revenue-operations

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

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:

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:

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:

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:

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:

Key Metrics Calculated:

Input Schema:

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 (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?)

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

6. 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. Verify input data: Confirm all forecast periods have corresponding actuals and no periods are missing before running.

2. Generate accuracy report:

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

3. Cross-check actuals against closed-won records in your CRM before drawing conclusions.

4. Analyze patterns:

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

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

6. 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. Verify input data: Confirm revenue, cost, and customer figures reconcile with finance records before running.

2. Calculate efficiency metrics:

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

3. Cross-check computed ARR and spend totals against your finance system before sharing results.

4. Benchmark against targets:

   • Magic Number (>0.75)
   • LTV:CAC (>3:1)
   • CAC Payback (<18 months)
   • Rule of 40 (>40%)

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

6. 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