Product-Led Growth skill 13 of 13

PLG Metrics and Reporting SKILL.md

The PLG Metrics skill makes Claude, Codex or another AI agent measure product-led growth from sign-up to net revenue retention. It builds a metric tree, writes shared definitions, checks data quality, creates cohort and segment views, separates self-serve and sales-assisted results, uses benchmarks carefully, designs dashboards and runs reviews that end with decisions logged against metrics.

plg-metrics/SKILL.md, version 1.0.0, 1,535 words.

By Afeez Adebayo, Marketing, SEO & Growth Expert with 10+ years of experience.

Created 10 October 2026. Updated 10 October 2026. Free to use under CC BY 4.0.

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Product-led businesses live or die by what happens inside the product, yet many still report sign-ups and revenue and little in between. Without activation, habit, team formation, conversion, expansion and retention metrics, teams can't tell whether growth problems come from acquisition, onboarding, the free model or sales assist. Good PLG reporting connects product behaviour to revenue, by cohort and segment, with a small set of metrics everyone agrees on.

This skill builds PLG metrics and reporting: the metric tree from sign-up to net revenue retention, definitions, cohort analysis, product-qualified account tracking, self-serve versus sales-assisted views, benchmarks used carefully, dashboards, review rhythms and decisions.

Operating principles

Measure the journey, not just the ends. Activation, habit, team and conversion matter as much as sign-ups and revenue.
Cohorts beat totals. Compare groups who signed up in the same period.
Accounts and users both matter. B2B PLG needs account-level views alongside users.
One definition per metric. Shared definitions end debates between teams.
Every review ends in a decision. Metrics exist to change what teams do.

Core PLG metrics

Stage	Metric	Definition (example)
Acquisition	Sign-ups	New accounts created
Activation	Activation rate	Sign-ups reaching the value moment within 7 days
Time to value	Median time to activation	Hours or days from sign-up
Habit	Week-4 retention	Activated users active in week 4
Team	Team formation rate	Accounts with 2+ active users within 30 days
Conversion	Free-to-paid or trial-to-paid rate	Accounts paying within 30, 60 or 90 days
Monetisation	Revenue per sign-up	Revenue from a cohort divided by its sign-ups
Expansion	Expansion rate	Paying accounts that add seats or upgrade
Retention	Logo and revenue retention	Accounts and revenue kept over time
Overall	Net revenue retention	Revenue from a cohort a year later, including expansion and churn
Sales assist	PQL conversion	PQL accounts converting to paid
Efficiency	CAC payback	Months to recover acquisition cost

When to use, and when not to

Use it when setting up PLG dashboards, preparing board updates, diagnosing growth slowdowns, comparing self-serve and sales-assisted revenue and aligning product and go-to-market teams.

Don't use it for detailed tracking plans (the Growth Marketing tracking-plan skill) or company-wide growth reporting beyond PLG (the Growth Marketing growth-reporting skill).

Inputs to ask for, in one message

Product analytics, billing and CRM data access.
Current definitions of activation, active users and conversion.
Plans, free model and sales assist thresholds.
Cohort data for at least 6 to 12 months.
Stakeholders and decisions they make.
Existing dashboards.

Raw SKILL.md file

.md

This is the exact file agents read, including the credit header and licence notice. Save it as plg-metrics/SKILL.md.

---
name: plg-metrics
description: "Measure product-led growth from sign-up to net revenue retention: a metric tree, shared definitions for activation, time to value, habit, team formation, free-to-paid conversion, revenue per sign-up, expansion and PQL conversion, cohort and segment views, self-serve versus sales-assisted reporting, careful benchmarks, dashboards and decision-led reviews. Use whenever someone asks about PLG metrics, product-led growth KPIs, activation rate, free-to-paid conversion, revenue per sign-up, PLG dashboards, cohort analysis for self-serve or diagnosing why sign-ups grow but revenue does not."
license: CC-BY-4.0
metadata:
  author: Afeez Adebayo
  author_url: https://afeezadebayo.com
  author_linkedin: https://www.linkedin.com/in/afeez-adebayo/
  author_x: https://x.com/za_icon
  source: https://afeezadebayo.com/skills-md/marketing/product-led-growth/plg-metrics/
  category: marketing
  subcategory: product-led-growth
  version: "1.0.0"
  created: "2026-10-10"
  updated: "2026-10-10"
---

