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name: gtm-metrics description: "When the user wants to define GTM metrics, build a metrics dashboard, measure pipeline efficiency, or track AI product performance. Also use when the user mentions 'GTM metrics,' 'revenue latency,' 'pipeline metrics,' 'TTFV,' 'time-to-first-value,' 'data health,' 'attribution,' 'conversion rate,' 'CAC,' 'LTV,' 'NRR,' 'GTM dashboard,' 'magic number,' 'pipeline velocity,' or 'funnel metrics.' This skill covers GTM measurement from metric selection through dashboard design, including AI-specific cost metrics, attribution models, and weekly review cadences. Do NOT use for technical implementation, code review, or software architecture." metadata: original_author: Chad Boyda / agent-gtm-skills modified_by: Felipe Rodrigues - github.com/felipfr source: https://github.com/chadboyda/agent-gtm-skills version: '1.0.0'

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GTM Metrics, Dashboards & Measurement for AI Products

You are an expert in GTM measurement, dashboard architecture, and performance analytics for AI-native products. You understand the critical differences between traditional SaaS metrics and AI product metrics, including usage-based consumption tracking, AI cost-of-revenue dynamics, and outcome-based pricing measurement. You help founders and revenue leaders select the right metrics, build actionable dashboards, design attribution models, and run weekly review cadences that drive decisions. You know that the median B2B SaaS growth rate has settled to 26% in 2025-2026 while CAC has risen 14% to $2.00 per new ARR dollar, making measurement discipline the difference between efficient growth and cash burn.

Before Starting

Gather this context before building any metrics framework, dashboard, or measurement plan:

• What is the current sales motion? PLG, sales-led, agent-led, or hybrid.
• What is the pricing model? Per-seat, usage-based, outcome-based, or hybrid.
• What is the current ARR or MRR? Stage determines which benchmarks apply.
• What CRM and data tools are in use? HubSpot, Salesforce, Attio, or spreadsheets.
• What analytics/BI tools are available? Metabase, Looker, Mode, or Google Sheets.
• How many reps or GTM team members exist? Solo founder vs. team of 50 require different metric depth.
• What does the buyer journey look like today? Touches, average sales cycle, primary channels.
• Is there a weekly review cadence in place? If yes, what gets reviewed and by whom.

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1. Core GTM Metrics Dashboard

Revenue Metrics

| Metric | Definition | How to Calculate | Target | |---|---|---|---| | ARR / MRR | Recurring revenue | Sum of active subscription revenue | Growth rate benchmarks below | | Net New ARR | New minus churned | New ARR + Expansion - Churned ARR | Positive every quarter | | Revenue Latency | Days from first signal to closed deal | Median days first-touch to closed-won | <30d SMB, <90d mid-market, <180d enterprise | | Expansion Revenue % | New ARR from existing customers | Expansion ARR / Total New ARR | >40% at scale ($50M+ ARR companies ~60%) |

Efficiency Metrics

| Metric | How to Calculate | Target | |---|---|---| | CAC | Total S&M spend / New customers | Varies by segment | | CAC Payback | CAC / (ARR per customer * Gross Margin) | <8 months (median 8.6; top performers 5-7) | | Magic Number | Net New ARR (qtr) / S&M Spend (prior qtr) | >0.75 efficient, >1.0 excellent, <0.5 red flag | | LTV:CAC Ratio | (ARPA * Margin * Lifetime) / CAC | >3:1 healthy, >5:1 may be under-investing | | Burn Multiple | Net Burn / Net New ARR | <2x good, <1x excellent, >3x concerning |

Pipeline Metrics

| Metric | How to Calculate | Target | |---|---|---| | Pipeline Coverage | Pipeline value / Period quota | 3-4x sales-led, 2-3x PLG | | Pipeline Velocity | (Qualified Opps * Deal Size * Win Rate) / Cycle Length | Increasing QoQ | | Pipeline per Rep | Total pipeline / Quota-carrying reps | Track trend, not absolute | | Slippage Rate | Deals moved out / Total deals in forecast | <15% weekly |

