GTM Motion Decision — B2B SaaS

Variables to fill in: {{product_stage}}{{target_customer}}{{resources}}

You are a go-to-market operator who has taken products from zero to repeatable revenue. I need your help in the context of a B2B SaaS startup selling recurring software subscriptions.

TASK: Choose between self-serve, sales-led, or hybrid motion.

DELIVERABLE: Produce a motion analysis: what the price point and buyer math permit, what each motion demands of product and team, the recommendation, and the signals that would trigger a motion change.

PROCESS:
1. Review the inputs below. If anything critical is missing or ambiguous, ask me up to three clarifying questions before producing the deliverable.
2. Define the beachhead from the inputs: the narrowest customer segment that desperately needs this now.
3. Match motion to economics: how the price point, sales cycle, and customer count constrain the GTM options.
4. Design for learning velocity — early GTM is an experiment engine, not a scaling machine.
5. Produce the deliverable in a clean, skimmable format I can use directly.

QUALITY BAR:
- Channel recommendations come with expected math, not vibes.
- Pricing is grounded in value delivered and alternatives, not cost-plus.
- Every plan defines what 'working' means numerically before starting.

INPUTS:
- The product, its stage, and evidence of demand so far: {{product_stage}}
- Best guess at the ideal customer and what they pay today for alternatives: {{target_customer}}
- Team, budget, and time available for GTM: {{resources}}

Run this prompt on a real model without leaving the page. Every run is saved to your history for this prompt.

How to use

1. 1Fill in the {{product_stage}}, {{target_customer}}, {{resources}} variables with your real details — specifics in, specifics out.
2. 2Paste the prompt into ChatGPT, Claude, Gemini, or any capable model.
3. 3Answer the clarifying questions it asks; that step is what makes the output fit your situation.
4. 4Iterate on the deliverable: ask for alternatives, tighter versions, or a different angle on any section.

Pro tips

• If the output feels generic, add more concrete detail to the inputs — names, numbers, and constraints sharpen everything.
• Works well in a thread: keep the conversation going to refine a motion analysis.