Growth Analytics

Growth & Analytics

Guide founders from defining their first metrics (pre-launch) through sophisticated retention analysis and experimentation (post-launch).

Workflow

1. Diagnose Current State

Ask: "Where are you in your analytics journey?"

2. Metrics Framework

Build a metrics system that drives the right behavior. See references/metrics-frameworks.md for complete framework library.

AARRR Pirate Metrics

The universal startup funnel framework:

Stage-Appropriate Focus:

North Star Metric

One metric that best captures core value delivered to customers.

Selection Criteria:

1. Measures value — Correlates with customers getting value
2. Leading indicator — Predicts future revenue/growth
3. Actionable — Team can influence it
4. Simple — Easy to understand and communicate

North Star Examples by Business Model:

Supporting Metrics:

Every North Star needs 3-5 supporting metrics that explain HOW to move it:

North Star: Weekly Active Teams (B2B SaaS)
├── Activation: Teams completing onboarding
├── Engagement: Features used per team
├── Expansion: Seats added per team
└── Retention: Team churn rate

One Metric That Matters (OMTM)

For early-stage focus, pick ONE metric for a defined period:

OMTM Selection:

1. What's the biggest constraint right now?
2. What metric would prove that constraint is solved?
3. Can you move it in 4-8 weeks?

Examples:

• Pre-launch: "Waitlist signups" (validate demand)
• Beta: "D7 retention" (validate stickiness)
• Post-launch: "Activation rate" (validate onboarding)
• Growth: "Payback period" (validate unit economics)

3. A/B Testing & Experimentation

Run experiments that generate reliable insights. See references/ab-testing.md for templates and calculators.

Experiment Design Framework

Hypothesis Structure:

If we [change], then [metric] will [improve/decrease] by [amount]
because [reason based on user insight].

Example:

If we reduce signup form from 5 fields to 3 fields, then signup completion rate will increase by 15% because user research shows form length is the #1 drop-off reason.

Before Running Any Test

Pre-flight Checklist:

Sample Size Estimation:

For 80% power and 95% confidence:

• 10% baseline, detect 10% relative lift → ~15,000 per variant
• 10% baseline, detect 20% relative lift → ~4,000 per variant
• 2% baseline, detect 20% relative lift → ~20,000 per variant

Rule of thumb: Multiply expected traffic by test duration. If you can't reach minimum sample in 4 weeks, the test isn't worth running—make a bigger change.

Running the Test

Test Execution Rules:

1. Run for full weeks (capture day-of-week effects)
2. Don't peek early—commit to duration
3. Track guardrail metrics (what shouldn't break)
4. Document everything before launch

Guardrail Metrics Examples:

• Revenue per user (main metric might improve but hurt revenue)
• Page load time (change might slow performance)
• Support tickets (change might confuse users)

Interpreting Results

Common Pitfalls:

• Stopping early when results look good (inflates false positives)
• Testing too many variants (dilutes sample)
• Ignoring segments (average hides important differences)
• No hypothesis (test without learning)

4. Retention & Cohort Analysis

Understand if users stick around. See references/retention-cohorts.md for SQL templates and benchmarks.

Retention Fundamentals

Types of Retention:

Critical Retention Points:

Cohort Analysis

Group users by signup date (or other dimension) to track behavior over time.

Cohort Table Structure:

Reading Cohort Tables:

• Rows = Compare cohorts (are newer users retaining better?)
• Columns = Retention decay (where's the biggest drop-off?)
• Diagonals = Same calendar week (external events)

Cohort Dimensions Beyond Time:

• Acquisition channel (organic vs. paid)
• Plan type (free vs. paid)
• First action taken (feature X vs. feature Y)
• Geography

Retention Curves

Healthy Curve Shape:

100% ─┐
      │╲
      │ ╲
      │  ╲____________________  ← Flattens = retention
      │
  0% ─┴─────────────────────────
      D1   D7   D30   D60   D90

Danger Signs:

• Curve never flattens (continuous bleed)
• Steep drop after D1 (activation problem)
• Drop at specific point (feature/billing issue)

Churn Analysis

Churn Rate Calculation:

Monthly Churn = Customers Lost This Month / Customers at Start of Month

Churn Benchmarks (SaaS):

Churn Diagnosis Questions:

1. When do they churn? (Tenure analysis)
2. Who churns? (Segment analysis)
3. Why do they churn? (Exit surveys, support tickets)
4. What predicts churn? (Behavioral signals)

5. Dashboard Design

Create visibility that drives action. See references/dashboard-design.md for templates and tool recommendations.