Retention Metrics

| Metric | How to Calculate | Target | |---|---|---| | NRR | (Start MRR + Expansion - Contraction - Churn) / Start MRR | >106% median; >120% best-in-class | | GRR | (Start MRR - Contraction - Churn) / Start MRR | >90%; >94% at scale | | Logo Churn | Customers lost / Customers at start | <2% monthly SMB, <1% mid-market | | TTFV | Median time from signup to first value event | <15 min self-serve, <1 day sales-led |

NRR Benchmarks by Stage

| ARR Band | Median NRR | Top Quartile | Notes | |---|---|---|---| | $1-3M | ~90% | 94% | Focus on finding high-retention segments | | $3-15M | ~95% | 99% | Expansion motions starting | | $15-30M | ~100% | 105%+ | Expansion should offset churn | | $50-100M | ~110% | 120%+ | Expansion revenue exceeds new logos | | $100M+ | ~115% | 130%+ | Aggressive expansion expected |

Growth Rate Benchmarks

| ARR Band | Median Growth | Top Quartile | |---|---|---| | <$1M | 100%+ | 200%+ | | $1-5M | 80-100% | 150%+ | | $5-20M | 50-80% | 100%+ | | $20-50M | 30-50% | 70%+ | | $100M+ | 20-30% | 40%+ |

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2. Funnel Metrics by GTM Motion

PLG Funnel

Visitor --> Signup (3-5%) --> Activation (30-40%) --> Conversion (5-8%) --> Expansion (NRR 110-120%)

PLG-specific metrics: PQL conversion rate, time-to-activation (<15 min target), feature adoption breadth (core features used in first 14 days), viral coefficient (>0.3 target).

Sales-Led Funnel

Signal --> Outreach (3-5% reply) --> Meeting (50%) --> Demo (60%) --> Pilot (40%) --> Close (30%)

Sales-led specific: ACV trend, sales cycle length (median days), win rate by segment, pipeline created per rep per month, quota attainment distribution.

Agent-Led Funnel (AI SDR)

Signal --> AI Qualification (10-15%) --> Human Meeting (50%) --> Close (35%)

Agent-led specific: cost per meeting booked, cost per qualified lead, AI outreach ROI (revenue from AI pipeline / AI cost), send-to-reply ratio, human-to-AI leverage ratio.

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3. AI Product-Specific Metrics

AI products carry cost structures that traditional SaaS metrics miss. These supplementary metrics are essential for AI-native businesses.

AI Cost Metrics

| Metric | How to Calculate | Target | |---|---|---| | AI Cost of Revenue | Inference + compute cost / Revenue | <20% of revenue | | Cost per AI Action | Total AI compute / Actions generated | Decreasing over time | | ROAI | AI-attributed revenue / (Inference + compute overhead) | >10x for high performers | | Gross Margin after AI | (Revenue - COGS - AI compute) / Revenue | >70% (vs. ~80% pure SaaS) |

Usage-Based Pricing Metrics

42% of SaaS companies use consumption-based pricing in 2025 (up from 29% in 2023). When pricing is usage-based, supplement ARR metrics with:

| Metric | Why It Matters | |---|---| | Committed vs. Consumed ARR | Gap indicates pricing misalignment or under-adoption | | Usage Growth Rate | Leading indicator of expansion revenue | | Overage Frequency | Signals pricing tier design quality | | Unit Economics per Consumption Unit | Revenue minus cost per unit; must be positive and improving | | NRR by Cohort (usage-based only) | Separates usage-driven expansion from seat expansion |

SaaS vs. AI Product Metrics Differences

| SaaS Metric | AI Difference | Additional AI Metric | |---|---|---| | Gross margin (~80%) | AI inference lowers to 60-75% | Track AI cost of revenue separately | | DAU/MAU | Usage is task-driven, not session-driven | Task completion rate, actions per session | | Feature adoption | AI features are singular and deep | Outcome success rate per AI action | | Time-on-platform | Less time can mean more value | Time-saved-per-task | | Per-seat revenue | Consumption pricing varies by user | Revenue per consumption unit |

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4. Data Health Scoring

Bad CRM data makes every other metric unreliable. Quantify data trustworthiness before trusting pipeline reports.

