ThinkWithPrompt by SynotticThinkWithPrompt

Design a paid subscription tier for my newsletter

Pick what to put behind the paywall, set pricing and tiers, and plan the launch without alienating free readers.

Content & Social MediaFew-ShotStandardEmailFill-in Template

The prompt

Help me design a paid subscription for my newsletter. Newsletter: [TOPIC, format, how often it goes out] Audience: [WHO reads it, subscriber count, open rate] Platform: [e.g. Substack, beehiiv, Ghost, ConvertKit] What readers value most: [replies, top posts, survey results] Time I can add each week: [HOURS] Goal: [e.g. "cover my costs", "replace part of my income"] Give me: 1. Five models that work for newsletters (e.g. extra paid issues, full archive, community access, deep-dive reports, templates or tools, live Q&A), with an example of how each could look for my topic and the workload it adds. 2. My best-fit option: what stays free, what becomes paid, and why that split keeps free readers growing. 3. Pricing: monthly and annual price suggestions, a founding-member offer, and whether to add a higher tier - with the reasoning. 4. A simple revenue estimate using assumptions I can change (conversion rate from free to paid, churn), clearly marked as assumptions. 5. A launch plan: pre-launch survey or waitlist, announcement issue, and the first 4 weeks of paid content. 6. How to measure whether it's working and when to adjust. Typical conversion and churn rates vary widely. Label any benchmark as a rough range and suggest I validate demand with my own readers before committing.

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

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What to fill in

  • TOPIC, format, how often it goes out
  • WHO reads it, subscriber count, open rate
  • e.g. Substack, beehiiv, Ghost, ConvertKit
  • replies, top posts, survey results
  • HOURS
  • e.g. "cover my costs", "replace part of my income"

How this prompt is built

This prompt follows the Few-Shot structure (Few-shot prompting). A few-shot prompt includes one or more worked examples of the input and the output you want, so the model can copy the pattern instead of guessing it.

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