Channel Strategy And CAC Test Matrix
Prioritizes acquisition channels for your stage and designs cheap tests to find a scalable, profitable one.
Prompt
ROLE: You are a growth lead who finds the one or two channels that actually scale for a startup, using disciplined cheap tests. CONTEXT: Product: [PRODUCT]. Customer: [CUSTOMER] and where they spend time: [WHERE]. Price point: [PRICE] (affects which channels are viable). Stage and budget for tests: [BUDGET]. Channels tried so far: [TRIED_AND_RESULTS]. TASK: 1. From the full channel set (content/SEO, paid search, paid social, outbound sales, partnerships, community, marketplaces, virality/referral, events, PR), shortlist the 3-4 most plausible for MY customer and price point. Eliminate the obviously wrong ones and say why. 2. For each shortlisted channel, estimate fit, expected CAC range, time-to-signal, and the minimum viable test (budget, duration, success metric). 3. Sequence the tests: which to run first to learn fastest and cheapest, and the kill criteria for each. 4. Define what a 'winning channel' looks like (CAC payback and scalability thresholds) so I know when to double down. OUTPUT FORMAT: (1) Channel shortlist with eliminations explained; (2) Per-channel test card (fit, CAC range, signal time, MVP test); (3) Test sequence with kill criteria; (4) Winning-channel thresholds. CONSTRAINTS: Match channels to price point - low ACV can't support outbound sales, high ACV rarely works on cheap paid social. Run one channel test at a time with a clear success metric; reject the 'spray across all channels' impulse. Insist on kill criteria so I stop pouring money into a dead channel.
How to use this prompt
- 1
Copy the prompt above and paste it into ChatGPT, Claude, or Gemini — or open it in the visual Studio to edit each part on a canvas and run it with your own key.
- 2
Replace any bracketed placeholders with your specifics. The more concrete your context and constraints, the sharper the result — see the 5-part prompt structure.
- 3
Run it, then refine. Ask the model to critique and improve its own answer with self-critique prompting.
Techniques in this prompt
Assigns the model an expert persona so it adopts the right vocabulary, depth, and standards for the task.
Learn this techniqueA tree of thoughts technique used to shape and strengthen the model's response.
Forces explicit intermediate reasoning instead of jumping to a conclusion, which improves accuracy on hard tasks.
Learn this techniqueRecommended models
Build on this prompt
Open it in the visual Studio to wire it into a full workflow with your own API key — or learn the craft behind prompts like this.
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