How Unilever is driving growth opportunities with NRM
Read how Unilever is focusing on revenue growth with a data-driven net revenue management (NRM) strategy and get tips from its NRM expert.
Conjoint analysis is a statistical technique, originated in mathematical psychology, that is used to determine how people value different features that make up an individual product or service.
This popular research technique was initially developed by psychologists in the early 70s, interested in understanding how people make decisions. By directly asking how and why – assuming a conscious decision making process – people might respond in line with what is top of mind or what they believe the interviewer wants to hear (politically / socially correct answers). The answers don’t necessarily reflect what one would actually do, choose or buy. Choices involve trade-offs and compromises.
The key characteristic of conjoint analysis is that a product is composed of multiple conjoined elements (attributes or features). Based on how the combined elements (product concepts) are evaluated, the underlying preference structure can be determined.
Over time, various forms of conjoint analysis have been developed: from Conjoint Value Analysis and Adaptive Conjoint Analysis to Choice Based Conjoint and Adaptive Choice Based Conjoint to Menu Based Conjoint and Preference Based Conjoint.
Below is an example of how a Conjoint Analysis exercise looks like. You can also try it online here.
Watch our webinars which cover our innovation in conjoint analysis:
There are a few advantages that you can benefit from when doing a conjoint analysis:
However, there are also some disadvantages that you need to take into account before conducting the analysis:
Conjoint analysis is usually done via a respondents survey. One needs to define the attributes and levels to test having the end goal in mind: for instance does one want to optimize product management or product development or does one want to test an online product or service by replicating the purchase decision? Or is one mainly interested in the brand price trade-off? The right set up is necessary to make sure that the final market simulators can be used to test different scenarios and deliver the answers to the business questions.
Many factors play a role when determining how to set up ones conjoint analysis survey. See below for an instruction video
Within SKIM we use various software packages. First of all, for scripting the surveys and setting up the conjoints we use Lighthouse, the latest update of Sawtooth Software. The extended packages have possibilities to run Hierarchical Bayesian analysis and segmentation via Latent Class or Combined Cluster Ensemble Analysis.
The first package has quite some interesting settings for the more experienced researcher, e.g. to estimate utilities part worth or linear. The output of the regular questionnaire data is in SPSS format.
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