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Lab Logbook - Product Analytics Deep-Dive

Step-by-Step Execution Workflow

Prerequisites (10 minutes)

  • Python 3.10+ installed
  • Virtual environment created
  • Dependencies installed from requirements.txt
  • Project structure verified

Phase 1: Data Generation (30 minutes)

Objective

Generate realistic synthetic data simulating 50,000 Notion users over 2 years.

Steps

  1. Configure parameters in src/config.py:

    • Set user count: 50,000
    • Date range: Jan 2023 - Dec 2025
    • Business metrics (MAU, revenue, etc.)

  2. Run data generator:

   python src/data_generator.py

  1. Verify outputs:

    • Check data/synthetic/user_profiles.csv (50,000 rows)
    • Check data/synthetic/user_events.csv (~500,000-1M events)

  2. Review data quality:

    • User segments distributed correctly
    • Event types realistic
    • Timestamps sequential

Expected Results

  • 50,000 user profiles
  • ~750,000 events
  • 4 user segments (individual, small_team, enterprise, education)
  • 6 acquisition channels
  • ~13% paid conversion rate

Time: 5-10 minutes runtime

Phase 2: Metrics Framework (45 minutes)

Objective

Calculate North Star metric and all supporting KPIs.

Steps

  1. Define North Star Metric:

    • Rationale: Weekly Active Collaborative Workspaces
    • Combines engagement (weekly active) + network effects (collaboration)

  2. Calculate engagement metrics:

   python src/metrics_framework.py

  1. Review outputs:

    • North Star: ~2.1M collaborative workspaces
    • WAU: ~7M users
    • MAU: ~10M users
    • DAU/MAU ratio: ~35%
    • Activation rate: ~60%

  2. Document insights:

    • Stickiness benchmark (DAU/MAU > 30% is good for productivity tools)
    • Activation rate comparison (60% is above industry average)
    • Feature adoption rates

Key Metrics Calculated

  • Daily Active Users (DAU)
  • Weekly Active Users (WAU)
  • Monthly Active Users (MAU)
  • DAU/MAU Ratio (Stickiness)
  • Activation Rate
  • Feature Adoption Rates
  • Power User Segmentation

Time: 2-3 minutes runtime

Phase 3: Funnel Analysis (60 minutes)

Objective

Build and analyze 7-stage user acquisition funnel.

Steps

  1. Define funnel stages:

    • Awareness → Signup → Activation → Engagement → Habit → Collaboration → Monetization

  2. Run funnel analysis:

   python src/funnel_analysis.py

  1. Analyze conversions:

    • Stage-by-stage conversion rates
    • Identify biggest drop-offs
    • Segment-wise performance

  2. Compare segments:

    • Enterprise vs Individual users
    • Channel performance (referral vs paid ads)
    • Activation speed impact on retention

Key Findings

  • Overall funnel conversion: ~1.2% (signup to paid)
  • Biggest drop-off: Activation → Engagement (55% drop)
  • Enterprise users convert 6x better than individuals
  • Fast activators (< 24 hours) have 2x better retention

Deliverables

  • funnel_metrics.csv
  • segment_funnel.csv
  • Drop-off analysis report

Time: 30-45 seconds runtime

Phase 4: Cohort Analysis (60 minutes)

Objective

Analyze user retention by signup cohort over time.

Steps

  1. Create monthly cohorts:

    • Group users by signup month
    • Track activity in subsequent months

  2. Run cohort analysis:

   python src/cohort_analysis.py

  1. Calculate retention rates:

    • Day 1, 7, 14, 30, 60, 90 retention
    • Month-over-month retention curves
    • Cohort LTV estimates

  2. Compare early vs late cohorts:

    • Product improvements reflected in retention
    • Late cohorts show 8% better Month 1 retention

Key Insights

  • Month 1 retention: 45%
  • Month 3 retention: 29%
  • Month 6 retention: 19%
  • Power law: 10% of users drive 50% of activity

Deliverables

  • cohort_retention.csv
  • retention_matrix.csv (heatmap data)
  • LTV by cohort analysis

Time: 30-45 seconds runtime

Phase 5: Growth Modeling (90 minutes)

Objective

Identify and quantify 5 major growth opportunities.

Steps

  1. Define growth levers:

    • Template discovery improvement
    • Viral sharing optimization
    • SEO content strategy
    • Mobile experience enhancement
    • API/integrations expansion

  2. Model each lever:

   python src/growth_modeling.py

  1. Calculate impact:

    • Revenue projections
    • User acquisition estimates
    • Confidence levels

  2. Prioritize by ROI:

    • Rank by revenue impact × confidence
    • Create implementation timeline

  3. Project compound impact:

    • Select top 3 levers
    • Model 12-month projection
    • Calculate cumulative revenue

Key Results

  • #1 Lever: SEO Content → $12.5M annual revenue
  • #2 Lever: Viral Sharing → $7.8M annual revenue
  • #3 Lever: Templates → $6.2M annual revenue
  • Compound 12-month impact: $26.5M additional revenue

Deliverables

  • growth_levers.csv
  • growth_projections.csv
  • Sensitivity analysis charts

Time: 1-2 minutes runtime

Phase 6: SQL Queries (30 minutes)

Objective

Generate production-ready SQL queries for all key metrics.

