The author discusses three ways to treat overall price in conjoint analysis experiments: traditional approach, conditional price, and summed price. The traditional pricing method treats price as a separate attribute with a fixed set of price points that apply to all products. The problem with treating price in this traditional manner is that products with the best features are sometimes shown at the lowest prices (and products with the worst features are sometimes shown at the highest prices). This can lead to dominated choices and lack of realism. With conditional pricing, incremental amounts are added to the price for premium brands or features, so enhanced products are generally shown at higher prices. One uses a look-up table to determine actual prices shown in the questionnaire. Summed pricing generalizes the idea of conditional prices to n-way attribute contributions to overall price. Also, it estimates the effect of overall price as a linear (or piecewise) coefficient, rather than as a part-worth utility function. After summing the prices across the feature components, price is varied by an additional random shock % specified by the researcher. One of the challenges of summed pricing is that the price variable is moderately to strongly correlated with other attributes, depending on the design. The author conducts a simulation study investigating the stability of the price coefficient within summed pricing, given different amounts of random shock. Summed pricing is a capability of the Adaptive CBC (ACBC) software system. It may also be implemented using power tricks and custom data processing for CBC.

Decades ago, a concern was raised regarding what has been called the “Number of Levels Effect” in conjoint analysis. Researchers in the 80s and 90s showed that by doubling the number of levels of a quantitative attribute like price (from 2 to 4), the importance of price could significantly increase (even though the range of variation for the attribute was held constant). The conjoint analyses employed in those early studies were traditional ratings-based conjoint analysis. In this updated view on the subject, Orme & Hewett conduct two CBC (Choice-Based Conjoint) studies and find that the number of levels effect is not as strong as previously thought. They further test whether the number of levels effect continues for more than four levels of a quantitative attribute, finding no increase in attribute importance when moving from 11 to 21 levels of a quantitative attribute.

Common approaches for survey-based pricing research include conjoint analysis, Van Westendorp PSM, and Gabor-Granger. The authors (Orme & Chrzan) describe the three methods, including strengths and weaknesses. They show how the Newton-Miller-Smith purchase intent question extension for the PSM approach can improve results; though conjoint analysis is argued to be superior for most survey-based pricing research applications.

Chris reviewed four novel applications of Conjoint Analysis for product design from various presentations he’s delivered over the past 12 years at Sawtooth Software Conferences. He highlighted how each method was useful, describing its key points for success. The four applications were as follows: 1) comparing CBC and ACBC for real respondent preferences, where ACBC was found to slightly improve predictions of real choices, 2) using Game Theory with conjoint analysis, to decide whether to add a product enhancement or not while considering the possible reactions of a key competitor, 3) using genetic algorithms with market simulators to figure out which products to include in a streamlined product line, and 4) using “Profile CBC” for psychographic segmentation, where respondents pick which conjoint profile most resembles them in terms of personality characteristics. When comparing CBC and ACBC results, Chris found that they were very similar, but that ACBC seemed to have a bit better precision. He recommended ACBC for smaller sample sizes and when you can take some extra time with respondents in the interview. As for “Profile CBC,” Chris recommended its use for developing personas (characteristic profiles of groups of buyers) via latent class that validate with external measures and directly follow from the CBC data.

This article describes how to simulate Willingness to Pay (WTP) in a more realistic and focused way than using either the common algebraic approach or the two-product simulation approach. The common approaches don’t consider competition and tend to overstate WTP. We recommend simulating product enhancements against a competitive set of alternatives (including the possibility of a None alternative) together with bootstrap sampling for estimation of confidence intervals around WTP. We introduce a generalizable and powerful extension called Sampling Of Scenarios (SOS) for estimating WTP that can be tailored to make certain detailed assumptions regarding the firm’s product as well as competitive reactions in the marketplace.

Researchers regularly clean inconsistent respondents from CBC and MaxDiff studies and the fit statistic from HB (RLH) provides a way to try to identify them. Chrzan and Halversen compare four approaches to identifying random-acting respondents using Latent Class MNL and HB MNL. They find that HB’s RLH more reliability discriminates between random-acting respondents and real respondents than the latent class approaches. They identify RLH cutoffs for classifying random-acting respondents by generating a pool of random responders and estimating their preferences via HB. Chrzan and Halversen also report false positive and false negative classification rates for the recommended cutoff values.

Over the last few years, the incidence of bad respondents is increasing. Conjoint analysis and MaxDiff have a fit statistic called RLH when using HB estimation that helps identify bad respondents. As long as the conjoint or MaxDiff questionnaire has enough questions relative to the number of levels or items in the study, random responders can be identified with a high degree of accuracy. This paper gives instructions for generating random data to identify the RLH cutoff that has a high probability of identifying random respondents.

This article conveys the basics of conjoint utility estimation using a common software tool: Microsoft's Excel. It covers dummy-coding and experimental design issues for full-profile conjoint analysis (single concept). When using Excel to perform the steps described in this article, you'll need Excel's Analysis Toolpak add-in with Regression Analysis.

