ThinkWithPrompt by SynotticThinkWithPrompt

Improve free trial to paid conversion for my SaaS product

A diagnosis of where trials stall and a trial plan with in-app guidance, emails, and experiments to lift conversion.

Marketing & BrandRTFStandardEmailAnalyzePlanFill-in Template

The prompt

Help me improve free-trial-to-paid conversion for my SaaS product. Trial setup: [length, credit card required or not, which plan's features are included] Trial funnel data: [signups, % who activate, % who convert, median days to convert - or "not tracked"] Activation milestone: [the action that shows a user got value, if known] Who signs up: [roles, company sizes, sources] Current trial touchpoints: [emails, in-app tips, sales calls] Main reasons trials don't convert: [survey data, guesses] Give me: 1. A diagnosis: where trial users most likely drop off (signup, setup, first value, team adoption, payment) based on my data, and the data I'm missing. 2. Trial design choices to test: trial length, card upfront vs no card, reverse trial (premium trial that drops to free), and the trade-offs for my model. 3. A day-by-day trial journey table: day | trigger (behavior-based where possible) | channel (in-app, email, sales) | message goal | copy draft. 4. Behavior branches: a user who has activated vs one who has not, and a high-fit account that should get a human. 5. Upgrade moments: where to show pricing and limits in context without blocking value. 6. An end-of-trial sequence and a trial-expired recovery offer. 7. 5 experiments ranked by expected impact and effort, each with a hypothesis and success metric. Keep emails under 120 words each. Don't invent benchmarks; label any rule-of-thumb numbers as rough.

Highlighted [brackets] are blanks to replace with your own details.

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What to fill in

  • the action that shows a user got value, if known
  • roles, company sizes, sources
  • emails, in-app tips, sales calls
  • survey data, guesses

How this prompt is built

This prompt follows the RTF structure (Role, Task, Format). RTF tells the model who to be, what to do and what shape the answer should take. It is the quickest structure that still gives noticeably better results than a bare request.

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