product-metrics

Product Metrics

When to Use

When you need to define what "success" means for a product or feature, and build the measurement system to track it.

Core Jobs

1. Define the North Star Metric

One metric that best captures the value users get:

• Must be actionable (team can influence it)
• Must correlate with business outcomes
• Examples: "weekly active users who complete 3+ tasks", "monthly recurring revenue" Avoid vanity metrics: total signups, page views, app downloads.

2. Build the Metric Tree

North Star → Input Metrics → Leading Indicators

• Acquisition: new users, activation rate, channel mix
• Engagement: DAU/MAU, feature adoption, session depth
• Retention: day-7/30/90 retention, churn rate
• Revenue: ARPU, LTV, conversion rate

3. Instrument the Feature

Before launch, define:

• What events to track (user action → event name → properties)
• What the baseline is (current state before change)
• What the target is (expected improvement, with confidence level)
• How long to measure (minimum 2 weeks post-launch for significance)

4. Interpret Results

• Check statistical significance before declaring success
• Segment results: by cohort, platform, plan tier
• Look for unintended consequences (metric moved, but another degraded)

Key Outputs

• North Star metric definition
• Input metric tree
• Instrumentation plan (event schema)
• Post-launch measurement report

Anti-Patterns

• Measuring too many metrics (pick 3–5 that matter)
• Declaring success after 3 days
• Ignoring segment-level data
• No baseline before launching