Dashboard Hierarchy

Level 1: Executive Dashboard (weekly, whole company)

• 3-5 top-level KPIs
• Trend vs. target
• One screen, no scrolling

Level 2: Functional Dashboards (daily, by team)

• Sales: Pipeline, conversion, activity
• Product: Engagement, retention, feature adoption
• Marketing: Acquisition, CAC, channel performance
• Support: Tickets, response time, CSAT

Level 3: Operational Dashboards (real-time, by function)

• Engineering: Uptime, latency, errors
• Sales: Daily activity, quota attainment

KPI Selection

For Each Metric, Answer:

1. What decision does this inform?
2. Who needs to see it and how often?
3. What's the target and why?
4. What action triggers if it's off-track?

Metric Types to Include:

Visualization Principles

Choosing Chart Types:

Dashboard Anti-Patterns:

• ❌ Too many metrics (more than 8-10 per view)
• ❌ No context (numbers without targets/trends)
• ❌ Vanity metrics (impressive but not actionable)
• ❌ Stale data (updated monthly when weekly needed)
• ❌ No owner (who acts on this?)

Tool Selection

Tool Recommendations by Stage:

Tool Comparison:

6. Anti-Patterns

Metrics Mistakes:

• Tracking everything, focusing on nothing
• Vanity metrics (total signups vs. active users)
• Lagging-only metrics (revenue without leading indicators)
• No targets (data without context)

Experimentation Mistakes:

• Testing small changes on low-traffic pages
• Multiple changes in one test (can't isolate effect)
• Stopping tests early based on early results
• No hypothesis (random changes)

Retention Mistakes:

• Only looking at aggregate retention (hiding segment issues)
• Ignoring activation (retention starts at first experience)
• Not defining "active" clearly

Dashboard Mistakes:

• Dashboard nobody checks
• Real-time when weekly is sufficient
• No owners assigned to metrics

Deliverables

1. Metrics Framework Document

Create as markdown:

• North Star metric with rationale
• AARRR funnel with specific metrics
• Supporting metrics hierarchy
• Targets and owners

2. Metrics Tracker Spreadsheet

Create using xlsx skill:

• AARRR funnel metrics with weekly/monthly tracking
• Formulas for calculated metrics (conversion rates, growth rates)
• Target vs. actual comparison
• Charts for trends

3. A/B Test Plan

Create as markdown:

• Hypothesis statement
• Variants description
• Primary and guardrail metrics
• Sample size and duration calculation
• Success criteria

4. Cohort Analysis Spreadsheet

Create using xlsx skill:

• Cohort table (rows = cohorts, columns = time periods)
• Retention percentages with conditional formatting
• Retention curve visualization
• Cohort comparison charts

5. Dashboard Specification

Create as markdown:

• KPI hierarchy (executive → functional → operational)
• Metric definitions with formulas
• Visualization recommendations
• Data sources and refresh frequency
• Tool recommendation with rationale

6. SQL Query Templates

Create as markdown:

• Cohort retention query
• Funnel conversion query
• Active user calculation
• Churn identification query

Reference Files

• references/metrics-frameworks.md — AARRR deep dive, North Star selection guide, metrics by business model, anti-patterns
• references/ab-testing.md — Experiment templates, sample size calculator, significance interpretation, SQL queries
• references/retention-cohorts.md — Cohort methods, retention curves, SQL templates, benchmarks by model
• references/dashboard-design.md — Dashboard templates, visualization guide, tool comparison

Integration with Other Skills

• Use business-model skill for unit economics metrics (LTV, CAC, payback)
• Use product skill for feature prioritization based on analytics
• Use go-to-market skill for channel-specific acquisition metrics
• Use operations skill for OKRs aligned with metrics framework
• Use fundraising skill for investor-ready metrics presentation
• Use xlsx skill for metrics trackers and cohort spreadsheets
• Use docx skill for analytics documentation

Adapted from Linas Beliūnas's The One-Person Unicorn founder skill set.