Conjoint Analysis: Price & Product Optimization Using Holistic Conjoint Analysis
It is an open secret in the industry. Conjoint Analysis Software often delivers strange results forcing analysts to tweak datasets or analytic parameters so that results will have some face validly. Only this is not a procedure that produces correct results. The phenomenon is a clear indicator that something is still wrong with today’s conjoint analysis approaches.
The challenge with Conjoint Analysis
Many advances to Conjoint Analysis approaches have been developed. There is ACA (Advanced Conjoint Analysis enabling for partial feature profiles), CBC (Choice Based Conjoint switching from preference to choice) or CSC (Constant Sum Conjoint enabling for a distributed choice).
The basic principle is to conduct several choice experiments by manipulating causes (i.e. features). Due to practical reasons, it is not possible to assess all possible feature combinations at one respondent. This is where the issue evolves. That is why the choice of feature combinations per person is randomized. This would work if samples were large enough, but they rarely are in real life studies!
An example clarifies
Imagine there is a study where actual consumers of a food brand are more likely to choose the brand – simply because they have already tried it. But this feature “have already tried” is not part of the conjoint experiment. If features are randomized, there will be features or feature combinations with significantly higher ratios of persons who have already tried the product.
Since they are much more likely to buy, conjoint analysis will “think” those features or feature combinations must be important. The problem is not solvable by quotation nor by other measures in advance because normaly we don´t know every “disturbing factor” in advance.
Why a solution of the Conjoint Analysis dilemma would be so valuable
If we could consider all the biasing factors of a conjoint, market researchers would enjoy several advantages:
- First and foremost, it would very much increase the trust and confidence in the validity of results. You can be much more confident that derived recommendations will work.
- An analysis with all relevant factors will enable a holistic view of the decision process. It will not just focus on the choice decision but will also quantify the impact of product features and prices towards brand reputation, brand awareness, consideration, loyalty or recommendation.
- Finally, such a study would be a single source for multiple information needs. It may serve for brand positioning, segmentation and customer journey questions.
The powerful solution: Holistic Conjoint Analysis
For a holistic conjoint analysis approach we need to collect several pieces of information:
- Several choice tasks per respondent with randomized feature profiles.
- Attitudes and image of brand.
- Data about the outcomes of interest, such as purchase intention, willingness to inform, brand consideration
- Data about the respondents such as demographics, their situation, and their history. Are they already customers? Are they bargain seekers? Are they familiar with the costs? All those items that might have an impact on their desired outcomes.
The ultimate question is ‘how do we identify which changes would improve the outcome?’ It is a challenge to conventional methods as they are built to fit parameters of a known model.
What is needed is a method that builds a prior unknown model in light of data, a model that incorporates indirect causal effects and does not neglect nonlinearity or interaction of those unknown relationships. This is exactly the strength of the Universal Structure Modeling approach that is performed by the NEUSRELTM Software.
The good news: Holistic Conjoint Analysis is proven, reliable, tried and tested
We compared dozens of studies on the validity of Holistic vs. Conventional Conjoint and found a validity lift of 25 to 85% in explanation power. Results show typically higher face validity and give seamless recommendation where pricing and product configuration naturally fit to positioning, segmentation and marketing strategy advice, as it is derived from one single data source and one single causal model.