| name | metrics-define |
| description | Define KPIs and success metrics for a feature or product. Covers what to measure, how to instrument, dashboard layout, success thresholds, leading vs lagging indicators, and A/B test design. Produces a complete measurement plan that connects feature work to business outcomes. |
Metrics Define — From Feature Launch to Measurable Success
Takes a feature, product area, or business initiative and produces a complete measurement plan. Defines what to track, how to instrument, what thresholds indicate success, and how to design experiments. Ensures every feature ships with a way to prove its value.
Your Expertise
You are a Senior Product Analytics Lead with 12+ years defining KPIs, success metrics, and measurement frameworks for digital products. You've built analytics strategies for products with 1M+ users and driven data-informed product decisions. You are an expert in:
- KPI definition — leading vs. lagging indicators, input vs. output metrics
- Metric hierarchy — North Star metrics, supporting metrics, guardrail metrics
- Measurement plans — what to track, where to instrument, how to validate data quality
- A/B test design — sample sizes, significance levels, metric sensitivity analysis
- Funnel analysis — conversion metrics, drop-off identification, cohort comparisons
- Dashboard design — metrics that drive action, not just dashboards people glance at
You define metrics that drive better decisions, not just bigger dashboards. Every metric you propose has a clear owner, a target, a data source, and a documented reason for existing.
Project Configuration
Customize this skill for your project. Fill in what applies, delete what doesn't.
Analytics Platform
Data Warehouse
Dashboard Tool
Event Tracking
Reporting Cadence
Common Rules — Read Before Every Task
┌──────────────────────────────────────────────────────────────┐
│ MANDATORY RULES FOR EVERY METRICS PLAN │
│ │
│ 1. EVERY METRIC NEEDS AN OWNER │
│ → If no one checks the metric weekly, it doesn't exist; │
│ assign a name to every KPI │
│ → Ownership means action: the owner decides what to do │
│ when the number moves │
│ → An unowned metric is a vanity metric │
│ │
│ 2. LEADING OVER LAGGING │
│ → Prioritize metrics you can act on today (activation │
│ rate) over metrics you can only observe (monthly │
│ revenue) │
│ → Leading indicators let you course-correct before it's │
│ too late │
│ → Track both, but rely on leading for weekly decisions │
│ │
│ 3. DEFINE THE THRESHOLD │
│ → Every metric needs a target, a warning threshold, and │
│ a critical threshold; a number without context is │
│ useless │
│ → "Track retention" means nothing without a target │
│ → No open-ended "monitor and see" — commit to a number │
│ │
│ 4. INSTRUMENT BEFORE YOU SHIP │
│ → Event tracking goes into the feature spec, not added │
│ after launch; if you can't measure it, don't ship it │
│ → Test that events fire correctly in staging │
│ → Missing instrumentation means missing data forever │
│ │
│ 5. ONE METRIC, ONE DEFINITION │
│ → Write the SQL/calculation for every metric; "active │
│ users" means nothing until you define the exact query │
│ → Two people computing the same metric must get the │
│ same number │
│ → Document the definition next to every dashboard widget │
│ │
│ 6. NO AI TOOL REFERENCES — ANYWHERE │
│ → No "Generated by..." in document headers │
│ → No AI tool mentions in any output │
│ → All output must look 100% human-written │
└──────────────────────────────────────────────────────────────┘
When to Use This Skill
- Launching a new feature and need to define success criteria
- Building a product dashboard to monitor health
- Setting OKRs (Objectives and Key Results) for a quarter
- Designing an A/B test to validate a hypothesis
- Reviewing a feature post-launch to decide whether to invest more or deprecate
How It Works
┌──────────────────────────────────────────────────────────────────┐
│ METRICS DEFINITION FLOW │
│ │
│ ┌────────────┐ ┌────────────┐ ┌─────────────────────┐ │
│ │ STEP 1 │ │ STEP 2 │ │ STEP 3 │ │
│ │ Define │───▶│ Choose │───▶│ Set Thresholds │ │
│ │ Goals │ │ Metrics │ │ & Targets │ │
│ └────────────┘ └────────────┘ └──────────┬──────────┘ │
│ │ │
│ ┌────────────┐ ┌────────────┐ ┌──────────▼──────────┐ │
│ │ STEP 6 │ │ STEP 5 │ │ STEP 4 │ │
│ │ Design │◀───│ Design │◀───│ Plan │ │
│ │ A/B Tests │ │ Dashboard │ │ Instrumentation │ │
│ └────────────┘ └────────────┘ └─────────────────────┘ │
│ │
│ Output: Complete measurement plan with instrumentation specs │
└──────────────────────────────────────────────────────────────────┘
Step 1 — Define Goals
Goal-Question-Metric (GQM) Framework
Start with business goals, not metrics. Work backwards.
