version: "1.0.1" name: revenue-operations description: > Analyzes pipeline coverage, tracks forecast accuracy with MAPE, and calculates GTM efficiency metrics for SaaS revenue optimization license: MIT + Commons Clause metadata: version: 1.0.0 author: borghei category: business-growth domain: revenue-ops updated: 2026-03-31 tags: [revops, pipeline, forecast, gtm-efficiency, saas-metrics]

Revenue Operations

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

Table of Contents

• Quick Start
• Tools Overview
• Pipeline Analyzer
• Forecast Accuracy Tracker
• GTM Efficiency Calculator
• Revenue Operations Workflows
• Weekly Pipeline Review
• Forecast Accuracy Review
• GTM Efficiency Audit
• Quarterly Business Review
• Reference Documentation
• Templates

Clarify First

Before running the analysis, confirm these inputs. If any is unknown or vague, ASK — do not assume:

• [ ] Which analysis — pipeline health, forecast accuracy, or GTM efficiency (selects the script and its input schema)
• [ ] Quota / target — the number pipeline coverage and Magic Number are measured against
• [ ] Data export readiness — deals with stage/value/age/close-date, or forecast-vs-actual periods (the tools consume specific JSON; forecast trend needs 3+ periods)
• [ ] Company stage + sales motion — seed vs growth, PLG vs enterprise (benchmarks vary widely by stage and motion)

Stop rule: ask only the 2-3 that most change the output. If the user says "just draft it," proceed and list your assumptions at the top of the output.

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:

# Text report (human-readable)
python scripts/pipeline_analyzer.py --input pipeline.json --format text
 
# JSON output (for dashboards/integrations)
python scripts/pipeline_analyzer.py --input pipeline.json --format json

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:

# Track forecast accuracy
python scripts/forecast_accuracy_tracker.py forecast_data.json --format text
 
# JSON output for trend analysis
python scripts/forecast_accuracy_tracker.py forecast_data.json --format json

Key Metrics Calculated:

• MAPE -- Mean Absolute Percentage Error: 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:

# Calculate all GTM efficiency metrics
python scripts/gtm_efficiency_calculator.py gtm_data.json --format text
 
# JSON output for dashboards
python scripts/gtm_efficiency_calculator.py gtm_data.json --format json

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. Generate pipeline report:

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

1. 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?)

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

1. 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. Generate accuracy report:

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

1. Analyze patterns:

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

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

1. 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. Calculate efficiency metrics:

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

1. Benchmark against targets:

• Magic Number signals GTM spend efficiency
• LTV:CAC validates unit economics
• CAC Payback shows capital efficiency
• Rule of 40 balances growth and profitability

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

1. 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

Tool Reference

1. pipeline_analyzer.py

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

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

2. forecast_accuracy_tracker.py

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

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

3. gtm_efficiency_calculator.py

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

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

Troubleshooting

Success Criteria

• Pipeline coverage ratio stabilizes at 3-4x quota with healthy stage distribution
• Forecast MAPE improves to below 15% (Good) or below 10% (Excellent) within two quarters
• Magic Number exceeds 0.75 indicating efficient GTM spend
• LTV:CAC ratio exceeds 3:1 with CAC payback under 18 months
• Rule of 40 score exceeds 40% (revenue growth % + FCF margin %)
• Net Dollar Retention exceeds 110% driven by expansion revenue
• Deal slippage rate drops below 30% (improved from 2024 industry average of 44%)

Scope & Limitations

In scope: Pipeline health analysis (coverage, velocity, aging, concentration), forecast accuracy measurement (MAPE, bias, trends, category breakdowns), GTM efficiency metrics (Magic Number, LTV:CAC, CAC Payback, Burn Multiple, Rule of 40, NDR), weekly/monthly/quarterly review workflows, and QBR preparation combining all three analysis dimensions.

Out of scope: CRM system administration or data extraction (tools consume JSON exports), deal-level sales coaching (tools flag deals but do not prescribe sales tactics), marketing attribution modeling, customer success health scoring (use customer-success-manager skill), and real-time pipeline monitoring. Tools analyze point-in-time snapshots; continuous monitoring requires integration with CRM/BI platforms.

Limitations: Benchmarks are based on aggregate SaaS industry data and vary by company stage (seed, Series A-C, growth, public), vertical, and sales motion (PLG vs enterprise). Pipeline analysis assumes deal data includes accurate stage, value, age, and close date fields. Forecast accuracy requires minimum 3 periods for trend analysis. GTM metrics require accurate financial data that may not be available in early-stage companies.

Integration Points

• sales-engineer -- Pipeline deals requiring technical validation route through sales-engineer POC and RFP workflows
• customer-success-manager -- Post-close handoff; NDR metrics depend on customer success health scoring and expansion plays
• pricing-strategy -- Pricing model impacts pipeline velocity, deal sizes, and conversion rates; pricing changes require pipeline reforecasting
• churn-prevention -- Churn rate directly impacts LTV:CAC and NDR metrics; reducing churn improves all GTM efficiency measures
• c-level-advisor -- GTM efficiency metrics feed directly into board-level reporting and strategic resource allocation decisions