Skill: alirezarezvani/revenue-operations (version 1.0.0)

--- 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 ```bash # 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:** ```bash 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:** ```json { "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:** ```bash 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:** ```json { "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:** ```bash 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:** ```json { "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:** ```bash 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:** ```bash 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:** ```bash 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 | Reference | Description | |-----------|-------------| | [RevOps Metrics Guide](references/revops-metrics-guide.md) | Complete metrics hierarchy, definitions, formulas, and interpretation | | [Pipeline Management Framework](references/pipeline-management-framework.md) | Pipeline best practices, stage definitions, conversion benchmarks | | [GTM Efficiency Benchmarks](references/gtm-efficiency-benchmarks.md) | SaaS benchmarks by stage, industry standards, improvement strategies | --- ## Templates | Template | Use Case | |----------|----------| | [Pipeline Review Template](assets/pipeline_review_template.md) | Weekly/monthly pipeline inspection documentation | | [Forecast Report Template](assets/forecast_report_template.md) | Forecast accuracy reporting and trend analysis | | [GTM Dashboard Template](assets/gtm_dashboard_template.md) | GTM efficiency dashboard for leadership review | | [Sample Pipeline Data](assets/sample_pipeline_data.json) | Example input for pipeline_analyzer.py | | [Expected Output](assets/expected_output.json) | Reference output from pipeline_analyzer.py |


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