Data Health Score

Data Health Score = (Completeness * 0.35) + (Accuracy * 0.30) + (Recency * 0.20) + (Consistency * 0.15)

| Component | Weight | What It Measures | |---|---|---| | Completeness | 35% | % of required fields populated per record | | Accuracy | 30% | % of data points verified against enrichment sources | | Recency | 20% | % of records updated within 90 days | | Consistency | 15% | % of records matching format standards |

Health Score Targets

| Score | Grade | Action | |---|---|---| | 90-100% | A | Maintain current enrichment cadence | | 80-89% | B | Schedule enrichment refresh for lowest-scoring segments | | 70-79% | C | Pipeline metrics may be unreliable; run enrichment sprint | | Below 70% | F | Stop trusting pipeline reports; full data cleanup required |

B2B data decays at 2.1% monthly on average. Required enrichment refresh cadence: contact email/phone every 90 days, firmographics every 90 days, intent signals weekly or real-time, ICP scores recalculated on any underlying data refresh.

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5. Attribution Models

Attribution answers "what caused the deal?" Getting it right determines where you invest next.

Model Comparison

| Model | How It Works | Best For | Limitation | |---|---|---|---| | First-touch | 100% to first interaction | Top-of-funnel channel effectiveness | Ignores nurture and closing touches | | Last-touch | 100% to final interaction | Bottom-of-funnel conversion analysis | Ignores awareness investment | | Linear | Equal credit to all touchpoints | Simple fairness | Treats blog visit same as demo request | | U-shaped | 40% first, 40% last, 20% middle | B2B with clear awareness-to-conversion journey | Undervalues mid-funnel | | W-shaped | 30/30/30/10 (first/lead/opp/rest) | B2B with defined marketing-to-sales handoff | Requires clear CRM stage definitions | | Time-decay | Increasing credit toward conversion | Long sales cycles | Undervalues early brand investment | | AI-driven | ML determines credit dynamically | Orgs with 500+ conversions | Black box; requires data maturity |

Choosing by Company Stage

| Stage | Model | Why | |---|---|---| | Pre-revenue / <$1M | First-touch | Know which channels generate any pipeline | | $1-5M | U-shaped | Credits awareness and conversion, most actionable | | $5-20M | W-shaped | Marketing-to-sales handoff stages worth measuring | | $20M+ | Time-decay or AI-driven | Enough data; long cycles justify recency weighting | | PLG (any stage) | Product-touch | Attribute to in-product actions, not just marketing |

Attribution Lookback Windows

Set lookback to match your sales cycle: 90 days for SMB, 180 days for mid-market, 365 days for enterprise. Run parallel first-touch and multi-touch models for 2 quarters to calibrate. Review quarterly.

AI GTM Attribution Challenges

| Challenge | Mitigation | |---|---| | AI SDR touches invisible to buyers | Tag AI-generated touches with source=AI-SDR in CRM | | Multi-channel AI sequences | Track channel and sequence membership, not just "AI outreach" | | Influence vs. creation confusion | Separate "source" from "influence" attribution | | Dark social (Slack, Discord, DMs) | Ask "how did you hear about us?" in demo forms |

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6. Dashboard Architecture

Three-Tier Hierarchy

Tier 1: Board (5-7 metrics, monthly) - ARR + Net New ARR waterfall, NRR, CAC Payback, Burn Multiple, Pipeline Coverage, Magic Number, Cash Runway.

Tier 2: Executive (10-12 metrics, weekly) - Pipeline created, pipeline by stage, win rate by segment, deal size trend, sales cycle length, quota attainment by rep, NRR by cohort, CAC by channel, TTFV, data health score, slippage rate.

Tier 3: Operator (15-25 metrics, daily) - Activity (emails, calls, meetings booked), pipeline (new opps, stage movements), response (speed-to-lead, follow-up rate), conversion (stage-by-stage rates), quality (ICP fit distribution), AI ops (AI messages, AI reply rate, cost per meeting).

Tool Selection

| Tool | Best For | Cost | |---|---|---| | HubSpot Dashboards | Teams already on HubSpot | Included | | Me