AI newsletter analytics tool
AI analysis of a newsletter's past issues and reader behaviour, telling the writer what to publish next.
Estimated cost
Under $20,000 for a no-code build, plus AI usage fees
Who you sell to
Run from India, customers abroad
First version
One person can build it with AI tools
What it is
Newsletter writers can see opens and clicks but not why some issues work. This tool connects to an account on Beehiiv, ConvertKit or Substack, or takes an exported file, and uses AI to group past issues by topic, format and sending time. It shows what drove opens, clicks and unsubscribes, forms simple reader groups from their behaviour, and suggests what to write next. A first version can run on Supabase, a no-code builder like Bubble and the OpenAI API, with Make or Zapier moving the data around. Prices are in dollars, for creators worldwide.
The problem it solves
A writer with thousands of subscribers sits on years of issues and data but has no time to dig through it. Built-in dashboards show averages. The writer still guesses which topics, subject lines and send times work, and notices too late when readers drift away. Agencies and company newsletters face the same problem across several clients.
Who it suits
- Newsletter writers or agency owners who feel this pain themselves
- No-code builders comfortable connecting tools and data
- Analysts who can tell real patterns from noise
How it makes money
- Starter plan at $29 a month for up to 5,000 subscribers analysed
- Professional plan at $79 a month for up to 25,000 subscribers
- Enterprise plan at $199 a month with no subscriber limit
- Done-for-you content audits at $500 to $2,000 each
How to test it this month
- 1Find five newsletter writers with a few thousand readers, and offer each a free review of their last 20 issues from an exported file.
- 2Do those reviews by hand with an AI model, write a one-page report for each writer, and ask whether they would pay for it monthly.
- 3Sell the review alone first, at a low price. Build the dashboard only when three people ask you for the same report again.
What to watch out for
Your insights are only as good as the data, and small lists give noisy results: a topic that beat the average once may simply have been luck. You also handle data about readers, so work with totals rather than email addresses, and check each platform's terms on data access. Platforms can add their own analytics.
Costs and prices are estimates from our plan, not results from a real business. Nothing here promises income.
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