Conjoint Types

What Is Full Profile Conjoint Analysis?

Full-profile conjoint analysis displays a large number of full product descriptions to the respondent. The evaluation of these packages yields large amounts of information for each customer/respondent.

In the rating task, the respondent gives their preference or likelihood of purchase. While many features and levels may be studied, this type of conjoint analysis involves many evaluations and can weary respondents. Full profile conjoint is best used for a moderate number of profiles. The advanced functionality of Qualtrics employs experimental designs to reduce the number of evaluation requests within the survey.

The output and analysis accumulated from full-profile conjoint surveys is similar to that of other conjoint models.

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If you are interested in learning more about how Qualtrics can help you with a choice-based conjoint project, contact us at research@qualtrics.com.

What Is Choice-Based Conjoint Analysis?

The Choice-based conjoint analysis (CBC) (also known as discrete-choice conjoint analysis) is the most common form of conjoint analysis. The respondent is asked to indicate the option or package they are most likely to purchase. Choice-based conjoint presents choice sets that are similar to those faced in the marketplace.

The importance and preference for attribute features and levels can be mathematically deduced from the trade-offs made when selecting one (or none) of the available choices.

The length and detail of choice-based conjoint surveys are contingent on the number of features and levels. Often, that number is large and an experimental design is implemented to avoid respondent fatigue. Qualtrics provides extreme flexibility in utilizing experimental designs within the conjoint survey.

Output

The output of a Choice-based conjoint analysis provides excellent estimates of the importance of the features, especially in regards to pricing. CBC will estimate the value of each level and the combinations that make-up optimal products. Simulators report the preference and value of a selected package and the expected choice share (surrogate for market share).

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To learn more about how Qualtrics can help with your conjoint project, contact us at research@qualtrics.com.

What Is Adaptive Choice Conjoint Analysis?

In adaptive conjoint analysis, the choice sets presented to respondents will vary based on the preferences they express. This adaption focuses on the respondent’s most preferred feature and levels. As each package is presented for evaluation, the survey accounts for the choices made and then makes the next question more efficient.

This process makes the conjoint exercise more efficient, wasting no questions on levels with little or no appeal. Every package shown is more relevant to the respondent and will yield ‘smarter’ data.

Adaptive conjoint reduces the survey length without diminishing the power of the conjoint analysis metrics or simulations.

Implementation

There are multiple ways to adapt the conjoint scenarios to the respondent. Most commonly the design is based on the most important feature levels.

A combination of full profile and feature evaluation methods can be utilized and is referred to as Hybrid Conjoint Analysis.

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To learn more about how Qualtrics can help with your conjoint project, contact us at research@qualtrics.com.

What Is Self-Explicated Conjoint Analysis?

Self-explicated conjoint analysis is a hybrid approach that focuses the evaluation on the various attributes of a product. The respondent rates their preference for each feature level rather than the preference for a bundle of features. Although the attribute level approach differs from full profile techniques, the outcome is still the high quality estimates of preference utilities. Let us show you how…

The Process

• For each feature, the respondent selects the level they prefer and least prefer.
• Next, the remaining levels of each feature are rated in relationship to the most preferred and least preferred levels.
• Then respondents use point allocation to indicate the relative importance of the overall features.

The Advantage of Self Explicated Conjoint Analysis

The strength of self-explicated conjoint analysis is that it does not require long experimental designs or the presentation of a list of product profiles. The respondent can quickly indicate what attributes they like and which they do not.

There are some limitations to self-explicated conjoint analysis, including an inability to tradeoff price with other attribute bundles. Because price would be evaluated as a single attribute, the respondent always prefers the lowest price. If you are not considering pricing analysis, then self-explicated conjoint is a great option.

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To learn more about how Qualtrics can help with your conjoint project, contact us at research@qualtrics.com.

What Is Max-Diff Conjoint Analysis?

Max-Diff conjoint analysis presents product configurations as an assortment of packages / features. These packages are evaluated and selected under best-most preferred and worst-least preferred scenarios.

Respondents can quickly indicate the best and worst items in a list, but often struggle to decipher their feelings for the ‘middle ground’.

Max-Diff provides an easier task because consumers are well programmed in the art of making comparative judgments.

Max-Diff conjoint analysis is an ideal methodology when the decision task is to evaluate product choice. An experimental design is employed to balance and properly represent the sets of items.

There are several approaches that can be taken with analyzing Max-Diff studies including: Hierarchical Bayes modeling to derive utility score estimations, best/worst counting analysis and TURF analysis.

We Can Help

To learn more about how Qualtrics can help with your conjoint project, contact us at research@qualtrics.com.