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.

In this article, Orme outlines the pros and cons of different pricing methods in Choice-Based Conjoint (CBC) studies. He focuses on Summed Pricing, where total price is determined by fixed component prices and a random shock. Summed Pricing leads to showing more realistic products at reasonable market-like prices than when using a standard price attribute in CBC. The article provides a workaround to implement Summed Pricing in Lighthouse Studio’s CBC so that the analysis feels just like when using a standard single price attribute. Orme emphasizes checking the Test Design report for reasonable estimates and discusses potential issues and solutions related to Summed Pricing in CBC studies.

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 following article features Chapter 12 of Bryan Orme and Keith Chrzan's 2017 book, Becoming an Expert in Conjoint Analysis: Choice Modeling for Pros.

CBC (Choice-Based Conjoint) choice simulators predict share of choice for product concepts within competitive market scenarios, but they provide no insights into perceptions—the why’s behind the choice. The author (Orme) introduces Perceptual Choice Experiments as an extension of CBC questionnaires for integrating diagnostic perceptual dimensions into CBC analysis and simulators. Experimental design, model estimation, and sample size issues are explicated. Fortunately, perceptual choice experiments leverage the same tools we already employ in CBC studies: orthogonal experiments, MNL estimation, and market simulation via the logit rule (share of preference). The author demonstrates how perceptual choice data may be analyzed using Sawtooth Software’s MBC system—though any system that can estimate MNL models is suitable for the task.

In modeling the prescribing decisions of physicians, pharmaceutical marketing researchers face challenges which combine to limit sample sizes while requiring complex models. This paper reviews some of the challenges facing pharmaceutical choice modelers and then surveys the variety of choice models available to them. It concludes with recommendations for improving models of physicians’ prescribing decisions, for making models that cause less pain to respondents and analysts.

A new choice paradigm available to academic and applied choice modelers is Random Regret Minimization (RRM). RRM works from the assumption that what drives choice is the avoidance of regret: a chooser selects the alternative that minimizes her chance for regretting her decision. The authors (Keith Chrzan and Jefferson Forkner) introduce RRM and discuss some of its interesting properties. They describe how to analyze RRM models in standard logit and HB software packages like Sawtooth Software's Latent Class and CBC/HB. The results of two marketing research applications conducted to compare RRM and standard CBC in terms of predictive validity find that the two perform about equally well. The authors also discuss the relative strengths and weaknesses of RRM.

Latent Class for Multinomial Logit (MNL) is a popular procedure for finding segments of respondents with different preferences from choice data such as CBC and MaxDiff.  However, one aspect of standard Latent Class analysis that may interfere with some analysts’ goals is that it sometimes can form segments that mainly differ in terms of scale (response error) but don’t differ much in terms of real preference patterns.

Bryan Orme proposes a simple method for constraining latent class solutions, assuming that the standard deviation across a utility vector represents a proxy for scale.  He demonstrates using an artificial data set that scale constrained latent class avoids distinguishing between high-error and low-error respondents who otherwise have identical preferences.

The scale constraints described within this white paper are available as an option within Sawtooth Software’s latent class modules starting with the SSI Web v8.3 release.

Sawtooth Software's founder, Rich Johnson, describes the events that led to the development of the CBC software product. This document provides insights into key developments that have made CBC the most commonly used conjoint software product for conducting conjoint-related studies. An enjoyable read to develop an appreciation for the key people and main forces behind the creation of a classic.

In this article, the author (Orme) uses random split-sample experiments to test different ways of asking CBC and Adaptive CBC (ACBC) questionnaires. Specifically, he examines:

• Use of minimal overlap vs. modest overlap for CBC questionnaires (modest overlap seems to improve results)
• ACBC for small designs--just 4 attributes (ACBC is shown to work as well as CBC)
• Placing “Unacceptable” screening questions prior to “Must-Have” screening questions in ACBC (seems to work better)
• Individual-level (customized) utility constraints in ACBC (no benefit shown for this 4-attribute data set, though benefits should be greater for larger, more demanding designs)
• Whether giving respondents a “consistency game” will improve their data and their experience (minimal gains in fit observed, but with the risk of annoying about 1/5 of the respondents)