borghei Revenue Operations
Pipeline analysis, forecast accuracy tracking, and GTM efficiency measurement for SaaS revenue teams.
Table of Contents
Clarify First
Before running the analysis, confirm these inputs. If any is unknown or vague, ASK — do not assume:
- Which analysis — pipeline health, forecast accuracy, or GTM efficiency (selects the script and its input schema)
- Quota / target — the number pipeline coverage and Magic Number are measured against
- Data export readiness — deals with stage/value/age/close-date, or forecast-vs-actual periods (the tools consume specific JSON; forecast trend needs 3+ periods)
- Company stage + sales motion — seed vs growth, PLG vs enterprise (benchmarks vary widely by stage and motion)
Stop rule: ask only the 2-3 that most change the output. If the user says "just draft it," proceed and list your assumptions at the top of the output.
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.
Generate pipeline report:
python scripts/pipeline_analyzer.py --input current_pipeline.json --format textReview 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.mdAction items: Address aging deals, redistribute pipeline concentration, fill coverage gaps
Forecast Accuracy Review
Use monthly or quarterly to evaluate and improve forecasting discipline.
Generate accuracy report:
python scripts/forecast_accuracy_tracker.py forecast_history.json --format textAnalyze 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.mdImprovement 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.
Calculate efficiency metrics:
python scripts/gtm_efficiency_calculator.py quarterly_data.json --format textBenchmark 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
Document using template: Use
assets/gtm_dashboard_template.mdStrategic 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
- Run GTM calculator for efficiency benchmarks
- Cross-reference pipeline health with forecast accuracy
- Align GTM efficiency metrics with growth targets
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 |
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 |
Tool Reference
1. pipeline_analyzer.py
Analyzes sales pipeline health including coverage ratios, stage conversion rates, sales velocity, deal aging risks, and concentration risks.
python scripts/pipeline_analyzer.py --input pipeline.json --format text
python scripts/pipeline_analyzer.py --input pipeline.json --format json
| Flag | Type | Description |
|---|---|---|
--input |
required | Path to JSON file with deals, quota, and stage configuration |
--format |
optional | Output format: text (default) or json |
2. forecast_accuracy_tracker.py
Tracks forecast accuracy over time using MAPE, detects systematic bias, analyzes trends, and provides category-level breakdowns.
python scripts/forecast_accuracy_tracker.py forecast_data.json --format text
python scripts/forecast_accuracy_tracker.py forecast_data.json --format json
| Flag | Type | Description |
|---|---|---|
forecast_data.json |
positional | Path to JSON file with forecast periods and optional category breakdowns |
--format |
optional | Output format: text (default) or json |
3. gtm_efficiency_calculator.py
Calculates core SaaS GTM efficiency metrics with industry benchmarking, ratings, and improvement recommendations.
python scripts/gtm_efficiency_calculator.py gtm_data.json --format text
python scripts/gtm_efficiency_calculator.py gtm_data.json --format json
| Flag | Type | Description |
|---|---|---|
gtm_data.json |
positional | Path to JSON file with revenue, cost, and customer metrics |
--format |
optional | Output format: text (default) or json |
Troubleshooting
| Problem | Likely Cause | Resolution |
|---|---|---|
| Pipeline coverage below 3x quota | Insufficient top-of-funnel activity or poor lead-to-opportunity conversion | Audit lead sources and conversion rates by stage; increase outbound activity or marketing spend in underperforming channels |
| Forecast MAPE above 25% | Inconsistent deal stage criteria, sandbagging, or lack of inspection rigor | Standardize stage exit criteria; implement weekly pipeline reviews tied to velocity not just activity; coach high-bias reps individually |
| Magic Number below 0.5 | GTM spend is inefficient relative to new ARR generated | Review channel ROI; reduce spend in low-performing channels; improve rep productivity before adding headcount |
| LTV:CAC below 3:1 | CAC too high or churn eroding lifetime value | Address churn first (use churn-prevention skill); then optimize CAC by shifting to lower-cost acquisition channels |
| Deals slipping past forecast close date | Lack of deal qualification, missing champion, or no compelling event | Implement MEDDIC/BANT qualification; require compelling event documentation for commit-stage deals |
| Pipeline heavily concentrated in early stages | Poor stage progression indicating stalled deals or loose qualification | Set maximum stage age limits; implement automated alerts for deals exceeding 2x average cycle per stage |
| Net Dollar Retention below 100% | Contraction and churn outpacing expansion revenue | Prioritize expansion playbooks for healthy accounts; conduct exit interviews for churning accounts; review pricing tier structure |
Success Criteria
- Pipeline coverage ratio stabilizes at 3-4x quota with healthy stage distribution
- Forecast MAPE improves to below 15% (Good) or below 10% (Excellent) within two quarters
- Magic Number exceeds 0.75 indicating efficient GTM spend
- LTV:CAC ratio exceeds 3:1 with CAC payback under 18 months
- Rule of 40 score exceeds 40% (revenue growth % + FCF margin %)
- Net Dollar Retention exceeds 110% driven by expansion revenue
- Deal slippage rate drops below 30% (improved from 2024 industry average of 44%)
Scope & Limitations
In scope: Pipeline health analysis (coverage, velocity, aging, concentration), forecast accuracy measurement (MAPE, bias, trends, category breakdowns), GTM efficiency metrics (Magic Number, LTV:CAC, CAC Payback, Burn Multiple, Rule of 40, NDR), weekly/monthly/quarterly review workflows, and QBR preparation combining all three analysis dimensions.
Out of scope: CRM system administration or data extraction (tools consume JSON exports), deal-level sales coaching (tools flag deals but do not prescribe sales tactics), marketing attribution modeling, customer success health scoring (use customer-success-manager skill), and real-time pipeline monitoring. Tools analyze point-in-time snapshots; continuous monitoring requires integration with CRM/BI platforms.
Limitations: Benchmarks are based on aggregate SaaS industry data and vary by company stage (seed, Series A-C, growth, public), vertical, and sales motion (PLG vs enterprise). Pipeline analysis assumes deal data includes accurate stage, value, age, and close date fields. Forecast accuracy requires minimum 3 periods for trend analysis. GTM metrics require accurate financial data that may not be available in early-stage companies.
Integration Points
- sales-engineer -- Pipeline deals requiring technical validation route through sales-engineer POC and RFP workflows
- customer-success-manager -- Post-close handoff; NDR metrics depend on customer success health scoring and expansion plays
- pricing-strategy -- Pricing model impacts pipeline velocity, deal sizes, and conversion rates; pricing changes require pipeline reforecasting
- churn-prevention -- Churn rate directly impacts LTV:CAC and NDR metrics; reducing churn improves all GTM efficiency measures
- c-level-advisor -- GTM efficiency metrics feed directly into board-level reporting and strategic resource allocation decisions