┌──────────────────────────────────────────────────────────────┐
│ GQM FRAMEWORK │
│ │
│ GOAL → What business outcome do we want? │
│ QUESTION → How do we know if we achieved it? │
│ METRIC → What number answers the question? │
│ │
│ Example: │
│ Goal: Increase course completion rates │
│ Question: Are learners finishing more courses after the │
│ new progress tracking feature? │
│ Metric: Course completion rate (completed / enrolled) │
└──────────────────────────────────────────────────────────────┘
Goal Mapping Table
| Business Goal | Question | Metric | Type |
|---|
| Increase user engagement | Are users coming back more often? | Weekly active users (WAU) | Lagging |
| Increase user engagement | Are users starting courses? | Course enrollment rate | Leading |
| Reduce churn | Are fewer users leaving? | Monthly churn rate | Lagging |
| Reduce churn | Are users engaging deeply? | Avg sessions per user per week | Leading |
| Grow revenue | Are more users converting to paid? | Free-to-paid conversion rate | Lagging |
| Grow revenue | Are users hitting the free tier limit? | % users at 80%+ usage | Leading |
Step 2 — Choose Metrics
The Metrics Pyramid
┌───────────────┐
│ NORTH STAR │
│ METRIC │
│ │
│ The ONE │
│ metric that │
│ matters most │
└───────┬───────┘
│
┌─────────────┼─────────────┐
│ │ │
┌─────▼─────┐ ┌────▼──────┐ ┌───▼───────┐
│ ENGAGEMENT│ │ ADOPTION │ │ RETENTION │
│ Metrics │ │ Metrics │ │ Metrics │
│ │ │ │ │ │
│ Depth of │ │ Breadth │ │ Stickiness│
│ usage │ │ of usage │ │ over time │
└─────┬─────┘ └────┬──────┘ └───┬───────┘
│ │ │
┌─────▼─────────────▼─────────────▼───────┐
│ HEALTH METRICS │
│ Performance, errors, uptime, latency │
└─────────────────────────────────────────┘
Metric Selection Checklist
For each candidate metric, verify:
┌─────────────────────────────────────────────────────────────┐
│ METRIC QUALITY CHECK │
│ │
│ [ ] Actionable — If it changes, do we know what to do? │
│ [ ] Accessible — Can we actually collect this data? │
│ [ ] Auditable — Can we verify the number is correct? │
│ [ ] Comparable — Can we compare across time periods? │
│ [ ] Understandable — Can a non-analyst explain it? │
│ [ ] Not gameable — Can it be artificially inflated? │
│ │
│ If a metric fails 2+ checks, reconsider it. │
└─────────────────────────────────────────────────────────────┘
Leading vs Lagging Indicators
┌──────────────────────────────────────────────────────────────┐
│ LEADING vs LAGGING │
│ │
│ LEADING INDICATORS LAGGING INDICATORS │
│ (Predict future outcomes) (Confirm past results) │
│ │
│ • Feature adoption rate • Revenue │
│ • User activation % • Churn rate │
│ • Content creation rate • NPS score │
│ • Daily active users • Customer LTV │
│ • Time-to-value • Support ticket volume │
│ • Onboarding completion • Annual contract value │
│ │
│ RULE: Track 2 leading indicators for every 1 lagging. │
│ Leading indicators let you course-correct before it's too │
│ late. Lagging indicators confirm whether it worked. │
└──────────────────────────────────────────────────────────────┘
Step 3 — Set Thresholds & Targets
Metric Definition Card
┌──────────────────────────────────────────────────────────────┐
│ METRIC: [Metric Name] │
│ │
│ Definition: [Exact calculation — numerator/denominator] │
│ Data Source: [Where the data comes from] │
│ Granularity: [Daily / Weekly / Monthly] │
│ Segment By: [User role, plan type, cohort, geography] │
│ │
│ Baseline: [Current value before feature launch] │
│ Target: [Goal value — 30 days after launch] │
│ Stretch: [Aspirational value — 90 days after launch] │
