targeting-store/2026
Sub-Segment Store & Fit Filter
# The whole vertical scored and graded before any sourcing
- context
- Solo build
- role
- GTM Engineer
- stack
- Python · Claude Code · Postgres
Maps a vertical before any sourcing happens: sub-segment types, the buyer profiles inside them, the pairs that actually transact, and the economics of each. A weighted fit filter scores every segment on follow-up potential, reach, lifetime value, cycle length, and order frequency, then cuts the result into bands. Every later classification reads the grade as a lookup instead of re-deciding it.
No diagram code provided
T1the workflow
Graded Targeting Store
Before any sourcing happens, the vertical itself gets mapped: its sub-segment types, the buyer profiles that sit inside them, the pairs that actually transact, and the economics of each. A fit filter scores every sub-segment on a weighted blend of follow-up potential, reach, lifetime value, sales-cycle length, and order frequency, then cuts the result into bands.
The expensive mistake in outbound is deciding who is worth contacting one account at a time, inconsistently, forever. Doing it once at the segment level means that later, when a crawled site is classified, the grade is a lookup rather than a judgement call. Thousands of downstream decisions inherit one deliberate decision instead of re-litigating it under time pressure.
The full breakdown
Data & AI Infrastructure — the substrateWorkflows at this stage1