#retention

A genuinely useful SaaS growth pack should contain one instrumentation playbook, one pricing guide, one onboarding reference, and one experimentation framework. That combination is enough to keep a team honest about what is product work versus campaign work.

The kinds of materials worth saving in this space:
- operator writeups about activation redesigns
- pricing case studies with before-and-after metrics
- retention frameworks tied to actual user behavior

Read:
- PostHog growth handbook: posthog.com/handbook/growth
A rare public handbook that shows how a product team talks about growth in practice.
- Stripe SaaS pricing guide: stripe.com/resources/more/saas-pricing-guide
Solid framing for packaging, monetization models, and pricing tradeoffs.
- Intercom on user onboarding: intercom.com/blog/user-onboarding/
Helpful for teams redesigning the first-run experience around actual user value.

Documents and downloadable guides:
- PostHog docs: posthog.com/docs
A good product-and-instrumentation reference for teams trying to clean up their event model.
- GrowthBook docs: docs.growthbook.io/
Helpful when experimentation needs to stay grounded in flags, metrics, and rollout mechanics.

Watch:
- PostHog video archive: youtube.com/@PostHog/videos
Product, analytics, and growth discussions from a team that ships in public.

Build or inspect:
- PostHog source: github.com/PostHog/posthog
Useful if you want to see how an open product analytics stack is assembled.
- GrowthBook source: github.com/growthbook/growthbook
A practical open-source reference for experimentation infrastructure.

Image references:
- PostHog product analytics reference: posthog.com/product-analytics
Useful screenshots and concepts for thinking about funnels, activation, and retention visually.

The recurring mistake is optimizing acquisition while the first-run experience remains muddy. The next one is running pricing experiments without a packaging hypothesis, which usually means the test is teaching less than the dashboard suggests.

Common traps to watch:
- optimizing acquisition while activation remains fuzzy
- running pricing tests without clear packaging hypotheses
- calling every onboarding step a growth lever

References that help correct the drift:
- Stripe SaaS pricing guide: stripe.com/resources/more/saas-pricing-guide
Solid framing for packaging, monetization models, and pricing tradeoffs.
- PostHog product analytics reference: posthog.com/product-analytics
Useful screenshots and concepts for thinking about funnels, activation, and retention visually.

This folio post is meant to be saved and revised. Add examples from your own work whenever one of these mistakes keeps resurfacing.

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.