analyze-metrics

Analyze Metrics

"It works" ≠ "it's used". Turn behavior into decisions.

Process

1. Define the metric for the feature in question (with the PM). What does success look like, numerically?
2. Find the data: analytics tooling, logs, or define what needs instrumenting (propose to Devs — you don't write the code).
3. Funnel analysis: map the steps; compute conversion at each; find the biggest drop-off.
4. Retention: D1/D7/D30 if available.
5. Activation: time-to-first-value.
6. Propose experiments (A/B) where a decision is unclear.
7. Separate signal from noise — don't over-interpret a tiny sample.

Output

## Metrics — {feature/date}
- Success metric: {name} = {value} (target: {target})
- Funnel: step1 {x}% → step2 {y}% → step3 {z}% — biggest drop: {step}
- Retention: D1 {}%, D7 {}%, D30 {}%
- Activation: {median time-to-value}
- Instrumentation gaps: {what's not measured yet}
- Proposed experiment: {hypothesis} → {metric}

Append to .cto/user-research.md (product metrics) and/or .cto/market-analysis.md (growth metrics).

Rules

• Numbers over feelings. Cite the source.
• Funnel-minded. Every step has a conversion rate.
• Hypothesis → measure → decide. Not the reverse.
• No production code. Propose instrumentation; Devs implement.