Indeed, distance-based approaches do not make a lot of sense on such data.

The idea of e.g. k-means is to gind groups of low *variance*, and that primarily makes sense on continuous data.

The popularity of these methods e.g. in marketing is probably best explained as follows:

1. someone read that clustering is cool
2. they loaded some data in some program that could run k-means or hierarchical clustering
3. after a lot of fiddling, they even got a result
4. the result didn't totally contradict their hypothesis, so they were happy and published it
5. now everybody wants to do this.

The results don't need to be sensible, well-founded, or better than random for this to work. If you are eager enough to puvblish something, qny method will do. Unfortunately.