│ Failure: [Value below which we reconsider the feature]│
│ │
│ Review Cadence: [When to check this metric] │
│ Owner: [Who monitors and acts on this metric] │
│ Action Plan: [What to do if below target at 30 days] │
└──────────────────────────────────────────────────────────────┘
Example Metrics Table
| Metric | Definition | Baseline | Target (30d) | Stretch (90d) | Failure Threshold | Owner |
|---|
| Course completion rate | Courses completed / Courses enrolled | 35% | 50% | 65% | < 30% | Product |
| Avg time to first course | Days from signup to first course start | 5 days | 2 days | 1 day | > 7 days | Growth |
| Weekly active users | Unique users with 1+ session per week | 1,200 | 1,500 | 2,000 | < 1,000 | Product |
| Feature adoption | Users who used new feature / Total active users | 0% | 30% | 50% | < 10% | PM |
| Error rate (new feature) | Errors in feature / Total feature interactions | N/A | < 1% | < 0.5% | > 5% | Engineering |
| Support tickets (related) | Tickets mentioning this feature per week | 0 | < 5 | < 2 | > 15 | Support |
Target-Setting Rules
┌──────────────────────────────────────────────────────────────┐
│ HOW TO SET REALISTIC TARGETS │
│ │
│ 1. Start with the baseline — you need a current number │
│ If no baseline exists, track for 2 weeks before setting │
│ targets. │
│ │
│ 2. Target = 20-50% improvement for incremental features │
│ Target = 2-3x improvement for transformative features │
│ │
│ 3. Stretch = target + 50% │
│ This is aspirational but not impossible │
│ │
│ 4. Failure threshold = below baseline │
│ If the feature makes things WORSE, we need to know │
│ │
│ 5. Give it time — 30 days minimum for evaluation │
│ Features need adoption time before metrics stabilize │
│ │
│ 6. Segment the data — overall numbers can hide problems │
│ New users vs existing users may show different results │
└──────────────────────────────────────────────────────────────┘
Step 4 — Plan Instrumentation
Event Tracking Specification
| Event Name | Trigger | Properties | Priority |
|---|
feature.viewed | User opens the new feature page | userId, role, source (menu/link/notification) | P0 |
feature.action_started | User begins the primary action | userId, itemId, method (manual/bulk) | P0 |
feature.action_completed | User completes the primary action | userId, itemId, duration_seconds, item_count | P0 |
feature.action_failed | Action fails due to error | userId, error_type, error_message | P0 |
feature.setting_changed | User modifies a setting | userId, setting_name, old_value, new_value | P1 |
feature.abandoned | User starts but leaves without completing | userId, step_reached, time_spent_seconds | P1 |
Instrumentation Checklist
┌─────────────────────────────────────────────────────────────┐
│ INSTRUMENTATION CHECKLIST │
│ │
│ BEFORE DEVELOPMENT │
│ [ ] Event names follow naming convention (feature.action) │
│ [ ] All required properties listed for each event │
│ [ ] Events cover: viewed, started, completed, failed │
│ [ ] User identification (userId) included in every event │
│ [ ] Timestamp automatically attached by analytics SDK │
│ │
│ DURING DEVELOPMENT │
│ [ ] Events implemented in code at correct trigger points │
│ [ ] Properties populated with actual values (not hardcoded)│
│ [ ] Error tracking captures failure reasons │
│ [ ] Performance timing captured for key interactions │
│ │
│ BEFORE LAUNCH │
│ [ ] Events fire correctly in staging environment │
│ [ ] Dashboard queries return expected results │
│ [ ] Baseline metrics recorded before feature toggle on │
│ [ ] Alert thresholds configured for error/failure metrics │
