Verified against ChatGPT · 2026-08-03
Pick a GTM channel by testing cheaply across many, not betting on the obvious one
Rank candidate go-to-market channels by fit and cost of a cheap test rather than raw potential, then get a pre-committed pass/fail test plan for the top two before spending real budget.
The prompt
Ready to copy — highlighted parts are example details you can swap.
You are a GTM strategist using the "bullseye" approach to channel selection: test cheaply across many channels before committing real budget to the one that looks obvious. Context: - Product: Automated insurance claim generation for dental clinics - Target customer: Dental office managers, who mostly find tools through their billing software vendor or peer referrals - Budget/resources available: $1,500/month, one founder doing GTM part-time - Channels being considered, if any: Cold email to office managers, dental conference booth, partnership with billing software vendors Task: 1. List candidate GTM channels — use Cold email to office managers, dental conference booth, partnership with billing software vendors plus any others clearly relevant to how Dental office managers, who mostly find tools through their billing software vendor or peer referrals actually discovers solutions like this, given $1,500/month, one founder doing GTM part-time. 2. Score each channel on three axes only: (a) fit — how closely it matches where Dental office managers, who mostly find tools through their billing software vendor or peer referrals already looks for solutions, (b) cost/effort to run a real cheap test, (c) speed to a usable signal. Do not score on scale or long-term potential — that's a later-stage question, and scoring for it now is what leads teams to over-invest in a channel before it's proven at small scale. 3. Select the top 2 channels by that scoring and design a 2-week test for each, with a pass/fail metric stated in advance, before any test result exists to argue with. 4. Explicitly name any channel from the list that's a poor fit given the customer's actual buying behavior, and say why — so it doesn't get tried later just because it's popular or "what everyone does." Format: scored table, then two test plans with pre-committed pass/fail metrics.
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Why this works
This is the Bullseye framework from Gabriel Weinberg and Justin Mares' Traction: rank many channels cheaply on fit, cost, and speed to signal before committing real budget to any one of them, because early-stage teams reliably default to whichever channel is most visible — usually the one their competitors are already using — rather than the one that actually fits how their specific customer buys. Explicitly excluding scale/potential from the scoring matters because that's the axis that makes an unproven channel look attractive on a spreadsheet before it's been tested at all; potential is a real question, but it's the wrong question to optimize for with the first dollar. Pre-committing the pass/fail metric before running the test is what stops a team from quietly redefining 'success' after a lukewarm result comes in, which is the most common way a bad channel keeps getting funded past the point the data already killed it.
Verified against
ChatGPT GPT-5.1 · 2026-08-03
Changelog
- 2026-08-03 — Initial publish.
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