# PLG Metrics and Reporting

Product-led businesses live or die by what happens inside the product, yet many still report sign-ups and revenue and little in between. Without activation, habit, team formation, conversion, expansion and retention metrics, teams can't tell whether growth problems come from acquisition, onboarding, the free model or sales assist. Good PLG reporting connects product behaviour to revenue, by cohort and segment, with a small set of metrics everyone agrees on.

This skill builds PLG metrics and reporting: the metric tree from sign-up to net revenue retention, definitions, cohort analysis, product-qualified account tracking, self-serve versus sales-assisted views, benchmarks used carefully, dashboards, review rhythms and decisions.

## Operating principles
1. **Measure the journey, not just the ends.** Activation, habit, team and conversion matter as much as sign-ups and revenue.
2. **Cohorts beat totals.** Compare groups who signed up in the same period.
3. **Accounts and users both matter.** B2B PLG needs account-level views alongside users.
4. **One definition per metric.** Shared definitions end debates between teams.
5. **Every review ends in a decision.** Metrics exist to change what teams do.

## Core PLG metrics
| Stage | Metric | Definition (example) |
|---|---|---|
| Acquisition | Sign-ups | New accounts created |
| Activation | Activation rate | Sign-ups reaching the value moment within 7 days |
| Time to value | Median time to activation | Hours or days from sign-up |
| Habit | Week-4 retention | Activated users active in week 4 |
| Team | Team formation rate | Accounts with 2+ active users within 30 days |
| Conversion | Free-to-paid or trial-to-paid rate | Accounts paying within 30, 60 or 90 days |
| Monetisation | Revenue per sign-up | Revenue from a cohort divided by its sign-ups |
| Expansion | Expansion rate | Paying accounts that add seats or upgrade |
| Retention | Logo and revenue retention | Accounts and revenue kept over time |
| Overall | Net revenue retention | Revenue from a cohort a year later, including expansion and churn |
| Sales assist | PQL conversion | PQL accounts converting to paid |
| Efficiency | CAC payback | Months to recover acquisition cost |

## When to use, and when not to
Use it when setting up PLG dashboards, preparing board updates, diagnosing growth slowdowns, comparing self-serve and sales-assisted revenue and aligning product and go-to-market teams.

Don't use it for detailed tracking plans (the Growth Marketing `tracking-plan` skill) or company-wide growth reporting beyond PLG (the Growth Marketing `growth-reporting` skill).

## Inputs to ask for, in one message
1. Product analytics, billing and CRM data access.
2. Current definitions of activation, active users and conversion.
3. Plans, free model and sales assist thresholds.
4. Cohort data for at least 6 to 12 months.
5. Stakeholders and decisions they make.
6. Existing dashboards.

## Workflow

### Step 1. Build the metric tree
From sign-ups to net revenue retention, showing how each stage feeds the next.

### Step 2. Agree definitions
Write them down: events, time windows, user versus account level.

### Step 3. Check data quality
Event tracking, identity across devices, account mapping between product, billing and CRM.

### Step 4. Build cohort views
Monthly sign-up cohorts by activation, conversion and retention; segments by source, plan, company size and region.

### Step 5. Separate motions
Self-serve versus sales-assisted conversion, deal size and retention.

### Step 6. Build dashboards
A one-page PLG overview, plus stage-specific views for teams.

### Step 7. Use benchmarks carefully
Compare to your own history first; external benchmarks vary widely by product and definition.

