Sales Outreach Playbook

Hey [Name] — I've been following your build-in-public updates and noticed you recently [mentioned pricing / crossed $X MRR / asked about raising prices].

I'm building PricingSim — it takes your Stripe transaction history and runs a Bayesian simulation to show the safest price increase your data supports, with a projected revenue lift and a one-click rollback if it doesn't work.

I'd love to run a free simulation on [product name] and share the output with you — no strings, just want to see if the tool produces something useful for a real product. Would that be interesting?

— [Your name]

This exact situation is why I'm building PricingSim. The fear is rational — a blind price raise can hurt, but so is leaving money on the table for years.

What I've found works: run a Bayesian elasticity model on your existing transactions. Even with 50–100 data points you can get a directional read on what price your buyers would accept without significant churn. The confidence interval is wide, but it's better than guessing.

Happy to run a free simulation on your data if you want to share a Stripe CSV export (anonymized if you prefer). DM me or check out pricingsim.app — it does this automatically.

Hi Arvid — longtime reader of The Bootstrapped Founder. I'm building PricingSim, a tool that runs Bayesian pricing simulations on Stripe/Gumroad data for solo founders who are nervous about raising prices.

I'd love to propose a collab: I run a live, unedited pricing audit on a volunteer from your community — we publish the output (what the data says, what the simulation recommends, what they actually decide to do). It'd make for a concrete, data-driven piece that's different from the usual "just raise your prices" advice.

No payment needed — just honest coverage if it produces something worth sharing. Interested?

— [Your name]

Hey IH 👋

I'm building PricingSim — a tool that takes your Stripe/Gumroad CSV, runs a Bayesian elasticity analysis, and tells you the safest price increase your data supports (with a confidence score and projected revenue lift).

The problem I kept seeing: solo founders set prices arbitrarily at launch, suspect they're underpriced, but won't touch them because they're scared of churn. There's no good way to test safely with small transaction volumes.

PricingSim uses a Normal-InvGamma conjugate prior (elasticity ε ~ N(-1.0, 0.5²)) that performs decently even with 30–100 data points. It's conservative by design — the rule is maximize E[R] subject to p05(R) >= 95% of current revenue.

**Free audit offer:** Drop your CSV at [link] — no account, no email, results in 30 seconds. Happy to run a manual analysis for anyone who wants to discuss their specific situation.

Would love feedback: does the output feel trustworthy, or does it feel like a black box?