Revenue Operations

What This Skill Does

This skill provides pipeline analysis, forecast accuracy tracking, and GTM efficiency measurement for SaaS revenue teams. It helps revenue operations teams analyze sales performance and identify areas for improvement. It delivers insights via command line tools with text and JSON output.

When to Use

Analyze pipeline healthTrack forecast accuracyMeasure GTM efficiencyIdentify coverage gapsAssess deal agingCalculate sales velocity

Key Features

Analyzes pipeline coverage ratiosTracks stage conversion ratesCalculates sales velocityFlags deal agingIdentifies concentration riskSupports text and JSON output

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:

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:

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.

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 R