Conjoint analysis has been a great success story for the marketing research industry. This paper outlines its development from the late 1960s through today. The earliest conjoint analysis approaches were based on either full-profile card sort, or Johnson's tradeoff matrix. Later, Adaptive Conjoint Analysis and discrete choice (CBC) applications dominated. The use of CBC accelerated in the 1990s due largely to the introduction of CBC software in 1993 and the development of HB methods in the mid to late 1990s. The author states: "Much of the recent research and development in conjoint analysis has focused on doing more with less: stretching the research dollar using IT-based initiatives, reducing the number of questions required of any one respondent with more efficient design plans and HB (“data borrowing”) estimation, and reducing the complexity of conjoint questions using partial-profile designs." Since 2000, there has been increased interest in the use of optimization routines, greater realism (including "virtual shopping" environments) and real-time adaptive CBC routines.

Covers the essentials for interpreting conjoint analysis data, including part worths, importances, shares of preference and "counting" analysis. The framework for interpreting results is developed from formal definitions of scaled data: Nominal, Ordinal, Interval, and Ratio. Common errors in interpreting conjoint analysis are highlighted.

This paper (a chapter from the book Getting Started with Conjoint Analysis) illustrates how conjoint can be used to provide managers strategic marketing information that is intuitive and actionable. It explains how Choice-Based Conjoint (CBC) can be used to measure brand equity and determine brand sensitivity. The article focuses on how to get managers to "buy in" to conjoint, and some pitfalls to avoid when presenting conjoint data.

Conjoint analysis has become the most popular and useful way to measure respondents’ preferences for simple to complex offerings and predict market choices. This article is taken from Chapter 1 of Getting Started with Conjoint Analysis, a book written by Sawtooth Software president Bryan Orme. It provides a non-technical managerial overview of the technique. It describes the history of the method, the various flavors, its practical uses, and recent developments that have made conjoint analysis even more powerful.

Sample size considerations for conjoint analysis are often quite different from those for traditional market research surveys. This paper covers such topics as sampling error versus measurement error, confidence intervals, sampling for small populations, and how the choice of market simulation method affects the precision of results. The differences between ACA, traditional conjoint (CVA), and CBC are discussed with respect to sample size decisions. Finally, the paper reviews sample sizes commonly used by conjoint practitioners, and provides some rules-of-thumb and general recommendations.

It is advisable to include holdout choice tasks in conjoint interviews even though they may not appear to be needed for the main purpose of the study. This paper, originally published in Sawtooth Solutions, Spring 1997, and updated in 2010, 2014, and 2015, lists the benefits of holdout choices and provides general guidance on how to construct them.

Analysts sometimes add holdout questions to their conjoint surveys to test the way they have specified their models. Holdouts can also be used for out-of-sample validation. The author, Keith Chrzan, uses synthetic CBC respondents to test how many holdouts are needed to identify a true vs. a misspecified model reliably. The misspecified models were manipulated to involve known small, medium-sized, and large errors. Under medium-sized and large errors, just a handful (about 5) holdout tasks seems to provide enough data to reliably indicate that the true model is better than the misspecified one. Under relatively small misspecification error, no number of holdouts tested (up to 15) can reliably point to the correct model. Chrzan’s findings demonstrate that just 1 or 2 holdouts is probably not enough to give practitioners enough evidence to compare competing models, but 5 or more will often bring enough evidence to reliably point to which of two competing models provides the best fit.

Sawtooth Software offers different conjoint analysis packages, including choice-based (discrete choice) methologies (CBC, ACBC), menu-based choice (MBC), as well as the older ratings based approaches (ACA, CVA). This paper discusses the main differences between these approaches and offers suggestions regarding applicability to different research situations.

No, we’re not referring to OJ and gloves that won’t fit. We’re talking conjoint analysis,Sawtooth Software, and our book “Getting Started with Conjoint” all factoring into thecourtroom arguments in Apple’s $2.5 billion suit against Samsung that many are calling the Patent Trial of the Century.

This article provides an excellent case study and tutorial regarding how to bring sophisticated methods like conjoint analysis to an organization.

The author (Goodwin) discusses how conjoint has been adopted at Lifetime Products, Inc., including success stories and suggestions for obtaining buy-in from management. He outlines his history of progression in conjoint methods, from card-sort conjoint, to CBC, to part-profile CBC, and finally to adaptive CBC (ACBC).

Joel Huber of Duke University provides an insightful discussion on the history and theoretical underpinnings of conjoint analysis. He traces its development from its roots in psychometrics to its enthusiastic adoption by the market research community. The original paper is quite dated (originally published in our 1987 Sawtooth Software Conference Proceedings), but Joel Huber and Bryan Orme have added additional footnoted commentary from a 2004 perspective. This paper continues to be an excellent resource for today's conjoint practitioners.