# Turn product analytics into weekly growth experiments

> Use funnels, recordings, and events to choose sharper product growth experiments instead of guessing.

- Canonical: https://gptnavi.com/workflows/product-analytics-to-growth-experiments
- Category: Productivity
- Difficulty: Intermediate
- Setup time: 2 hours
- Estimated time saved: 3-8 hours
- Last materially updated: 2026-08-24
- Best for: Product teams, Founders, Growth teams, SaaS teams
- Tools: PostHog, Microsoft Clarity, ChatGPT, Linear, Notion

## Quick answer

This workflow connects product analytics with the actual weekly planning loop so insights become experiments.

## When to use it

- Activation improvement
- Onboarding optimization
- Feature adoption
- Conversion experiments

## Steps

1. **Pick one metric** — Choose a target metric such as activation, trial-to-paid conversion, onboarding completion, or feature adoption. Tool: PostHog. Expected output: One focused metric and baseline.
2. **Inspect funnel drop-offs** — Review the funnel and list where users drop, hesitate, repeat actions, or abandon. Tool: PostHog. Expected output: A friction map.
3. **Watch user sessions** — Review a small sample of recordings around the biggest drop-off point. Tool: Microsoft Clarity. Expected output: Observed UX friction and hypotheses.
4. **Generate experiment candidates** — Ask AI to turn friction evidence into experiment ideas with effort, risk, and expected impact. Tool: ChatGPT. Expected output: A ranked experiment backlog.
5. **Ship and review** — Create implementation issues, define success criteria, and review results next week. Tool: Linear. Expected output: Prioritized growth experiments with owners.

## Prompt templates

### Friction to experiment

Turn this funnel and session evidence into growth experiments. Include hypothesis, change, target segment, expected impact, effort, risk, and success metric. Evidence: [paste]

### Weekly growth review

Summarize this week's experiment results. Identify what changed, what we learned, what to stop, and what to test next. Results: [paste]

## Common mistakes

- Looking at too many metrics at once
- Jumping from analytics to redesign without watching real sessions
- Not defining success before shipping

## Related workflows

- https://gptnavi.com/workflows/customer-interviews-to-landing-page
- https://gptnavi.com/workflows/summarize-support-tickets-into-product-insights
- https://gptnavi.com/workflows/research-a-niche-market-with-ai