Conjoint Analysis is for discovering the relative importance to stakeholders – e.g. consumers or citizens – of the attributes underpinning a product or other alternative of interest.

Conjoint analysis – also known as Choice Modelling or Discrete Choice Experiments (DCE) – is widely used in the social sciences, marketing research and for designing new products.

For example, Conjoint Analysis can be used to discover car-buyers’ preferences with respect to ‘fuel efficiency’, ‘top speed’, ‘safety features’, ‘price’, etc.

Another example is discovering the relative importance of the various aspects of hospital services that people care about: such as ‘quality of medical care’, ‘waiting times’, ‘amenities’, ‘crowdedness’, ‘price’, etc.


Conjoint Analysis has its own specialised terminology (i.e. different from Multi-Criteria Decision-Making):

  • Attributes: Features or characteristics of the product or other alternative of interest, with two or more levels of performance / achievement.
  • Part-worth utilities: Values (or weights) representing the relative importance of the attributes.
  • Concepts (or Profiles): Combinations of attributes representing particular products or other alternatives of interest.

Choice-based conjoint analysis

1000minds implements the PAPRIKA method to determine decision-makers’ part-worth utilities –representing the relative importance of the attributes – by asking questions based on choosing between pairs of concepts involving trade-offs between the attributes. Hence this type of Conjoint Analysis is referred to as being ‘choice-based’.

1000minds Conjoint Analysis is for:

  • Discovering decision-makers’ part-worth utilities, representing the attributes’ relative importance.
  • Ranking or prioritizing concepts, including choosing the ‘best’ (top-ranked) concept.
  • Ranking or prioritizing all hypothetically possible concepts that might ever be considered.

Part-worth utilities can be easily exported to Excel – e.g. where market simulations can be performed.

Scenarios involving competing product concepts can be evaluated, enabling market shares for each concept to be predicted.

A wide range of outputs is available from 1000minds directly or with a little further analysis.

More information

To learn more about Conjoint Analysis, you might like to read the Wikipedia article.

Seminal articles about Conjoint Analysis in the Marketing literature include:

  • PE Green, AB Krieger & Y Wind (2001), “Thirty years of conjoint analysis: reflections and prospects”. Interfaces 31, S56-S73.
  • PE Green & V Srinivasan (1990), “Conjoint analysis in marketing: new developments with implications for research and practice”. Journal of Marketing 54, 3-19.
  • PE Green & V Srinivasan (1978), “Conjoint analysis in consumer research: issues and outlooks”. Journal of Consumer Research 5, 103-23.

How 1000minds works

Learn more from our simple guide.

Conjoint analysis

See also

PAPRIKA method

What is MCDM?

MeenyMo – ‘everyday’ decision-making software