SaaS Growth

A workable loop here is simple: define the user outcome, instrument the path to that outcome, study activated versus non-activated cohorts, then redesign onboarding or pricing with one clear hypothesis at a time. That sounds slow only until you compare it with random experimentation.

A sequence I would actually hand to a teammate:
1. Define the activation moment in terms of a concrete user outcome.
2. Instrument the path to that outcome so friction points are obvious.
3. Close the loop with lifecycle messaging, pricing, and in-product nudges.

Useful operating references:
- Stripe SaaS pricing guide: stripe.com/resources/more/saas-pricing-guide
Solid framing for packaging, monetization models, and pricing tradeoffs.
- PostHog source: github.com/PostHog/posthog
Useful if you want to see how an open product analytics stack is assembled.

If your team has a better workflow, post it with the context around team size, constraints, and exactly where the process tends to break.