Steps

  1. Generate query templates:
   python src/sql_queries.py

  1. Review queries for:

    • DAU/MAU metrics
    • Funnel analysis
    • Cohort retention
    • Power users
    • Feature adoption
    • Revenue metrics
    • North Star metric

  2. Customize for your database:

    • Adjust schema names
    • Optimize indexes
    • Add filters as needed

Deliverables

  • 7 SQL files in sql/ directory
  • Ready to run on PostgreSQL
  • Documented and commented

Time: < 1 second runtime

Phase 7: Visualizations (45 minutes)

Objective

Create beautiful, interactive dashboards.

Steps

  1. Generate all visualizations:
    • Included in main pipeline
    • Or run individually:
   python src/visualization.py

  1. Review outputs:

    • North Star metric gauge
    • Engagement trends
    • Funnel visualization
    • Cohort retention heatmap
    • Feature adoption charts
    • Growth levers bar chart
    • Executive dashboard

  2. Open dashboards:

   start outputs\dashboards\executive_dashboard.html

Deliverables

  • 8+ interactive HTML visualizations
  • Executive dashboard (comprehensive)
  • Print-ready PNG charts

Time: 10-15 seconds runtime

Phase 8: Final Report (30 minutes)

Objective

Generate executive summary and recommendations.

Steps

  1. Run complete pipeline:
   python scripts\run_full_analysis.py

  1. Review generated report:

    • Open outputs/reports/analytics_framework_report.txt

  2. Customize recommendations:

    • Add company-specific context
    • Adjust timelines
    • Include stakeholder notes

Report Sections

  • Executive Summary
  • North Star Metric Analysis
  • Key Metrics Snapshot
  • Funnel Analysis
  • Retention Insights
  • Growth Opportunities (ranked)
  • Strategic Recommendations
  • Next Steps

Time: Full pipeline ~3-5 minutes

Quality Checks

Data Quality

  • No missing user IDs
  • Timestamps sequential
  • Conversion rates realistic (1-20%)
  • Event distributions make sense

Analysis Quality

  • Metrics calculated correctly
  • Funnel stages flow logically
  • Retention curves show decay
  • Growth projections reasonable

Visualization Quality

  • All charts render correctly
  • Colors consistent with brand
  • Labels clear and readable
  • Interactive elements work

Documentation Quality

  • All code commented
  • README complete
  • SQL queries documented
  • Report clear and actionable

Total Timeline

Phase Time Cumulative
1. Data Generation 30 min 30 min
2. Metrics Framework 45 min 1h 15min
3. Funnel Analysis 60 min 2h 15min
4. Cohort Analysis 60 min 3h 15min
5. Growth Modeling 90 min 4h 45min
6. SQL Queries 30 min 5h 15min
7. Visualizations 45 min 6h
8. Final Report 30 min 6h 30min

Total: ~6.5 hours (spread over 2-3 days for thorough analysis)

Actual Runtime: ~5 minutes (for code execution)

Key Learnings

Technical Learnings

  1. Synthetic data generation at scale
  2. Complex funnel analysis with segments
  3. Cohort retention mathematics
  4. Growth modeling with confidence intervals
  5. SQL optimization for analytics

Product Learnings

  1. North Star metrics must combine engagement + value
  2. Activation is the most critical stage
  3. Collaboration drives retention and monetization
  4. Early product improvements compound over time
  5. Data-driven prioritization beats intuition

Communication Learnings

  1. Visualizations must tell a story
  2. Executive dashboards need context
  3. Recommendations must be actionable
  4. Quantify everything (revenue, users, time)
  5. Present ranges, not point estimates

Common Issues & Solutions

Issue: Data generation takes too long

  • Solution: Reduce n_users parameter to 10,000-25,000

Issue: Memory error during analysis

  • Solution: Process data in chunks, use chunksize parameter

Issue: Visualizations don't render

  • Solution: Install kaleido: pip install kaleido

Issue: SQL queries don't match your database

  • Solution: Adjust schema names in config.py

Next Steps

After completing this project:

  1. Adapt framework for your product
  2. Connect to real data sources
  3. Set up automated reporting
  4. Build real-time dashboards
  5. Implement top growth levers
  6. Track impact and iterate

Resources
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