ElasticFlow
AI SkillAnalyze PipelineSalesby Alireza Rezvaniv1.0.0

When weekly pipeline review takes 2 hours, /revenue-operations runs coverage, aging, MAPE, and Magic Number in 10 minutes.

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Run pipeline review, forecast accuracy, and GTM efficiency analysis

  • Pipeline coverage ratio (target 3-4x quota), stage conversion rates, sales velocity, deal aging flags, concentration risk
  • Forecast MAPE tracking with bias detection (over/under-forecasting), weighted accuracy, period trends, category breakdowns by rep
  • GTM efficiency metrics: Magic Number (>0.75), LTV:CAC (>3:1), CAC Payback (<18mo), Burn Multiple (<2x), Rule of 40 (>40%), NDR (>110%)
  • Industry benchmarks per metric so you know if your number is good or bad — not just what it is
  • Built-in templates for pipeline review, forecast report, and GTM dashboard

Who this is for

RevOps

Run weekly pipeline review in 10 minutes with coverage ratios, aging flags, and concentration risk

See skills for this role
VP Sales

Defend the forecast to the board with MAPE accuracy trends and bias analysis

See skills for this role
Sales Manager

Spot deals stuck in stage and intervene before quarter-end

See skills for this role
Founder

Get GTM efficiency benchmarks (Magic Number, Rule of 40) for fundraising

See skills for this role

What it does

Weekly pipeline review takes 2 hours every Monday

Your weekly cadence eats half a morning between CRM exports, manual coverage math, and aging-deal hunts. /revenue-operations runs pipeline_analyzer on your export and surfaces coverage gaps, aging flags, and concentration risk in 10 minutes.

Board wants forecast accuracy trends, not just last quarter

Investors are asking how well you forecast. /revenue-operations tracks MAPE across 4 quarters with bias detection (are reps systematically over- or under-forecasting?) and category breakdowns by rep, segment, and product.

CFO asks if GTM spend is efficient before fundraising

You are 6 weeks from a Series B and the lead investor wants Magic Number and Rule of 40. /revenue-operations calculates LTV:CAC, CAC Payback, Burn Multiple, and Rule of 40 with industry benchmarks per stage — board-ready.

Quarter-end deal triage with the sales managers

Two weeks left in the quarter and 12 deals are stuck in Negotiation. /revenue-operations flags deals exceeding 2x average cycle time, surfaces stage conversion bottlenecks, and identifies which deals to push vs which to slip.

How it works

1

Export your pipeline + forecast + revenue data as JSON (deals, quota, stages, periods, ARR, costs)

2

Run pipeline_analyzer — get coverage ratio, conversion rates, deal aging flags, concentration warnings

3

Run forecast_accuracy_tracker — get MAPE per period, bias direction, accuracy rating per rep/segment

4

Run gtm_efficiency_calculator — get Magic Number, LTV:CAC, CAC Payback, Rule of 40 with benchmarks

5

Get a board-ready report: critical issues first, then trends, then strategic recommendations

Example

Your pipeline export (47 deals)
47 open deals, $2.1M pipeline
Quota: $1,800,000
Closed-to-date: $620,000 (34%)
Avg cycle days: 45
Stages: Discovery, Qualification, Proposal, Negotiation
10 minutes later
Coverage Snapshot
Quota:        $1,800,000
Closed:       $620,000 (34%)
Weighted:     $1,540,000 (86%)
Gap:          $260,000
Coverage:     2.4x  ⚠ below 3x target
Stage Velocity & Concentration
Discovery → Qual:    62% conversion (healthy)
Qual → Proposal:     45% conversion (healthy)
Proposal → Neg:      38% conversion ⚠ below 50% target
Neg → Won:           71% conversion (strong)

Concentration risk: 1 deal = 22% of pipeline (CloudFirst $140K)
Aging Deals (>2x avg cycle)
⚠ Apex Co       $160K  Negotiation, 91 days, no activity since Mar 12
⚠ Orion Inc      $75K  Discovery, 78 days, close date already missed
⚠ Pinnacle       $90K  Negotiation, 88 days, single-threaded
Gap Analysis
To hit quota: $260K more needed
→ Stage bottleneck: improve Proposal → Neg conversion (38% → 50%)
→ Revive Apex with new thread to CTO ($160K stuck)
→ Need $180K new pipeline at 2x coverage to backfill

Metrics this improves

Forecast Accuracy
<10% MAPE excellent
Sales
Pipeline Coverage
3-4x quota target
Sales
Deal Velocity
Stage bottlenecks surfaced
Sales
Pipeline Hygiene
Aging deals flagged
Sales
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Sorted by attribute overlap × differentiation. Revenue Operations shares 18+ attributes with each.
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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:

RatingMAPE RangeInterpretation
Excellent<10%Highly predictable, data-driven process
Good10-15%Reliable forecasting with minor variance
Fair15-25%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:

MetricFormulaTarget
Magic NumberNet New ARR / Prior Period S&M Spend>0.75
LTV:CAC(ARPA x Gross Margin / Churn Rate) / CAC>3:1
CAC PaybackCAC / (ARPA x Gross Margin) months<18 months
Burn MultipleNet Burn / Net New ARR<2x
Rule of 40Revenue 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 (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

ReferenceDescription
RevOps Metrics GuideComplete metrics hierarchy, definitions, formulas, and interpretation
Pipeline Management FrameworkPipeline best practices, stage definitions, conversion benchmarks
GTM Efficiency BenchmarksSaaS benchmarks by stage, industry standards, improvement strategies

Templates

TemplateUse Case
Pipeline Review TemplateWeekly/monthly pipeline inspection documentation
Forecast Report TemplateForecast accuracy reporting and trend analysis
GTM Dashboard TemplateGTM efficiency dashboard for leadership review
Sample Pipeline DataExample input for pipeline_analyzer.py
Expected OutputReference output from pipeline_analyzer.py