Media/Maven lessons
Segmenting accounts into 1:1, 1:few, and 1:many plays
Building an AI GTM Engine/Michael Slawson & Benyamin Holley/
A session from Building an AI GTM Engine. Coverage has to get past basic firmographics. Source a TAM, clean it, then score with first-party and third-party data before you segment. Some accounts are worth maximum effort, some consistent effort, a long tail gets long-tail effort. Closed-won analysis, call transcripts, and signals are the clues. Size the buckets to the team you actually have. Manual review is still part of scoring. They walk an AI-agent scoring example, then routing for 1:many.
Notes
- 01
Inputs to segmentation: closed-won, transcripts, signals, firmographics. Not just employee count and industry.
- 02
Clean the list before you score. Garbage in the TAM becomes garbage in the buckets.
- 03
Mix deterministic fields with probabilistic scores. Leave room for sweat equity, a human pass on the edge cases.
- 04
1:1, 1:few, and 1:many is a team-structure question as much as an account-value question.
- 05
Keep segment hygiene. Accounts move. Founder content and 1:many tactics should match the bucket they are in.
