For example –

Start with huge unwieldy data set with many features/properties associated with some combination of verbal, textural, measuremen, and ML/PR forms and cases, probably with a lot of missing data & lack of common experimental frame.

Create linkage measures for the feature/properties based on things like some measure of similarity or mutual information in different frames of reference with some kind of hacked importance sampling.

Alternate techniques like: Minimum Spanning Tree, Spectral Clustering, & MDS to create vector spaces of manageable size/complexity for the application

Now do multi-dimensional scaling of the point cases in this new product vector space. Being able to visualize results of semantic claims is an important guide to getting it right, along with making correct predictions or “Turing/behavioral appropriateness” tests.

Idea for a potentially cool application of MDS to music marketing:

There are many potential dimensions of music choice that can be recognized as significant:
Old vs. New
Punchy Rhythm vs. languid (and or danceable vs. not)
Fast vs. Slow Tempo
Electronic vs. Acoustic
Popular vs. Esoteric
Familiar to me vs. Unfamiliar to me
Large Volume Dynamic range vs. not
Well-tempered vs. Microtonal
Very Harmonious vs. Dissonant
Lots of Words vs Instrumental
Light vs. Dark tonalities
etc.

An application could help users to create their own MDS for music they liked with various examples, while also referencing the dimensions that others notice & care about. The task of building it could be visually interesting & engaging for a music fan, while the end goal is to help them find new music that fits the context of their search for a given listening application.

Their are many ways to view more than 2/3 dimensions at a time, including Trellis panels. It could become something that users share with others. In my experience, the ability to share music interests is a key driver of commerce.