address feedback

contents/teams/experiments/index.mdx

@@ -18,27 +18,63 @@ Some of the things we're working on:
 
 ## User personas
 
-### Who we're building for
+### Assessing customer fit
+Use this guide to determine if a prospect's use case is a good fit for Experiments now, in the future, or at all.
+
+### Engineers (product, growth, software, etc.)
+**✅ Fully supported now**
 
-**Engineers (product, growth, software, etc.)**
 They get the most value since they can run experiments end-to-end, which allows them to test ideas quickly without coordination overhead.
 
-**Product Managers**
+**Best fit:** Teams where engineers can run experiments directly.
+
+---
+
+### Product Managers
+**✅ Fully supported now**
+
 They use PostHog to decide what to test, monitor experiment results, and make data-driven decisions. While they typically need an engineer to implement the test, they can independently analyze results using our charts, statistical analysis, and AI summaries.
 
-### Who we somewhat support
+**Best fit:** PMs who work with engineers for implementation but want to independently analyze results.
 
-**Data Scientists/Analysts**
-They can use our frequentist and bayesian analysis, view delta charts, timeseries, and funnel breakdowns. We're missing some advanced features like CUPED that the most sophisticated teams might need.
+---
 
-**Growth/Marketing teams**
-They can run experiments on messaging, landing pages, and campaigns - but need engineering help to implement them. Unlike pure no-code tools, they can't set up tests independently.
+### Data Scientists/Analysts
+**⏳ Growing support → Full support planned (2026)**
 
-**Designers**
-They can implement simpler experiments themselves - copy changes, styling updates, basic UI variations. For complex components or application logic, they'll need engineering support.
+**What works today:** Frequentist and bayesian analysis, delta charts, timeseries, funnel breakdowns
 
-### Note on no-code experiments
-We have a no-code experiment editor in beta, but it has limited support and doesn't cover all use cases. We're working on AI-powered visual editing that will generate implementation code automatically, making experiments more accessible to non-engineers.
+**Coming soon:** Advanced statistical methods (CUPED, variance reduction techniques, multilayer experiments) - planned for 2026
+
+**Is this customer a fit?**
+- ✅ Yes, if they need standard statistical analysis
+- ⏳ Wait or set expectations if they require advanced methods like CUPED (coming 2026)
+
+---
+
+### Growth/Marketing teams
+**⏳ Limited support → Full support planned (further out)**
+
+**What works today:** Can run experiments on messaging, landing pages, campaigns - but require engineering help for implementation
+
+**Coming later:** AI-powered visual editor will enable independent experiment setup without engineering (further out - requires mature code generation)
+
+**Is this customer a fit?**
+- ✅ Yes, if they have dedicated engineering resources
+- ⏳ Wait if they need no-code/independent setup (AI editor coming but no firm timeline)
+
+---
+
+### Designers
+**⏳ Limited support → Full support planned (further out)**
+
+**What works today:** Can implement simple experiments (copy changes, styling updates) independently; need engineering for complex components
+
+**Coming later:** AI-powered visual editor will enable more complex implementations (further out - requires mature code generation)
+
+**Is this customer a fit?**
+- ✅ Yes, if simple UI experiments or they have engineering support
+- ⏳ Wait if they need to independently implement complex UI changes (AI editor coming but no firm timeline)
 
 
 ## Slack channel