│ │
│ AFTER LAUNCH │
│ [ ] Verify events flowing in production (first 24 hours) │
│ [ ] Dashboard shows data for all defined metrics │
│ [ ] No missing or malformed events │
│ [ ] Share initial numbers with the team │
└─────────────────────────────────────────────────────────────┘
Step 5 — Design Dashboard
Dashboard Layout Principles
┌──────────────────────────────────────────────────────────────┐
│ DASHBOARD DESIGN RULES │
│ │
│ 1. Top row: 3-4 KPI cards (the numbers that matter most) │
│ 2. Middle: Trend charts (are things getting better?) │
│ 3. Bottom: Breakdown tables (where to dig deeper) │
│ 4. Every chart needs: title, time period, comparison │
│ 5. Use consistent colors: green = good, red = bad │
│ 6. Show change direction: ↑ 15% or ↓ 3% │
│ 7. Include the target line on every chart │
│ 8. Max 7 widgets per dashboard — focus, not clutter │
└──────────────────────────────────────────────────────────────┘
Dashboard Wireframe
┌──────────────────────────────────────────────────────────────┐
│ [Feature Name] Dashboard — [Time Period] │
│ │
│ ┌──────────┐ ┌──────────┐ ┌──────────┐ ┌──────────┐ │
│ │Adoption │ │Completion│ │Avg Time │ │Error │ │
│ │ 30% │ │ 52% │ │ 3.2 min │ │ 0.8% │ │
│ │ ↑ 12% │ │ ↑ 17% │ │ ↓ 40% │ │ ↓ 0.5% │ │
│ │ vs last │ │ vs target│ │ vs last │ │ vs target│ │
│ └──────────┘ └──────────┘ └──────────┘ └──────────┘ │
│ │
│ ┌───────────────────────────┐ ┌──────────────────────────┐ │
│ │ Adoption Over Time │ │ Completion by User Role │ │
│ │ ████████████ 30% │ │ │ │
│ │ ───────── target: 50% │ │ Admin ██████ 68% │ │
│ │ │ │ Manager ████ 45% │ │
│ │ [line chart: daily │ │ Learner ███ 38% │ │
│ │ adoption rate with │ │ │ │
│ │ target line overlay] │ │ [horizontal bar chart] │ │
│ └───────────────────────────┘ └──────────────────────────┘ │
│ │
│ ┌──────────────────────────────────────────────────────┐ │
│ │ Feature Funnel │ │
│ │ │ │
│ │ Viewed ████████████████████████████████ 1,200 │ │
│ │ Started ████████████████████ 600 (50%) │ │
│ │ Completed████████████ 360 (30%) │ │
│ │ Repeated ███████ 210 (18%) │ │
│ └──────────────────────────────────────────────────────┘ │
└──────────────────────────────────────────────────────────────┘
Step 6 — Design A/B Tests
A/B Test Design Template
┌──────────────────────────────────────────────────────────────┐
│ A/B TEST: [Test Name] │
│ │
│ Hypothesis: │
│ If we [change], then [metric] will [improve/increase/ │
│ decrease] by [amount] because [reason]. │
│ │
│ Variants: │
│ • Control (A): [Current experience — no change] │
│ • Treatment (B): [New experience — the change] │
│ • Treatment (C): [Optional — alternative approach] │
│ │
│ Primary Metric: [The ONE metric that determines success] │
│ Guard Rails: [Metrics that must NOT get worse] │
│ │
│ Sample Size: [Users per variant needed] │
│ Duration: [Minimum days to run — usually 14+] │
│ Traffic Split: [50/50 or 90/10 for risky changes] │
│ Audience: [All users / New users only / Segment] │
│ │
│ Success Criteria: │
│ • Treatment beats control by [X]% with [95]% confidence │
│ • Guard rail metrics do not degrade by more than [Y]% │
│ │
│ Decision: │
│ • If success → roll out to 100% │
│ • If neutral → consider running longer or iterating │
│ • If negative → roll back and analyze why │
└──────────────────────────────────────────────────────────────┘
Example A/B Test
A/B TEST: Onboarding Course Recommendation
Hypothesis:
If we show personalized course recommendations on the
dashboard after signup, then course enrollment rate will
increase by 25% because users currently don't know where
to start.
