# Weekly agentic CRO experiment loop

> Use behavior data to identify one conversion bottleneck, draft a controlled experiment, and decide from evidence what to keep or reverse.

- Canonical: https://gptnavi.com/workflows/weekly-agentic-cro-experiment-loop
- Category: Marketing
- Difficulty: Intermediate
- Setup time: 2.5 hours
- Estimated time saved: 3-7 hours
- Last materially updated: 2026-08-24
- Best for: Growth teams, Ecommerce teams, SaaS founders, Product marketers
- Tools: Splitsense, Basedash, PostHog, Figma Make, Notion

## Quick answer

Agentic optimization can find patterns quickly, but traffic allocation, claims, pricing, and product changes need a human-owned hypothesis and stop condition.

## When to use it

- Pricing page
- Signup funnel
- Checkout flow
- Product activation

## Steps

1. **Define the decision metric** — Choose one primary conversion event, a guardrail metric, minimum evidence threshold, and the owner who can approve a change. Tool: Notion. Expected output: An experiment charter.
2. **Inspect the funnel** — Review trusted events and segments to identify a specific point of friction, not merely the lowest-converting page. Tool: PostHog. Expected output: A measurable friction hypothesis.
3. **Ask for a governed analysis** — Query the shared metric layer for related changes, cohorts, revenue context, and possible confounders. Tool: Basedash. Expected output: A verified analysis brief.
4. **Draft one reversible test** — Let the optimization agent propose a focused page or flow experiment, then edit it so the hypothesis, audience, and risk remain clear. Tool: Splitsense. Expected output: A human-reviewed experiment draft.
5. **Visualize and decide** — Create the tested state, review on desktop and mobile, then keep, revise, or stop the change based on the agreed evidence threshold. Tool: Figma Make. Expected output: A documented weekly decision.

## Prompt templates

### Experiment brief

Turn this conversion observation into one reversible experiment. Include evidence, audience, hypothesis, exact change, primary metric, guardrail, minimum sample or time window, risks, and stop condition. Observation: [paste]

### Results review

Review this experiment result. State whether evidence supports keep, revise, or stop. Call out novelty effects, segment differences, tracking gaps, and the next smallest question. Results: [paste]

## Common mistakes

- Testing several ideas in one variant
- Optimizing a metric disconnected from value
- Rolling out a winner without checking key segments

## Related workflows

- https://gptnavi.com/workflows/product-analytics-to-growth-experiments
- https://gptnavi.com/workflows/launch-mvp-with-landing-page
- https://gptnavi.com/workflows/ai-creative-brief-to-short-campaign