conjoint analysis

Conjoint analysis is a sophisticated analytical technique used to determine the joint influence that feature and level combinations of a product or service have on the purchase decisions. For example, a company could identify the most profitable combinations of pricing, quality, and quantity for a certain product or service. A properly designed conjoint analysis study allows any combination of features and levels to be profiled in order to estimate market or choice share. The goal is to reveal the underlying value that respondents would consciously or subconsciously place on profiles that represent full product configurations.

Qualtrics powers conjoint research in two ways:

• Conjoint Software: The Qualtrics survey software is equipped with a conjoint creation wizard. Using the self-explicated conjoint model, the wizard can set up a conjoint survey easily and quickly.
• Conjoint Project Contracting: Led by Ph.D. researchers, the Qualtrics specialists have the expertise to ensure relevant, intelligible, and actionable conjoint results. The Qualtrics specialists can be contracted to consult for or perform conjoint studies.

• Full Profile Conjoint Analysis: Ratings or rankings of distinct product profiles are used to estimate pricing effects. A set of utility functions are processed for each respondent measured, for segments within the sample, and for the total sample. Utility functions show the demand curve or relative importance of each attribute and each level of each attribute. Simulations are used to analyze the sensitivity of each of the attributes to changes in the market place. Simulations of the actual market place estimate the market share that would be derived from changing the feature level combinations that make up the product.
• Self-Explicated Conjoint Analysis: First, all attribute levels are presented to respondents for evaluation to eliminate any levels that would not be acceptable in a product under any conditions. Next, attribute levels are presented and each level is evaluated for desirability. Finally, based on these evaluations, the most desirable levels of all attributes are evaluated for relative importance. These scores can be summed and simulations run to obtain a score for any profile of interest.
• Discrete-Choice Conjoint Analysis: The respondent is shown different profile sets, and chooses the set he/she most prefers. The respondents' choices reflect the value they assign to each attribute. These choices are analyzed to derive differences in the attribute values from the competing alternatives and/or differences in the characteristics. You can produce estimates of the demand curves for all attributes, brands, and feature level interactions, such as the brand-price interaction.