/revenue-operations

name: "revenue-operations"
description: Analyzes sales pipeline health, revenue forecasting accuracy, and go-to-market efficiency metrics for SaaS revenue optimization. Use when analyzing sales pipeline coverage, forecasting revenue, evaluating go-to-market performance, reviewing sales metrics, assessing pipeline analysis, tracking forecast accuracy with MAPE, calculating GTM efficiency, or measuring sales efficiency and unit economics for SaaS teams.

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:**

• Pipeline coverage ratio