Conjoint Analyse

Type of record:
  • micromethod
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Ideal innovation phases for this method:
  1. Innovation Phase
  2. 1
  3. 2
  4. 3
  5. 4
  6. 5
  7. 6
  8. 7
  9. 8
  10. 9
  11. 10

Description

The aim of the conjoint analysis is to assess the acceptance of a product and its functions by the customer.

To this end, customers are presented with various variants of a product, which are ranked by these according to weighted customer requirements with regard to the individual product features.

Subsequently, customer-oriented benefit values for the individual characteristic values are obtained, for example, by mathematical-statistical iteration and simulation methods.

As a rule, it is assumed that the total benefit is composed additively of the benefit of the individual characteristic values. In order to perform conjoint analysis, you need different product variants, weighted customer requirements and a sufficiently large group of test persons.

Example: For a bicycle manufacturer, it would be important to determine the importance of the characteristics "manufacturer", "gearshift" and "colour of the bicycle" for the user's purchasing decision. A conjoint analysis would combine total products consisting of different features (for example a blue Giant bike with a 21-speed gearbox and a red Hercules bike with a seven-speed gearbox and so on).

The interviewee now votes on each of these overall concepts. Within the framework of the conjoint procedure, it is possible to deduce from the user's data his preferences with regard to the individual characteristics and characteristic values. In our example, it could be shown that the manufacturer is mainly responsible for the benefit of the bicycle perceived by the customer. With the help of current information technology, it is now possible to present such alternatives as virtual two- and three-dimensional models.

The advantage of this method over other methods (market surveys, customer surveys and so on) is that the relevance of different product characteristics can be revealed by the customer during the purchasing process.
  • Effective for radical innovations
  • Effective for disruptive innovations
  • Effective for highly complex challenges
  • Effective for medium complexity challenges
  • Effective for design innovations
  • Effective for new business models
  • Effective for product innovations
  • Effective for service innovations
  • P1 Understanding (identifying innovation search fields - problem solving)
  • P2 Analysis (of problems - the environment - people - products)
  • P8 Early Prototyping (testing directly on the user)
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