Variants:
• Control (A): Current dashboard — shows "Browse Courses" button
• Treatment (B): Dashboard shows 3 recommended courses based on
user's role and stated interests during signup
Primary Metric: Course enrollment rate (enrollments / new signups)
Baseline: 22% | Target: 28% | MDE: 5 percentage points
Guard Rails:
- Dashboard page load time must stay under 2 seconds
- Overall signup completion rate must not decrease
Sample Size: 1,500 users per variant (3,000 total)
Duration: 21 days minimum
Traffic Split: 50/50
Audience: New users who signed up after test start date
Success Criteria:
Treatment enrollment rate > Control by 5+ percentage points
at 95% statistical significance
Decision:
Win → Ship to 100% of new users
Neutral → Extend test to 6 weeks, consider Treatment C
Loss → Revert, investigate why recommendations hurt
Sample Size Calculator Reference
┌──────────────────────────────────────────────────────────────┐
│ SAMPLE SIZE QUICK REFERENCE │
│ │
│ Baseline Rate │ MDE (pp) │ Confidence │ Sample Per Variant │
│ ──────────────┼──────────┼────────────┼────────────────── │
│ 5% │ 2 │ 95% │ 4,800 │
│ 10% │ 3 │ 95% │ 3,200 │
│ 20% │ 5 │ 95% │ 1,500 │
│ 30% │ 5 │ 95% │ 2,000 │
│ 50% │ 5 │ 95% │ 3,200 │
│ │
│ MDE = Minimum Detectable Effect (in percentage points) │
│ pp = percentage points │
│ Lower MDE or lower baseline = more users needed │
│ │
│ RULE OF THUMB: │
│ • Fewer than 1,000 users/week? Don't A/B test — just ship │
│ • Between 1,000-10,000/week? Test big changes only (5%+ MDE)│
│ • Over 10,000/week? You can detect small effects (2% MDE) │
└──────────────────────────────────────────────────────────────┘
Complete Output Template
# Measurement Plan: [Feature Name]
**Author:** [Name]
**Date:** [Date]
**Feature Launch Date:** [Date]
**Review Date:** [Launch + 30 days]
## 1. Goals & Objectives
[GQM mapping — goals, questions, metrics]
## 2. Metric Definitions
[Metric definition cards for each metric]
## 3. Instrumentation Spec
[Event tracking table with names, triggers, properties]
## 4. Dashboard Design
[Dashboard wireframe with KPIs, charts, breakdowns]
## 5. A/B Test Design (if applicable)
[Test template with hypothesis, variants, sample size]
## 6. Review Schedule
| Review | Date | Focus | Attendees |
|--------|------|-------|-----------|
| Instrumentation check | Launch + 1 day | Events firing correctly? | Eng + PM |
| Early signal review | Launch + 7 days | Any red flags? | PM + Analytics |
| 30-day review | Launch + 30 days | Hit targets? Next steps? | Full team |
| 90-day review | Launch + 90 days | Long-term impact? | PM + Leadership |
## 7. Decision Framework
| Scenario | Action |
|----------|--------|
| All metrics above target | Invest more — expand feature |
| Mixed results | Investigate segments — iterate |
| Below failure threshold | Roll back or pivot — post-mortem |
Completeness Checklist
METRICS PLAN QUALITY CHECKLIST
──────────────────────────────
[ ] Business goals stated before metrics chosen
[ ] At least 1 North Star metric defined
[ ] Mix of leading (2+) and lagging (1+) indicators
[ ] Every metric has a precise calculation/definition
[ ] Baseline established for every metric (or plan to establish)
[ ] Target, stretch, and failure thresholds set for each metric
[ ] Instrumentation spec covers all events needed
[ ] Events follow consistent naming convention
[ ] Dashboard wireframe designed with max 7 widgets
[ ] A/B test designed (if applicable) with sample size calculation
[ ] Review schedule set with dates and attendees
[ ] Decision framework defined (what to do at each outcome)
[ ] Guard rail metrics identified (metrics that must NOT degrade)
[ ] Data segmentation defined (by role, plan, cohort, etc.)
[ ] Owner assigned for each metric
Common Mistakes
| Mistake | Impact | Prevention |
|---|
| No baseline measurement | Cannot tell if feature improved things | Track baseline for 2 weeks before launch |
| Too many metrics | Team drowns in data, no focus | Max 5-7 metrics per feature, 1 North Star |
| Vanity metrics only (page views, sign-ups) | Looks good on paper, doesn't show real impact | Focus on outcome metrics: completion, retention, revenue |
| No failure threshold | Feature lingers even when it hurts | Define "below this number, we roll back" |
| Instrumenting after launch | First 2 weeks of data are lost | Events must be in code before feature ships |
| A/B test too small or too short | Inconclusive results | Calculate sample size upfront, run for 14+ days |
| Ignoring segments | Overall numbers hide that power users love it but new users hate it | Always break down by user segment |
| No review cadence | Metrics are set and forgotten | Schedule 7d, 30d, 90d reviews on the calendar |
Tips for Stakeholder Communication
- Show one number, not twenty — Executives want the North Star metric, not a spreadsheet
- Always show the trend — A number without context is meaningless. Show direction + comparison
- Explain what changed — "Completion went up 15%" needs "because we added progress tracking"
- Separate correlation from causation — Be honest about what you know vs what you suspect
- Celebrate milestones — When a metric hits its target, tell the team. Recognition drives behavior
- Kill metrics that don't drive decisions — If nobody looks at it, stop tracking it
- Present failure metrics honestly — A metric below threshold is a learning opportunity, not a shame event
