The Conjoint Framework

In conjoint analysis:

  • researchers describe products or services by a number of variables levels and
  • then collect and measure respondents’ preference, potential choice, or purchase interest. The adopted approach defines the model.

  • product features are not shown in isolation as in conventional questioning but respondents are exposed to partial or complete products;
  • respondents assess not only one but several products profiles;
  • the type of assessment defines the conjoint model (choice-based model, allocation model, rank conjoint model, etc.);
  • analysis of the respondents’ choice patterns brakes down overall evaluations for products into preferences for their individual elements and thus allow researchers estimating how much value a respondent puts on each elements and, consequently, allows modeling the decision making process;
  • the choice behavior elements are usually incorporated into a simulator that mimics respondents preferences.

 

Alternative Conjoint Models

There are many conjoint models available on the market. The following are the most popular.

Rating & Rank-Based Conjoint Respondents rate or rank all scenarios based on preference or likelihood to purchase
Paired Comparisons of Profiles Conjoint (PCP) Respondents choose the most preferred scenario between 2
Choice-Based Conjoint (CBC) Respondents choose the most preferred scenario, usually from a set of 3 or 4
Allocation-Based Conjoint (ABC) Along with the new product, all  key competitors are listed. Respondents allocate a number of points to each option
Hybrid Designs (ACA, ACBC, etc), Menu-Based, and Dual Conjoint Various exercises, typically a combination of other conjoint models. Appropriate when many attributes need to be explored

R-sw Conjoint is the only commercial software that allows analyzing Rank-Based and Allocation-Based Conjoint data. It also allows analysis of Choice-Based Conjoint data.

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