Find out what people care about when making choices involving trade-offs.

1000minds Conjoint Analysis helps you answer questions like:

  • Which attributes or characteristics of a product or other alternative of interest are most important to consumers or citizens?
  • What is their relative importance (weights)?
  • How are different products or other alternatives of interest ranked relative to each other, and which is best?

Our conjoint analysis surveys allow you to include as many participants as you like, potentially 1000s. 1000minds is useful in all sectors – see our case studies and publications.

In addition to our valued business and government clients, we’re honoured that 1000minds is used for research and teaching at more than 200 universities worldwide – confirming our scientific validity and user-friendliness (see our awards).

Conjoint analysis terminology

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.

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 or 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.

For example, conjoint analysis is used to discover car-buyers’ preferences with respect to ‘fuel efficiency’, ‘top speed’, ‘safety’, ‘price’, etc. Another example is discovering the relative importance of the various attributes of hospital services that people care about: such as ‘quality of care’ vs ‘waiting time’ vs ‘amenities’ vs ‘price’, etc.

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

Choice-based, adaptive conjoint analysis

1000minds implements the PAPRIKA pairwise comparisons method to determine people’ part-worth utilities (weights) by asking questions based on choosing between pairs of concepts involving trade-offs between the attributes. An example of a question – involving designing a car – appears below. Hence this type of conjoint analysis is referred to as ‘choice-based’.

In addition, 1000minds is a type of ‘adaptive’ conjoint analysis because each time a choice is made, 1000minds adapts by formulating a new question to ask based on all previous choices.

1000minds is fast and scaleable. No extra analysis is needed to derive standard conjoint analysis outputs (see analyzing outputs). Also, potentially 1000s of people can participate in conjoint analysis surveys.

How 1000minds works

Learn more from this simple guide.

1000minds Conjoint Analysis

Adaptable templates

Or build your own models from scratch.

1000minds demo models