Hey Collin I built an account scoring model in Clay at my last company.
Here's how I did it:
Exporting a list of all customers from CRM including ARR
Ran enrichment in Clay to gather data points like "PLG vs SLG", "OSS Core to Business Model", Popularity of Open Source Repos, etc.
Then I exported the fully enriched list to ChatGPT
I spent about 2.5 days just playing with the data. ChatGPT is really good at helping to prompt you for different slices and ways to explore
I tried running a k-means and linear regression, both bombed and didn't turn up anything good
I found the data points that had the most impact on deal size and time-to-close
I built the scoring model in Sheets then copied to a Clay Score
Iterated on the point system until I got known "great" , "good", and "ok" accounts scoring correctly
After building, I realized I should have built 3 scoring models specific to Commercial vs Enterprise Vs Renewals
There's no easy button on this. Great scoring comes from deep understanding of the market and customers. Clay helps make getting accurate data easier.
Hope that helps!
