Adaptive Conjoint Analysis

Overview

Adaptive Conjoint Analysis (ACA) is a legacy conjoint analysis approach originally developed in the 1980s that is not often used today (~2% of total conjoint analysis projects as reported by Sawtooth Software users), but has some advantages for certain research situations.   

ACA customizes the experience for each respondent. It was designed for situations in which the number of attributes exceeded what could reasonably done with more traditional methods conjoint methods available at the time such as CVA. ACA focuses on the attributes that are most relevant to the respondent and avoids information overload by focusing on just a few attributes at a time.  

ACA is a weak methodology for studies involving price, where CBC and ACBC are considered best of class tools.  Before choosing the ACA system for your project, we recommend you discuss your research needs with a Sawtooth Software content expert to determine that it would be appropriate. 

ACA System

• Two system sizes to choose from: up to 10 attributes / up to 30 attributes
• Up to 15 levels per attribute (text and/or graphics)

Methodology

In a study covering many attributes, respondents are sometimes provided with too much information to consider thoroughly. The scope of many studies has also been constrained by limitations in respondents' time and attention. ACA moves beyond those limitations by adapting the interview for each respondent. Early in the interview the computer learns enough about each respondent's values to focus on those areas of importance to that respondent. This results in broader scope, since more attributes can be tested.