User word2vec model output in larger kmeans project
There are two common approaches.

Taking the average of all words. That is easy, but the resulting vectors tend to be, well, average. They are not similar to the keywords of the document, but rather similar to the most average and least informative words... My experiences with this approach are pretty disappointing, despite this being the most mentioned approach.
par2vec/doc2vec. You add a "word" for each user to all it's contexts, in addition to the neighbor words, during training. This way you get a "predictive" vector for each paragraph/document/user the same way you get a word in the first word2vec. These are supposedly more informative but require much more effort to train - you can't download a pretrained model because they are computed during training.
I am attempting a rather large unsupervised learning project and am not sure how to properly utilize word2vec. We're trying to cluster groups of customers based on some stats about them and what actions they take on our website. Someone recommended I use word2vec and treat each action a user takes as a word in a "sentence". The reason this step is necessary is because a single customer can create multiple rows in the database (roughly same stats, but new row for each action on the website in chronological order). In order to perform kmeans on this data we need to get that down to one row per customer ID. Hence the previous idea to collapse down the actions as words in a sentence "describing the user's actions"
My question is I've come across countless tutorials and resources online that show you how to use word2vec (combined with kmeans) to cluster words on their own, but none of them show how to use the word2vec output as part of a larger kmeans model. I need to be able to use the word2vec model along side other values about the customer. How should I go about this? I'm using python for the clustering if you want to be specific with coding examples, but I could also just be missing something super obvious and high level. It seems the word2vec outputs vectors, but kmeans needs straight numbers to work, no? Any guidance is appreciated.