Gtm Metrics
Define GTM and AI-specific metrics, design dashboards, and set weekly review cadences so pipeline and unit economics drive decisions instead of vanity counts.
Overview
gtm-metrics is an agent skill most often used in Grow (also Validate pricing, Operate iterate) that defines GTM metrics, dashboards, attribution, and review cadences for AI products.
Install
npx skills add https://github.com/chadboyda/agent-gtm-skills --skill gtm-metricsWhat is this skill?
- Covers GTM measurement from metric selection through dashboard design and weekly review cadences
- Addresses AI-native differences: usage-based consumption, AI cost-of-revenue, outcome-based pricing measurement
- Triggers on TTFV, revenue latency, magic number, pipeline velocity, NRR, attribution, and data health
- Frames 2025–2026 context: median B2B SaaS growth ~26% and rising CAC (~$2 per new ARR dollar cited in skill)
- Starts with a Before Starting context gather before building any metrics framework
- Median B2B SaaS growth rate cited at 26% (2025–2026 framing in skill)
- CAC cited rising 14% to about $2.00 per new ARR dollar
Adoption & trust: 1 installs on skills.sh; 50 GitHub stars; 3/3 security scanners passed (skills.sh audits); trending (+100% hot-view momentum).
What problem does it solve?
You are shipping AI GTM motions but cannot tell which metrics matter, how AI COGS distort LTV/CAC, or what to review weekly to fix pipeline leaks.
Who is it for?
Founder-led B2B AI SaaS teams post-early-revenue who need a measurement framework before scaling paid or outbound spend.
Skip if: Pre-idea market scanning with no product metrics to feed, or pure engineering observability (errors/uptime) with no revenue funnel.
When should I use this skill?
User wants GTM metrics, dashboards, pipeline efficiency, TTFV, attribution, CAC/LTV/NRR, magic number, pipeline velocity, or AI product performance measurement.
What do I get? / Deliverables
You leave with a prioritized metric set, dashboard design direction, attribution approach, and a weekly review cadence aligned to AI consumption and revenue outcomes.
- Prioritized GTM metric framework including AI-specific cost lines
- Dashboard structure and weekly review cadence recommendation
- Attribution and data-health considerations documented for the motion
Recommended Skills
Journey fit
Spans multiple journey phases - primary shelf plus alternate fits below.
Canonical shelf is Grow/analytics because the skill’s core job is measuring pipeline, attribution, CAC/LTV, and AI cost-of-revenue after you have something to sell. Analytics subphase holds metric selection, dashboard architecture, funnel velocity, and review rituals—not one-off landing copy or deploy runbooks.
Where it fits
Pressure-test list price against projected CAC, LTV, and AI cost-of-revenue before committing to outbound spend.
Stand up a weekly funnel review spanning pipeline velocity, magic number, and TTFV for a new AI feature launch.
Diagnose worsening data health or attribution gaps and adjust metric definitions before the next growth sprint.
How it compares
GTM measurement playbook for revenue leaders, not a product analytics SDK install guide or generic SQL dashboard tutorial.
Common Questions / FAQ
Who is gtm-metrics for?
Solo founders and small GTM teams selling AI products who own pipeline reporting, board metrics, and efficiency reviews without a full RevOps hire.
When should I use gtm-metrics?
In Grow when building a GTM dashboard or weekly metric review; in Validate when pricing needs CAC/LTV and TTFV proof; in Operate when iterating funnel and data health fixes.
Is gtm-metrics safe to install?
Treat it as strategic guidance that may reference your revenue data in chat; review the Security Audits panel on this Prism page and avoid pasting raw credentials into prompts.
SKILL.md
READMESKILL.md - Gtm Metrics
# 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. --- ## 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 | Custome