Revenue Operations

Input: JSON file with deals, quota, and stage configuration Output: Coverage ratios, conversion rates, velocity metrics, aging flags, risk assessment

Usage:

python scripts/pipeline_analyzer.py --input pipeline.json --format text

Key Metrics Calculated:

• Pipeline Coverage Ratio -- Total pipeline value / quota target (healthy: 3-4x)
• Stage Conversion Rates -- Stage-to-stage progression rates
• Sales Velocity -- (Opportunities x Avg Deal Size x Win Rate) / Avg Sales Cycle
• Deal Aging -- Flags deals exceeding 2x average cycle time per stage
• Concentration Risk -- Warns when >40% of pipeline is in a single deal
• Coverage Gap Analysis -- Identifies quarters with insufficient pipeline

Input Schema:

{
  "quota": 500000,
  "stages": ["Discovery", "Qualification", "Proposal", "Negotiation", "Closed Won"],
  "average_cycle_days": 45,
  "deals": [
    {
      "id": "D001",
      "name": "Acme Corp",
      "stage": "Proposal",
      "value": 85000,
      "age_days": 32,
      "close_date": "2025-03-15",
      "owner": "rep_1"
    }
  ]
}

2. Forecast Accuracy Tracker

Tracks forecast accuracy over time using MAPE, detects systematic bias, analyzes trends, and provides category-level breakdowns.

Input: JSON file with forecast periods and optional category breakdowns Output: MAPE score, bias analysis, trends, category breakdown, accuracy rating

Usage:

python scripts/forecast_accuracy_tracker.py forecast_data.json --format text

Key Metrics Calculated:

• MAPE -- mean(|actual - forecast| / |actual|) x 100
• Forecast Bias -- Over-forecasting (positive) vs under-forecasting (negative) tendency
• Weighted Accuracy -- MAPE weighted by deal value for materiality
• Period Trends -- Improving, stable, or declining accuracy over time
• Category Breakdown -- Accuracy by rep, product, segment, or any custom dimension

Accuracy Ratings:

Input Schema:

{
  "forecast_periods": [
    {"period": "2025-Q1", "forecast": 480000, "actual": 520000},
    {"period": "2025-Q2", "forecast": 550000, "actual": 510000}
  ],
  "category_breakdowns": {
    "by_rep": [
      {"category": "Rep A", "forecast": 200000, "actual": 210000},
      {"category": "Rep B", "forecast": 280000, "actual": 310000}
    ]
  }
}

3. GTM Efficiency Calculator

Calculates core SaaS GTM efficiency metrics with industry benchmarking, ratings, and improvement recommendations.

Input: JSON file with revenue, cost, and customer metrics Output: Magic Number, LTV:CAC, CAC Payback, Burn Multiple, Rule of 40, NDR with ratings

Usage:

python scripts/gtm_efficiency_calculator.py gtm_data.json --format text

Key Metrics Calculated:

Input Schema:

{
  "revenue": {
    "current_arr": 5000000,
    "prior_arr": 3800000,
    "net_new_arr": 1200000,
    "arpa_monthly": 2500,
    "revenue_growth_pct": 31.6
  },
  "costs": {
    "sales_marketing_spend": 1800000,
    "cac": 18000,
    "gross_margin_pct": 78,
    "total_operating_expense": 6500000,
    "net_burn": 1500000,
    "fcf_margin_pct": 8.4
  },
  "customers": {
    "beginning_arr": 3800000,
    "expansion_arr": 600000,
    "contraction_arr": 100000,
    "churned_arr": 300000,
    "annual_churn_rate_pct": 8
  }
}

Revenue Operations Workflows

Weekly Pipeline Review

Use this workflow for your weekly pipeline inspection cadence.

1. Verify input data: Confirm pipeline export is current and all required fields (stage, value, close_date, owner) are populated before proceeding.

2. Generate pipeline report:

   python scripts/pipeline_analyzer.py --input current_pipeline.json --format text
   

3. Cross-check output totals against your CRM source system to confirm data integrity.

4. Review key indicators:

   • Pipeline coverage ratio (is it above 3x quota?)
   • Deals aging beyond threshold (which deals need intervention?)
   • Concentration risk (are we over-reliant on a few large deals?)
   • Stage distribution (is there a healthy funnel shape?)

5. Document using template: Use assets/pipeline_review_template.md

6. Action items: Address aging deals, redistribute pipeline concentration, fill coverage gaps

Forecast Accuracy Review

Use monthly or quarterly to evaluate and improve forecasting discipline.

1. Verify input data: Confirm all forecast periods have corresponding actuals and no periods are missing before running.

2. Generate accuracy report:

   python scripts/forecast_accuracy_tracker.py forecast_history.json --format text
   

3. Cross-check actuals against closed-won records in your CRM before drawing conclusions.

4. Analyze patterns:

   • Is MAPE trending down (improving)?
   • Which reps or segments have the highest error rates?
   • Is there systematic over- or under-forecasting?

5. Document using template: Use assets/forecast_report_template.md

6. Improvement actions: Coach high-bias reps, adjust methodology, improve data hygiene

GTM Efficiency Audit

Use quarterly or during board prep to evaluate go-to-market efficiency.

1. Verify input data: Confirm revenue, cost, and customer figures reconcile with finance records before running.

2. Calculate efficiency metrics:

   python scripts/gtm_efficiency_calculator.py quarterly_data.json --format text
   

3. Cross-check computed ARR and spend totals against your finance system before sharing results.

4. Benchmark against targets:

   • Magic Number (>0.75)
   • LTV:CAC (>3:1)
   • CAC Payback (<18 months)
   • Rule of 40 (>40%)

5. Document using template: Use assets/gtm_dashboard_template.md

6. Strategic decisions: Adjust spend allocation, optimize channels, improve retention

Quarterly Business Review

Combine all three tools for a comprehensive QBR analysis.

1. Run pipeline analyzer for forward-looking coverage
2. Run forecast tracker for backward-looking accuracy
3. Run GTM calculator for efficiency benchmarks
4. Cross-reference pipeline health with forecast accuracy
5. Align GTM efficiency metrics with growth targets

Reference Documentation

Templates