Conjoint analysis, also referred to as conjoint measurement, is a multivariate method used to determine customer benefit and willingness to pay.
In order to determine the utility of product characteristics for the customer and their willingness to pay, customers must rank various products with different characteristics. These different characteristics may, for example, be technical specifications, trademarks or additional services. The individual willingness to pay can be determined by considering the different customer preferences for the separate components. The advantage is that this selection procedure comes very close to a real purchase decision. The conjoint analysis offers a good basis for setting utility-based prices.
Conjoint analyses are suitable for dealing with numerous questions in marketing. The specific fields of application of conjoint analysis in marketing are diverse and range from product development to pricing and market segmentation. The following questions, for example, can be explored with a conjoint analysis:
Field of use: Product development
Field of use: Pricing
Field of use: Market segmentation
For a conjoint analysis to be successful, the target image must first be defined as concretely as possible. Will the analysis be used for (new) product development or for pricing? Is the aim to increase product profit or market coverage?
After defining the target picture, the target group for the survey must be defined. It is important to ensure that the selected group of people / customers is representative and suitable for answering the initial question or for achieving the target picture.
The next step is to define the exact type of conjoint analysis. Depending on the objective, one of the following three methods is usually selected:
Based on this, the relevant features and the respective characteristics must be defined. When making the selection, it is important to ensure that the characteristics and values are sufficiently meaningful and clearly distinguishable from one another. In addition, the number of features and characteristics should be based on the objective and the target group. As a guideline, a maximum of 5 – 7 characteristics with 2 – 4 values each should be defined. Each additional characteristic or expression increases the requirements for the size of the sample and the duration of the survey.
Subsequently, the selected characteristics and their proficiencies are combined into fictitious product variants. During the creation, non-logical variants should be excluded to ensure the feasibility of the product.
Depending on the target image, competitor products can also be included for selection in addition to fictitious product variants. Such a procedure extends the selection of product variants, but enables a more realistic decision scenario.
As part of an online survey, participants are asked to evaluate the different product variants along their own preferences. Here, attention must be paid to the exact question. Depending on the objective, the question must be carefully considered. For example, the questions “Which product variant do you prefer?” or “Which product variant would you be most likely to buy?” can lead to different results and may not produce the hoped-for results.
Finally, the analysis calculates the relative importance of the characteristics and assigns a utility value to each characteristic value. This reveals the partial benefit of the respective characteristic within the characteristic category. In the analysis, the utility values of the individual values of the characteristics are evaluated and assessed separately. The allocation of the individual utility values is based on multivariate statistics (likelihood method and hierarchical Bayes estimation algorithm). In addition, price-sales functions can be created, which are used to derive theoretical willingness-to-pay values.
The mathematical results of the conjoint analysis can now be used to answer the questions posed at the beginning and to derive appropriate measures for the portfolio, price or sales strategy. Depending on the objectives, optimal product variants can be defined, price adjustments determined or target segments identified.
Classical conjoint analysis: trade-off method
Classical conjoint analysis: full-profile method
Choice-based conjoint analysis
Adaptive conjoint analysis
Maximum-difference conjoint analysis
Limit conjoint analysis
If you would like to learn more about conjoint analysis or have identified a specific application need for your company, please feel free to contact our experts!