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“Value Matrix” is a technique sometimes used for feature selection for software bundles and plans. We do not express a view on validity of this technique and do not recommend using it for your projects.
The technique centres on building a 2x2 matrix, where each dot is a feature. The vertical axis is willingness to pay (measured through Van Westendorp) and the horizontal axis is “value” (measured through MaxDiff). The details of how it is created appears to be inconsistent from one analyst to another.
That said, it is possible to create this matrix using Conjoint.ly tools. For this, you might need two sets of data.
First, to identify respondents who valued one attribute in MaxDiff the most, there are a couple of ways:
Through the simulator:
In the simulator, you can put all the items into one scenario and see shares of preference for each item.
Under Advanced settings of that scenario, you can click “Segregate”. This will create a variable that you can use in segmentation.
Under the Segmentation tab, you can add a new segment with a new condition “Segregation based on simulation” and pick the relevant scenario and item.
Press “Save and apply” to effect segmentation.
Through Excel export:
Under the individual preferences tab, you see everyone’s preferences for separate items.
Using Excel formulas, you can find which item was most preferred by each respondent (looking for the highest value).
Second, to see the “normal price point” in Van Westendorp, you can look at the standard outputs. The table on the right will have it listed:
However, it will not provide confidence intervals. Van Westendorp results are not usually viewed in the context of statistical significance testing. There is no theory that backs up the application of the normal price point in practice. It is, of course, possible to calculate a confidence interval through computational statistics (bootstrap / jackknife).
This question from our users was answered on 19 April 2021. If there is anything else you'd like to know, please do not hesitate to contact us.