### Step 8. Set review rhythm
Weekly team metrics, monthly PLG review, quarterly board view.

### Step 9. Decide
Each review names the stage to focus on next and the experiments or fixes planned.

## PLG overview dashboard
```
| Metric | This month | Last month | Same month last year | Target | Note |
| Sign-ups | | | | | |
| Activation rate | | | | | |
| Median time to value | | | | | |
| Week-4 retention | | | | | |
| Team formation rate | | | | | |
| Free-to-paid (90 days) | | | | | |
| Revenue per sign-up | | | | | |
| Net revenue retention | | | | | |
| PQL conversion | | | | | |
```

## Diagnosing a slowdown
| Pattern | Likely stage | Next step |
|---|---|---|
| Sign-ups up, revenue flat | Activation or conversion | Check activation and limits by cohort |
| Activation stable, conversion falling | Free model or upgrade prompts | Review limits and prompts |
| Conversion stable, revenue retention falling | Retention or expansion | Cohort retention and feature adoption |
| Self-serve strong, large accounts stuck | Sales assist | PQL coverage and plays |

## Cohort table example
| Sign-up month | Sign-ups | Activated (7 days) | Week-4 retained | Paid by day 90 | Revenue per sign-up |
|---|---|---|---|---|---|
| January | 4,200 | 38% | 21% | 4.1% | \u00a311.40 |
| February | 4,900 | 37% | 20% | 3.6% | \u00a39.80 |
| March | 6,100 | 39% | 21% | 3.2% | \u00a38.70 |
Illustrative; here activation holds steady while conversion falls, pointing at the free model or upgrade prompts rather than onboarding.

## Account versus user metrics
| Question | User-level metric | Account-level metric |
|---|---|---|
| Are people getting value? | Activation, weekly active users | Accounts with activated users |
| Is usage spreading? | Invites sent | Active users per account, team formation rate |
| Is revenue growing? | Upgrades by users | Paid accounts, seats, expansion, net revenue retention |

## Decision log
Record each review's decision, the metric it targets, the owner and the date to check results. Over time the log shows which changes moved PLG metrics and stops teams repeating failed ideas.

## Edge cases and judgement calls
| Situation | What to do |
|---|---|
| Activation definition changes | Restate past cohorts or mark the break clearly |
| Free users with no account mapping | Improve identity and domain mapping |
| Long conversion cycles | Use 90 or 180-day windows and leading indicators |
| Seasonality | Compare cohorts year on year |
| Several products | Separate funnels with a shared account view |

## Handoffs to related skills
- Audit: `plg-audit`
- Strategy and value moment: `plg-strategy`
- PQLs: `product-qualified-leads`
- Tracking plans: the Growth Marketing `tracking-plan` skill
- Cohort analysis: the Growth Marketing `cohort-retention-analysis` skill
- Company reporting: the Growth Marketing `growth-reporting` skill

## Worked example
A SaaS company reported monthly sign-ups and revenue only. Sign-ups had doubled in a year while revenue grew slowly. A PLG dashboard with cohort views showed activation steady but free-to-paid conversion falling for recent cohorts, because a new free plan covered most small-team needs, while sales-assisted accounts expanded well. The monthly review led to tighter free limits, new upgrade prompts and more PQL coverage for larger accounts. Revenue per sign-up recovered over two quarters, and the board received a clearer story.

## Output format
```
# PLG metrics plan: <product>, <date>
## Metric tree
## Definitions
## Data quality checks
## Cohort and segment views
## Self-serve vs sales-assisted view
## Dashboard design
## Review rhythm and decision log
```

## Common mistakes
- Reporting only sign-ups and revenue.
- Inconsistent activation definitions across teams.
- Totals instead of cohorts.
- Ignoring account-level metrics in B2B.
- Comparing to external benchmarks with different definitions.

## Questions to ask before you build
- What is your value moment, and is activation measured against it?
- Can product usage be tied to accounts and revenue?
- Who uses the metrics, and what decisions do they make?

## Self-check before you answer
- Does the metric tree cover every stage?
- Are definitions written and shared?
- Are cohorts and segments used?
- Do reviews lead to decisions?

