Rules/Methods to use for determining user groups with no review data I’m working on an analytical problem where I have customers who deal with end consumers. End consumers can leave customers reviews based on their experience (Star ratings, text etc). I’m trying to segregate new/old customers into different quality categories: good, average, bad, for an online advertising platform. I’m looking for insights on rules/methods on how to do this


How to set a quantifiable threshold for calling a customer good, average, bad for business? There is consumer review data available (Imagine a table with customer, and the star rating they received, and/or textual reviews from consumer)
How to classify a new customer on the platform with no or minimal review data available about them particularly, while there’s also some customers who have many reviews available?
Start with some natural thresholds : >3* average, >4* good, >4,5*: excellent, 4,9+* perfect. Then you can correct your rating based on some averages, other metrics or even text (but that's hard). Honestly I am not sure it will work as ratings should be viewable and giving different status to customers with same average rating will get noticed. 
Leave them blank, consumers like to know they are first to comment / know there is no reliable rating. No comment is an interesting information. This will push both customers and consumers to give a rating.