Using feature importance to decet latent variables and grouping
You might be able to segmentate your items into groups that share a beer consumption trait by focusing on those variables. It's a good idea if you started with a lot of columns . Of course, expect more meaningful groups if your model is simpler rather than complex - a small decision tree rather than a random forrest, for example.

Why don't you do some descriptive statistics on these columns, dividing first your data in the target variable? The visualization of the different patterns can be quite inspiring.

Also, test your intuition: cluster your data based solely on these columns and check whether each cluster is significantly biased towards one of the target values.

Something that you can also try is using the notion of proximity between data points you get from a random forrest model in your clustering algorithm.