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[Pipeline Analyzer](#1-pipeline-analyzer)\n  - [Forecast Accuracy Tracker](#2-forecast-accuracy-tracker)\n  - [GTM Efficiency Calculator](#3-gtm-efficiency-calculator)\n- [Revenue Operations Workflows](#revenue-operations-workflows)\n  - [Weekly Pipeline Review](#weekly-pipeline-review)\n  - [Forecast Accuracy Review](#forecast-accuracy-review)\n  - [GTM Efficiency Audit](#gtm-efficiency-audit)\n  - [Quarterly Business Review](#quarterly-business-review)\n- [Reference Documentation](#reference-documentation)\n- [Templates](#templates)\n\n---\n\n## Quick Start\n\n```bash\n# Analyze pipeline health and coverage\npython scripts/pipeline_analyzer.py --input assets/sample_pipeline_data.json --format text\n\n# Track forecast accuracy over multiple periods\npython scripts/forecast_accuracy_tracker.py assets/sample_forecast_data.json --format text\n\n# Calculate GTM efficiency metrics\npython scripts/gtm_efficiency_calculator.py assets/sample_gtm_data.json --format text\n```\n\n---\n\n## Tools Overview\n\n### 1. Pipeline Analyzer\n\nAnalyzes sales pipeline health including coverage ratios, stage conversion rates, deal velocity, aging risks, and concentration risks.\n\n**Input:** JSON file with deals, quota, and stage configuration\n**Output:** Coverage ratios, conversion rates, velocity metrics, aging flags, risk assessment\n\n**Usage:**\n\n```bash\n# Text report (human-readable)\npython scripts/pipeline_analyzer.py --input pipeline.json --format text\n\n# JSON output (for dashboards/integrations)\npython scripts/pipeline_analyzer.py --input pipeline.json --format json\n```\n\n**Key Metrics Calculated:**\n- **Pipeline Coverage Ratio** -- Total pipeline value / quota target (healthy: 3-4x)\n- **Stage Conversion Rates** -- Stage-to-stage progression rates\n- **Sales Velocity** -- (Opportunities x Avg Deal Size x Win Rate) / Avg Sales Cycle\n- **Deal Aging** -- Flags deals exceeding 2x average cycle time per stage\n- **Concentration Risk** -- Warns when >40% of pipeline is in a single deal\n- **Coverage Gap Analysis** -- Identifies quarters with insufficient pipeline\n\n**Input Schema:**\n\n```json\n{\n  \"quota\": 500000,\n  \"stages\": [\"Discovery\", \"Qualification\", \"Proposal\", \"Negotiation\", \"Closed Won\"],\n  \"average_cycle_days\": 45,\n  \"deals\": [\n    {\n      \"id\": \"D001\",\n      \"name\": \"Acme Corp\",\n      \"stage\": \"Proposal\",\n      \"value\": 85000,\n      \"age_days\": 32,\n      \"close_date\": \"2025-03-15\",\n      \"owner\": \"rep_1\"\n    }\n  ]\n}\n```\n\n### 2. Forecast Accuracy Tracker\n\nTracks forecast accuracy over time using MAPE, detects systematic bias, analyzes trends, and provides category-level breakdowns.\n\n**Input:** JSON file with forecast periods and optional category breakdowns\n**Output:** MAPE score, bias analysis, trends, category breakdown, accuracy rating\n\n**Usage:**\n\n```bash\n# Track forecast accuracy\npython scripts/forecast_accuracy_tracker.py forecast_data.json --format text\n\n# JSON output for trend analysis\npython scripts/forecast_accuracy_tracker.py forecast_data.json --format json\n```\n\n**Key Metrics Calculated:**\n- **MAPE** -- Mean Absolute Percentage Error: mean(|actual - forecast| / |actual|) x 100\n- **Forecast Bias** -- Over-forecasting (positive) vs under-forecasting (negative) tendency\n- **Weighted Accuracy** -- MAPE weighted by deal value for materiality\n- **Period Trends** -- Improving, stable, or declining accuracy over time\n- **Category Breakdown** -- Accuracy by rep, product, segment, or any custom dimension\n\n**Accuracy Ratings:**\n| Rating | MAPE Range | Interpretation |\n|--------|-----------|----------------|\n| Excellent | <10% | Highly predictable, data-driven process |\n| Good | 10-15% | Reliable forecasting with minor variance |\n| Fair | 15-25% | Needs process improvement |\n| Poor | >25% | Significant forecasting methodology gaps |\n\n**Input Schema:**\n\n```json\n{\n  \"forecast_periods\": [\n    {\"period\": \"2025-Q1\", \"forecast\": 480000, \"actual\": 520000},\n    {\"period\": \"2025-Q2\", \"forecast\": 550000, \"actual\": 510000}\n  ],\n  \"category_breakdowns\": {\n    \"by_rep\": [\n      {\"category\": \"Rep A\", \"forecast\": 200000, \"actual\": 210000},\n      {\"category\": \"Rep B\", \"forecast\": 280000, \"actual\": 310000}\n    ]\n  }\n}\n```\n\n### 3. GTM Efficiency Calculator\n\nCalculates core SaaS GTM efficiency metrics with industry benchmarking, ratings, and improvement recommendations.