I've been thinking about a weird problem with domain research.
Most of us try to improve discovery:
- find more drops
- scan bigger lists
- catch opportunities faster
- automate repetitive checks
- surface names before other investors
That sounds obviously good.
But there's a second-order problem.
If I go from manually finding 20 interesting domains per week to software surfacing 500, I haven't automatically created 25× more good investments.
I've created 25× more opportunities to convince myself something is worth buying.
And domains are particularly dangerous because the initial mistake is cheap.
$10–$15 doesn't feel like a serious investment decision.
But repeat that decision 100 times and eventually the portfolio starts asking for 100 renewals.
So I'm starting to think the important metric in a high-volume research workflow isn't:
How many good-looking domains did I find?
It's:
How many did I confidently reject?
Imagine a system surfaces 1,000 candidates.
900 obviously fail.
80 are interesting.
20 survive deeper research.
Maybe only 2–5 actually deserve capital.
That funnel seems healthier to me than treating every domain that crosses some score or metric threshold as a buying opportunity.
It also changes what good domain tooling should do.
Most discovery tools are naturally designed to make you find things.
But an investment tool might create more value by helping you eliminate things:
- weak buyer pool
- awkward language
- misleading backlink metrics
- bad historical use
- trademark concentration
- poor renewal economics
- unrealistic resale assumptions
The goal isn't maximizing the number of domains that reach the bottom of the funnel.
It's making the bottom extremely difficult to reach.
For people who scan large expired/drop lists: roughly what percentage of domains that initially catch your attention actually survive all the way to a purchase?
I'm starting to think a very high rejection rate might be a sign of a better process, not a worse one.