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Revenue Operations

Domain: Business & Growth | Skill: revenue-operations | Source: business-growth/revenue-operations/SKILL.md

Revenue Operations

Pipeline analysis, forecast accuracy tracking, and GTM efficiency measurement for SaaS revenue teams.

Output formats: All scripts support --format text (human-readable) and --format json (dashboards/integrations).

Quick Start

# Analyze pipeline health and coverage
python scripts/pipeline_analyzer.py --input assets/sample_pipeline_data.json --format text

# Track forecast accuracy over multiple periods
python scripts/forecast_accuracy_tracker.py assets/sample_forecast_data.json --format text

# Calculate GTM efficiency metrics
python scripts/gtm_efficiency_calculator.py assets/sample_gtm_data.json --format text

Tools Overview

1. Pipeline Analyzer

Analyzes sales pipeline health including coverage ratios, stage conversion rates, deal velocity, aging risks, and concentration risks.

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: | Rating | MAPE Range | Interpretation | |--------|-----------|----------------| | Excellent | <10% | Highly predictable, data-driven process | | Good | 10-15% | Reliable forecasting with minor variance | | Fair | 15-25% | Needs process improvement | | Poor | >25% | Significant forecasting methodology gaps |

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:

Metric Formula Target
Magic Number Net New ARR / Prior Period S&M Spend >0.75
LTV:CAC (ARPA x Gross Margin / Churn Rate) / CAC >3:1
CAC Payback CAC / (ARPA x Gross Margin) months <18 months
Burn Multiple Net Burn / Net New ARR <2x
Rule of 40 Revenue Growth % + FCF Margin % >40%
Net Dollar Retention (Begin ARR + Expansion - Contraction - Churn) / Begin ARR >110%

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:

  5. Pipeline coverage ratio (is it above 3x quota?)
  6. Deals aging beyond threshold (which deals need intervention?)
  7. Concentration risk (are we over-reliant on a few large deals?)

  8. Stage distribution (is there a healthy funnel shape?)

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

  10. 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:

  5. Is MAPE trending down (improving)?
  6. Which reps or segments have the highest error rates?

  7. Is there systematic over- or under-forecasting?

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

  9. 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:

  5. Magic Number (>0.75)
  6. LTV:CAC (>3:1)
  7. CAC Payback (<18 months)

  8. Rule of 40 (>40%)

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

  10. 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

Reference Description
RevOps Metrics Guide Complete metrics hierarchy, definitions, formulas, and interpretation
Pipeline Management Framework Pipeline best practices, stage definitions, conversion benchmarks
GTM Efficiency Benchmarks SaaS benchmarks by stage, industry standards, improvement strategies

Templates

Template Use Case
Pipeline Review Template Weekly/monthly pipeline inspection documentation
Forecast Report Template Forecast accuracy reporting and trend analysis
GTM Dashboard Template GTM efficiency dashboard for leadership review
Sample Pipeline Data Example input for pipeline_analyzer.py
Expected Output Reference output from pipeline_analyzer.py