Adaptive Conjoint Analysis (ACA)

Adaptive Conjoint Analysis, launched in 1985, can be considered the driver of the market acceptance of conjoint analysis. It is a pairwise, ratings-based conjoint approach that adapts the concepts shown to the research participants based on previous answers. It tries to maximize the information gathered from each choice by formulating the concepts such that they are equal in preference. However, not all attributes/features are shown at the same time. 

ACA: benefits & limitations

• It allows for the use of more attributes thanks to the use of partial profiles
• The data collection is more convenient due to its staged approach
• ACA computes respondent-level utilities on the fly, enabling collection of additional diagnostics during interviews

• It is less realistic for not all attributes/features are shown at the same time (research participants may not be able to assume attributes not shown are held "constant")
• Often not good at pricing research (tends to understate the importance of price)
• Must be computer-administered (PC or Web)
• Interactions cannot be calculated (it assumes equal price elasticities for all brands / respondent)

ACA: when to use it?

• To forecast the likely acceptance of a new to the world product
• To measure the attractiveness of specific product features
• To model high involvement purchases
• To (re)design a (new) product