## Further reading
- Lenny Rachitsky on PLG benchmarks: https://www.lennysnewsletter.com/
- Amplitude product analytics playbooks: https://amplitude.com/blog

---
Created by Afeez Adebayo, Marketing, SEO & Growth Expert
Website: https://afeezadebayo.com
LinkedIn: https://www.linkedin.com/in/afeez-adebayo/
X: https://x.com/za_icon
Source: https://afeezadebayo.com/skills-md/marketing/product-led-growth/plg-metrics/

Licence: CC BY 4.0 (https://creativecommons.org/licenses/by/4.0/). You may copy, share and adapt this skill, including for commercial use, as long as you credit Afeez Adebayo and link to the source URL above. Keep this notice in any copy or adapted version.

Workflow

Step 1. Build the metric tree

From sign-ups to net revenue retention, showing how each stage feeds the next.

Step 2. Agree definitions

Write them down: events, time windows, user versus account level.

Step 3. Check data quality

Event tracking, identity across devices, account mapping between product, billing and CRM.

Step 4. Build cohort views

Monthly sign-up cohorts by activation, conversion and retention; segments by source, plan, company size and region.

Step 5. Separate motions

Self-serve versus sales-assisted conversion, deal size and retention.

Step 6. Build dashboards

A one-page PLG overview, plus stage-specific views for teams.

Step 7. Use benchmarks carefully

Compare to your own history first; external benchmarks vary widely by product and definition.

Step 8. Set review rhythm

Weekly team metrics, monthly PLG review, quarterly board view.

Step 9. Decide

Each review names the stage to focus on next and the experiments or fixes planned.

PLG overview dashboard

| Metric | This month | Last month | Same month last year | Target | Note |
| Sign-ups | | | | | |
| Activation rate | | | | | |
| Median time to value | | | | | |
| Week-4 retention | | | | | |
| Team formation rate | | | | | |
| Free-to-paid (90 days) | | | | | |
| Revenue per sign-up | | | | | |
| Net revenue retention | | | | | |
| PQL conversion | | | | | |

Diagnosing a slowdown

Pattern	Likely stage	Next step
Sign-ups up, revenue flat	Activation or conversion	Check activation and limits by cohort
Activation stable, conversion falling	Free model or upgrade prompts	Review limits and prompts
Conversion stable, revenue retention falling	Retention or expansion	Cohort retention and feature adoption
Self-serve strong, large accounts stuck	Sales assist	PQL coverage and plays

Cohort table example

Sign-up month	Sign-ups	Activated (7 days)	Week-4 retained	Paid by day 90	Revenue per sign-up
January	4,200	38%	21%	4.1%	\u00a311.40
February	4,900	37%	20%	3.6%	\u00a39.80
March	6,100	39%	21%	3.2%	\u00a38.70

Illustrative; here activation holds steady while conversion falls, pointing at the free model or upgrade prompts rather than onboarding.

Account versus user metrics

Question	User-level metric	Account-level metric
Are people getting value?	Activation, weekly active users	Accounts with activated users
Is usage spreading?	Invites sent	Active users per account, team formation rate
Is revenue growing?	Upgrades by users	Paid accounts, seats, expansion, net revenue retention

Decision log

Record each review's decision, the metric it targets, the owner and the date to check results. Over time the log shows which changes moved PLG metrics and stops teams repeating failed ideas.

Edge cases and judgement calls

Situation	What to do
Activation definition changes	Restate past cohorts or mark the break clearly
Free users with no account mapping	Improve identity and domain mapping
Long conversion cycles	Use 90 or 180-day windows and leading indicators
Seasonality	Compare cohorts year on year
Several products	Separate funnels with a shared account view

Audit: plg-audit
Strategy and value moment: plg-strategy
PQLs: product-qualified-leads
Tracking plans: the Growth Marketing tracking-plan skill
Cohort analysis: the Growth Marketing cohort-retention-analysis skill
Company reporting: the Growth Marketing growth-reporting skill

Worked example

A SaaS company reported monthly sign-ups and revenue only. Sign-ups had doubled in a year while revenue grew slowly. A PLG dashboard with cohort views showed activation steady but free-to-paid conversion falling for recent cohorts, because a new free plan covered most small-team needs, while sales-assisted accounts expanded well. The monthly review led to tighter free limits, new upgrade prompts and more PQL coverage for larger accounts. Revenue per sign-up recovered over two quarters, and the board received a clearer story.

