After running K-means on 12 features, I get an array containing clusters for each row. What is the next step after this?
You can run your K-Means for different values of K (e.g. k=2, 3, ..., N) and for each iteration you can compute the Silhouette metric, which provides a measurement on "how compact" your clusters are. You can plot your Silhouette score and find what is the K that makes the score minimum, or stable. Since this is a clustering operation and K-Means is a simple method (nothing bad about that), I would recommend using a second clustering method (e.g. t-SNE, DBSCAN) and apply a similar procedure to see whether there is some agreement between the models. This is a general framework in data science, just applied to a clustering problem.