GTM Metrics, Dashboards & Measurement for AI Products

Overview

This public intake copy packages

packages/skills-catalog/skills/(gtm)/gtm-metrics

https://github.com/tech-leads-club/agent-skills

Use it when the operator needs the upstream workflow, support files, and repository context to stay intact while the public validator and private enhancer continue their normal downstream flow.

This intake keeps the copied upstream files intact and uses

metadata.json

ORIGIN.md

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.

Imported source sections that did not map cleanly to the public headings are still preserved below or in the support files. Notable imported sections: Before Starting, 1. Core GTM Metrics Dashboard, 2. Funnel Metrics by GTM Motion, 3. AI Product-Specific Metrics, 4. Data Health Scoring, 5. Attribution Models.

When to Use This Skill

Use this section as the trigger filter. It should make the activation boundary explicit before the operator loads files, runs commands, or opens a pull request.

• Use when the request clearly matches the imported source intent: 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,'....
• Use when the operator should preserve upstream workflow detail instead of rewriting the process from scratch.
• Use when provenance needs to stay visible in the answer, PR, or review packet.
• Use when copied upstream references, examples, or scripts materially improve the answer.
• Use when the workflow should remain reviewable in the public intake repo before the private enhancer takes over.

Operating Table

metadata.json

ORIGIN.md

SKILL.md

SKILL.md

## Related Skills

Workflow

This workflow is intentionally editorial and operational at the same time. It keeps the imported source useful to the operator while still satisfying the public intake standards that feed the downstream enhancer flow.

1. Confirm the user goal, the scope of the imported workflow, and whether this skill is still the right router for the task.
2. Read the overview and provenance files before loading any copied upstream support files.
3. Load only the references, examples, prompts, or scripts that materially change the outcome for the current request.
4. Execute the upstream workflow while keeping provenance and source boundaries explicit in the working notes.
5. Validate the result against the upstream expectations and the evidence you can point to in the copied files.
6. Escalate or hand off to a related skill when the work moves out of this imported workflow's center of gravity.
7. Before merge or closure, record what was used, what changed, and what the reviewer still needs to verify.

Imported Workflow Notes

Imported: 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.

Examples

Example 1: Ask for the upstream workflow directly

Use @gtm-metrics to handle <task>. Start from the copied upstream workflow, load only the files that change the outcome, and keep provenance visible in the answer.

Explanation: This is the safest starting point when the operator needs the imported workflow, but not the entire repository.

Example 2: Ask for a provenance-grounded review

Review @gtm-metrics against metadata.json and ORIGIN.md, then explain which copied upstream files you would load first and why.

Explanation: Use this before review or troubleshooting when you need a precise, auditable explanation of origin and file selection.

Example 3: Narrow the copied support files before execution

Use @gtm-metrics for <task>. Load only the copied references, examples, or scripts that change the outcome, and name the files explicitly before proceeding.

Explanation: This keeps the skill aligned with progressive disclosure instead of loading the whole copied package by default.

Example 4: Build a reviewer packet

Review @gtm-metrics using the copied upstream files plus provenance, then summarize any gaps before merge.

Explanation: This is useful when the PR is waiting for human review and you want a repeatable audit packet.

Imported Usage Notes

Imported: Examples

• User says: "What metrics should we track for GTM?" → Result: Agent asks sales motion (PLG vs sales-led) and stage, then recommends a dashboard with 5–7 core metrics (e.g. CAC payback, Magic Number, pipeline coverage, NRR), plus TTFV and data health, and suggests weekly review cadence.
• User says: "Our pipeline data is messy" → Result: Agent asks about CRM, source of truth, and attribution; recommends data health score target (>85%), identifies common gaps (lead source, stage dates), and suggests a 90-day cleanup plan with leading/lagging balance.
• User says: "How do we compare to benchmarks?" → Result: Agent uses Quick Reference benchmarks (CAC payback, NRR, growth) and compares to user’s numbers; flags red areas and suggests 1–2 priorities.

Best Practices

Treat the generated public skill as a reviewable packaging layer around the upstream repository. The goal is to keep provenance explicit and load only the copied source material that materially improves execution.

• Keep the imported skill grounded in the upstream repository; do not invent steps that the source material cannot support.
• Prefer the smallest useful set of support files so the workflow stays auditable and fast to review.
• Keep provenance, source commit, and imported file paths visible in notes and PR descriptions.
• Point directly at the copied upstream files that justify the workflow instead of relying on generic review boilerplate.
• Treat generated examples as scaffolding; adapt them to the concrete task before execution.
• Route to a stronger native skill when architecture, debugging, design, or security concerns become dominant.

Troubleshooting

Problem: The operator skipped the imported context and answered too generically

Symptoms: The result ignores the upstream workflow in

packages/skills-catalog/skills/(gtm)/gtm-metrics

metadata.json

ORIGIN.md

Problem: The imported workflow feels incomplete during review

Symptoms: Reviewers can see the generated

SKILL.md

Problem: The task drifted into a different specialization

Symptoms: The imported skill starts in the right place, but the work turns into debugging, architecture, design, security, or release orchestration that a native skill handles better. Solution: Use the related skills section to hand off deliberately. Keep the imported provenance visible so the next skill inherits the right context instead of starting blind.