\n\n**Input:** JSON file with revenue, cost, and customer metrics\n**Output:** Magic Number, LTV:CAC, CAC Payback, Burn Multiple, Rule of 40, NDR with ratings\n\n**Usage:**\n\n```bash\n# Calculate all GTM efficiency metrics\npython scripts/gtm_efficiency_calculator.py gtm_data.json --format text\n\n# JSON output for dashboards\npython scripts/gtm_efficiency_calculator.py gtm_data.json --format json\n```\n\n**Key Metrics Calculated:**\n\n| Metric | Formula | Target |\n|--------|---------|--------|\n| Magic Number | Net New ARR / Prior Period S&M Spend | >0.75 |\n| LTV:CAC | (ARPA x Gross Margin / Churn Rate) / CAC | >3:1 |\n| CAC Payback | CAC / (ARPA x Gross Margin) months | <18 months |\n| Burn Multiple | Net Burn / Net New ARR | <2x |\n| Rule of 40 | Revenue Growth % + FCF Margin % | >40% |\n| Net Dollar Retention | (Begin ARR + Expansion - Contraction - Churn) / Begin ARR | >110% |\n\n**Input Schema:**\n\n```json\n{\n  \"revenue\": {\n    \"current_arr\": 5000000,\n    \"prior_arr\": 3800000,\n    \"net_new_arr\": 1200000,\n    \"arpa_monthly\": 2500,\n    \"revenue_growth_pct\": 31.6\n  },\n  \"costs\": {\n    \"sales_marketing_spend\": 1800000,\n    \"cac\": 18000,\n    \"gross_margin_pct\": 78,\n    \"total_operating_expense\": 6500000,\n    \"net_burn\": 1500000,\n    \"fcf_margin_pct\": 8.4\n  },\n  \"customers\": {\n    \"beginning_arr\": 3800000,\n    \"expansion_arr\": 600000,\n    \"contraction_arr\": 100000,\n    \"churned_arr\": 300000,\n    \"annual_churn_rate_pct\": 8\n  }\n}\n```\n\n---\n\n## Revenue Operations Workflows\n\n### Weekly Pipeline Review\n\nUse this workflow for your weekly pipeline inspection cadence.\n\n1. **Generate pipeline report:**\n   ```bash\n   python scripts/pipeline_analyzer.py --input current_pipeline.json --format text\n   ```\n\n2. **Review key indicators:**\n   - Pipeline coverage ratio (is it above 3x quota?)\n   - Deals aging beyond threshold (which deals need intervention?)\n   - Concentration risk (are we over-reliant on a few large deals?)\n   - Stage distribution (is there a healthy funnel shape?)\n\n3. **Document using template:** Use `assets/pipeline_review_template.md`\n\n4. **Action items:** Address aging deals, redistribute pipeline concentration, fill coverage gaps\n\n### Forecast Accuracy Review\n\nUse monthly or quarterly to evaluate and improve forecasting discipline.\n\n1. **Generate accuracy report:**\n   ```bash\n   python scripts/forecast_accuracy_tracker.py forecast_history.json --format text\n   ```\n\n2. **Analyze patterns:**\n   - Is MAPE trending down (improving)?\n   - Which reps or segments have the highest error rates?\n   - Is there systematic over- or under-forecasting?\n\n3. **Document using template:** Use `assets/forecast_report_template.md`\n\n4. **Improvement actions:** Coach high-bias reps, adjust methodology, improve data hygiene\n\n### GTM Efficiency Audit\n\nUse quarterly or during board prep to evaluate go-to-market efficiency.\n\n1. **Calculate efficiency metrics:**\n   ```bash\n   python scripts/gtm_efficiency_calculator.py quarterly_data.json --format text\n   ```\n\n2. **Benchmark against targets:**\n   - Magic Number signals GTM spend efficiency\n   - LTV:CAC validates unit economics\n   - CAC Payback shows capital efficiency\n   - Rule of 40 balances growth and profitability\n\n3. **Document using template:** Use `assets/gtm_dashboard_template.md`\n\n4. **Strategic decisions:** Adjust spend allocation, optimize channels, improve retention\n\n### Quarterly Business Review\n\nCombine all three tools for a comprehensive QBR analysis.\n\n1. Run pipeline analyzer for forward-looking coverage\n2. Run forecast tracker for backward-looking accuracy\n3. Run GTM calculator for efficiency benchmarks\n4. Cross-reference pipeline health with forecast accuracy\n5. Align GTM efficiency metrics with growth targets\n\n---\n\n## Reference Documentation\n\n| Reference | Description |\n|-----------|-------------|\n| [RevOps Metrics Guide](references/revops-metrics-guide.md) | Complete metrics hierarchy, definitions, formulas, and interpretation |\n| [Pipeline Management Framework](references/pipeline-management-framework.md) | Pipeline best practices, stage definitions, conversion benchmarks |\n| [GTM Efficiency Benchmarks](references/gtm-efficiency-benchmarks.md) | SaaS benchmarks by stage, industry standards, improvement strategies |\n\n---\n\n## Templates\n\n| Template | Use Case |\n|----------|----------|\n| [Pipeline Review Template](assets/pipeline_review_template.md) | Weekly/monthly pipeline inspection documentation |\n| [Forecast Report Template](assets/forecast_report_template.md) | Forecast accuracy reporting and trend analysis |\n| [GTM Dashboard Template](assets/gtm_dashboard_template.md) | GTM efficiency dashboard for leadership review |\n| [Sample Pipeline Data](assets/sample_pipeline_data.json) | Example input for pipeline_analyzer.py |\n| [Expected Output](assets/expected_output.json) | Reference output from pipeline_analyzer.py |\n","readmeExcerpt":"--- name: revenue-operations description: Analyzes pipeline coverage, tracks forecast accuracy with MAPE, and calculates GTM efficiency metrics for SaaS revenue optimization --- Revenue Operations Pipeline analysis, forecast accuracy tracking, and GTM efficiency measurement for SaaS revenue teams. 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