Recipe Analytics Strategy
Build a measurement framework that turns raw recipe data into actionable product decisions — without drowning in dashboards.
The Core Loop
Every recipe in Meridian generates three signals: creation, execution, and iteration. Track these as a funnel — not as isolated events. A recipe that gets created but never executed tells you something different than one that gets executed ten times and then abandoned.
Key Metrics
- Time-to-first-execution— how long after creation does a recipe actually run?
- Execution success rate— what percentage of runs complete without errors?
- Edit-to-execute ratio— are users tweaking recipes more than they run them?
- Recipe stickiness— how many recipes are still active 7, 30, and 90 days after creation?
Segmentation That Matters
Slice every metric by recipe type, trigger source, and user tier. A power user running webhook-triggered recipes has a completely different success profile than a free-tier user running manual recipes. Treat them as separate products.
Action Thresholds
Define triggers before you need them. If execution success rate drops below 92% for any recipe type, flag it for engineering review. If time-to-first-execution exceeds 48 hours for new users, investigate onboarding friction. Data without thresholds is just noise.
Pro tip
Instrument your recipe engine to emit structured events with a consistent schema. Use a lightweight event bus — not raw database queries — so you can swap analytics backends without touching recipe logic.