Imported Troubleshooting Notes

Imported: Troubleshooting

• Metrics don’t match across tools → Cause: Different definitions or attribution windows. Fix: Define one source of truth (e.g. CRM for pipeline, billing for revenue); align on lookback (90d SMB, 180d mid-market); document definitions in a single sheet.
• CAC payback getting worse → Cause: CAC up and/or velocity down. Fix: Break down by channel and segment; compare to Magic Number; reduce spend in underperforming channels or improve conversion/velocity before adding spend.
• NRR below 100% → Cause: Churn and/or downgrades outweigh expansion. Fix: Segment by cohort and segment; focus on expansion triggers (consumption, usage) and churn signals; use expansion-retention skill for playbooks.

Related Skills

• @accessibility

  - Use when the work is better handled by that native specialization after this imported skill establishes context.

• @ai-cold-outreach

  - Use when the work is better handled by that native specialization after this imported skill establishes context.

• @ai-pricing

  - Use when the work is better handled by that native specialization after this imported skill establishes context.

• @ai-sdr

  - Use when the work is better handled by that native specialization after this imported skill establishes context.

Additional Resources

Use this support matrix and the linked files below as the operator packet for this imported skill. They should reflect real copied source material, not generic scaffolding.

references

references/n/a

examples

examples/n/a

scripts

scripts/n/a

agents

agents/n/a

assets

assets/n/a

Imported Reference Notes

Imported: Quick Reference

Imported: 1. Core GTM Metrics Dashboard

Revenue Metrics

Efficiency Metrics

Pipeline Metrics

Retention Metrics

NRR Benchmarks by Stage

Growth Rate Benchmarks

Imported: 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.

Imported: 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

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:

SaaS vs. AI Product Metrics Differences

Imported: 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)

Health Score Targets

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.

Imported: 5. Attribution Models

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

Model Comparison

Choosing by Company Stage

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

Imported: 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

Dashboard Anti-Patterns

Imported: 7. Leading vs. Lagging Indicators

Maintain a 60/40 balance: 60% leading indicators (what is about to happen) and 40% lagging indicators (what already happened).

Leading Indicators

Lagging Indicators

Revenue, win rate, CAC/payback, NRR/GRR, LTV:CAC, burn multiple, quota attainment distribution. Review monthly or quarterly.

The Leading-to-Lagging Chain

Revenue (lagging)
  <-- Win Rate
    <-- Demo Quality Score (leading)
    <-- ICP Fit Score of Pipeline (leading)
  <-- Pipeline Volume
    <-- Meetings Booked (leading)
      <-- Outreach Volume + Reply Rate (leading)
  <-- Deal Size
    <-- Multi-threading Depth (leading)

Imported: 8. Weekly GTM Review Cadence

The Weekly Meeting (30-45 Minutes)

The single most important GTM operating ritual. Every metric from system-of-record data. No hand-edited slides.

Weekly Scorecard

Monthly Deep-Dives

NRR/retention analysis (cohort curves, churn reasons, expansion pipeline), CAC/efficiency review (CAC by channel, payback trend, Magic Number), data health audit (CRM completeness, enrichment gaps), competitive update (pricing, positioning, feature changes).

Quarterly Strategic Reviews

ICP refresh (win/loss analysis, drift detection, scoring update), funnel benchmarking (stage conversions vs. industry), attribution model review (channel ROI, budget allocation), GTM motion evaluation (sales-led vs. PLG vs. agent performance).

Imported: 9. PQL Scoring

Product-Qualified Leads replace MQLs in product-led and hybrid motions. Score on product usage instead of content downloads.

PQL Scoring Model

PQL Score = (Usage Signals * 0.50) + (Fit Signals * 0.30) + (Intent Signals * 0.20)

PQL-to-customer conversion: 5-15% (vs. 1-3% MQL-to-customer). Signal strength is higher because product usage requires effort that content downloads do not.

Imported: Questions to Ask

1. What metrics does your team review weekly today, and who owns each one?
2. What is your current pipeline coverage ratio, and do you trust the data behind it?
3. How do you measure time-to-first-value for new customers?
4. What is your CAC payback period, and is it trending up or down?
5. What percentage of new ARR comes from expansion vs. new logos?
6. How complete is your CRM data? Could you run a data health audit this week?
7. What attribution model are you using, and when was it last reviewed?
8. Do you have separate funnel metrics for each GTM motion?
9. What is your current NRR, and how does it break down by segment?
10. How do you score and prioritize PQLs vs. MQLs?
11. What does your AI inference cost look like as a percentage of revenue?
12. Do you track leading indicators separately from lagging indicators?
13. What is your average speed-to-lead for inbound demo requests?
14. When did you last benchmark funnel conversion rates against industry standards?
15. Do you have a defined weekly GTM review cadence with a scorecard?