Module is an add-on system to the CBC System for Choice-Based Conjoint. Latent Class analysis is a technique for dividing respondents into segments having similar preferences. Latent Class simultaneously estimates utilities for each segment and the probability that each respondent belongs to each segment.
Latent Class can improve the quality of marketing simulations over traditional aggregate logit modeling. It helps reduce IIA (red bus/blue bus) problems. This module also lets you fit linear terms to quantitative attributes.
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CBC/HB Technical Paper
Hierarchical Bayes is a relatively new 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. We at Sawtooth Software are not experts in Bayesian data analysis. In producing this software we have been helped by several sources. We have benefited particularly from the materials provided by Professor Greg Allenby in connection with his tutorials at the American Marketing Association's Advanced Research Techniques Forum.
CCEA Technical Paper
"Convergent Cluster & Ensemble Analysis" (CCEA) is software for doing cluster and cluster ensemble analysis. CCEA uses k-means as its standard cluster algorithm. However, the newer Ensemble Analysis included in the software is shown to produce better results for artificial datasets generated with known group membership. The procedure for the ensemble analysis is described in detail. Comparisons to k-means as provided by our previous CCA software are shown.
CPM Technical Paper
"Composite Product Mapping" (CPM) in many ways can be considered the successor to the APM system. It can generate the discriminant-based perceptual maps that APM does. In addition, two new "composite" mapping techniques are included. These new composite techniques create maps using both perceptual and preference data. The preference data can come from paired comparison judgments between brands (products or objects), or from conjoint part worths. Since composite product maps are more closely linked to preferences, a "density of demand" plot is possible, overlaying on the perceptual space colors ranging from light to dark representing various degrees of relative demand.
In contrast to the APM system, CPM does not include a module for collecting data. Data can come from paper-and-pencil or computerized surveys, so long as they are appropriately formatted in an ASCII file. CPM includes a Ci3 template for collecting data with the Ci3 System. If the Ci3 template is used, CPM can read the data directly from the Ci3 data file. The CPM System includes a Windows-based Plot module for creating presentation-quality maps.
CVA Technical Paper
"Conjoint Value Analysis" (CVA) is a PC-based software system for full-profile conjoint analysis. CVA fits within our SMRT suite of conjoint analysis tools. CVA interviews can be conducted using the software's built-in Windows interviewing program or data can be gathered using a paper-and-pencil approach or through a Web-based survey administered by our SSI Web System. CVA can be used to manage all aspects of card-sort (single-concept) or pairwise comparison conjoint studies. This paper provides a description and technical details of CVA.
CVA/HB Technical Paper
Hierarchical Bayes is a relatively new technique for computing individual- level part worths from conjoint data. HB has been described favorably in several recent journal articles. Its strongest point of differentiation is its ability to provide estimates of individual part worths given limited information from each individual. It does this by "borrowing" information from other individuals.
This technical paper describes the functionality of the software and math behind HB. We at Sawtooth Software are not experts in Bayesian data analysis. In producing this software we have been helped by several sources. We have benefited particularly from the materials provided by Professor Greg Allenby in connection with his tutorials at the American Marketing Association's Advanced Research Techniques Forum.
HB-Reg Technical Paper
Hierarchical Bayes is a relatively new technique for computing individual- level estimates of regression coefficients or part worths. HB has been described favorably in several recent 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 ASCII 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. We at Sawtooth Software are not experts in Bayesian data analysis. In producing this software we have been helped by several sources. We have benefited particularly from the materials provided by Professor Greg Allenby in connection with his tutorials at the American Marketing Association's Advanced Research Techniques Forum.
MaxDiff/Web Technical Paper
This paper describes the technical procedures used in the MaxDiff/Web 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/Web 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 exported to Sawtooth Software’s Latent Class system for segmentation analysis.