Revenue Operations

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

Quick Start
Tools Overview
Revenue Operations Workflows
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
  }
}