this is what i was told: "Ted addressed this - the result list in Clay has to be less than 50,000 for the people search to work, and you're limited to 50k per table anyway.
His solution was to not do it all at once. Start with your best customers only (purchased, not just signups), then work through it in batches. He mentioned doing it company by company or segment by segment - like if Nike has 150 former employees, you pull everyone at that company, then filter by title and job function after.
The other thing he said was to use the company-level filters to narrow it down before you even get to people. So if your TAM analysis shows consumer software + B2C + growth stage companies convert best, apply those filters first to shrink the company list, then pull people from that smaller set.
He also mentioned you can run this as a recurring workflow - once a month, pull people who changed jobs in the last 30 days from customer companies. That way you're not trying to do the entire historical backlog at once, you're just catching new movers on a rolling basis.
So the practical approach: tier your customer list by value, start with purchased only, work through in chunks that stay under 50k, and set up a monthly job change trigger for ongoing"
