revenue-operations

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.

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

---

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:

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:

# 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:

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. Generate pipeline report:

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

2. 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?)

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

4. 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:

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

2. Analyze patterns:

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

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

4. 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:

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

2. 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

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

4. 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

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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