After clustering, How do i choose the best customers (subset) from the top cluster?
Try some good old filtering! Select one or several features, create your own metric (maybe top customers are those who buy the most, or those who are more loyal/have stayedlonger with the company, or a weighted sum of those two factors), sort the 660 customers in your cluster and pick only the N first customers, N being your maximum allowed number of customers.
I ran a clustering exercise to identify my top customers based on 12 distinct features, using K-Means (on 3 PCA dimensions and 5 PCA dimensions) and GMM (using 5 PCA dimensions) methodologies. Both the outputs from the K-Means produced almost similar customers as the best set (1182 customers in each case with an overlap of 1156) while the GMM approach gave me 660 customers as my top customers. These 660 customers were present in both the K-Means approaches. 
Now i want to identify who my top customers are from among this list. Could you please suggest any statistical approaches that i could use to say that these X number of customers are truly my best set and run some A/B tests on them? I do not want to go with the full identified set as it might cost me more to do what is planned to such a large set of customers.