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.
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.
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