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.
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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