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).

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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

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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
  }
}

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Revenue Operations Workflows

Weekly Pipeline Review

Use this workflow for your weekly pipeline inspection cadence.

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

• Generate pipeline report:

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

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

• 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?)

• Document using template: Use assets/pipeline_review_template.md

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

Forecast Accuracy Review

Use monthly or quarterly to evaluate and improve forecasting discipline.

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

• Generate accuracy report:

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

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

• Analyze patterns:

- Is MAPE trending down (improving)?

- Which reps or segments have the highest error rates?

- Is there systematic over- or under-forecasting?

• Document using template: Use assets/forecast_report_template.md

• 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.

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

• Calculate efficiency metrics:

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

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

• Benchmark against targets:

- Magic Number (>0.75)

- LTV:CAC (>3:1)

- CAC Payback (<18 months)

- Rule of 40 (>40%)

• Document using template: Use assets/gtm_dashboard_template.md

• Strategic decisions: Adjust spend allocation, optimize channels, improve retention

Quarterly Business Review

Combine all three tools for a comprehensive QBR analysis.

• Run pipeline analyzer for forward-looking coverage
• Run forecast tracker for backward-looking accuracy
• Run GTM calculator for efficiency benchmarks
• Cross-reference pipeline health with forecast accuracy
• Align GTM efficiency metrics with growth targets

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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 |

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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 |