Output format

# PLG metrics plan: <product>, <date>
## Metric tree
## Definitions
## Data quality checks
## Cohort and segment views
## Self-serve vs sales-assisted view
## Dashboard design
## Review rhythm and decision log

Common mistakes

Reporting only sign-ups and revenue.
Inconsistent activation definitions across teams.
Totals instead of cohorts.
Ignoring account-level metrics in B2B.
Comparing to external benchmarks with different definitions.

Questions to ask before you build

What is your value moment, and is activation measured against it?
Can product usage be tied to accounts and revenue?
Who uses the metrics, and what decisions do they make?

Self-check before you answer

Does the metric tree cover every stage?
Are definitions written and shared?
Are cohorts and segments used?
Do reviews lead to decisions?

When to use this skill, and when not to

Use it when setting up PLG dashboards, preparing board updates, diagnosing slowdowns, comparing self-serve and sales-assisted revenue and aligning product and go-to-market teams.

Many product-led companies report sign-ups and revenue and little in between, so they cannot tell whether problems come from acquisition, onboarding, the free model or sales assist. Good reporting connects product behaviour to revenue by cohort.

Don’t use it for tracking plans (use the Growth Marketing Tracking Plan skill) or company-wide reporting (use Growth Reporting).

Example prompts

“Build a PLG dashboard with cohorts.”
“Define activation, habit and conversion for our team.”
“Sign-ups doubled but revenue did not. Diagnose it.”
“Compare self-serve and sales-assisted revenue.”
“What PLG metrics should go to the board?”

Example output

The skill returns a fixed structure. A shortened example for an imaginary site:

# PLG metrics review: example SaaS, 12 October 2026

Sign-ups: doubled in a year; revenue slow
Cohorts: activation steady; free-to-paid falling for recent cohorts (new free plan too generous)
Sales-assisted: expanding well
Decisions: tighter free limits, new upgrade prompts, more PQL coverage
Result: revenue per sign-up recovered over two quarters

Cohorts replace totals, because totals hide which stage of the journey is breaking.

What the agent needs from you

The skill asks for product analytics, billing and CRM access, current definitions of activation and conversion, plans, free model and sales thresholds, six to twelve months of cohort data, stakeholders and existing dashboards.

How this skill hands off to related skills

Metrics connect to the set: the Audit, Strategy and PQLs, plus the Growth Marketing Tracking Plan, Cohort Retention and Growth Reporting skills.

How to run PLG Metrics step by step

Install the skill as plg-metrics/SKILL.md.
Build the metric tree.
Agree definitions and check data.
Create cohort and segment views.
Separate motions and build dashboards.
Review and log decisions.

Situations this skill handles

PLG reporting meets familiar problems. The skill handles these situations:

Situation	What the skill does
Activation definition changes	Cohorts restated or marked
Free users without account mapping	Identity and domain mapping
Long conversion cycles	90 or 180-day windows
Seasonality	Year-on-year cohorts
Several products	Separate funnels, shared accounts
Board wants one number	Revenue per sign-up and NRR

What makes this skill different

Most PLG reporting shows the ends of the funnel. This skill measures every stage with shared definitions, compares cohorts, splits self-serve from sales-assisted revenue and diagnoses slowdowns by pattern.

A decision log records what changed after each review, so the team learns which moves worked.

Practical tips from real projects

Measure the journey, not just the ends.
Cohorts beat totals.
Accounts and users both matter.
Every review ends in a decision.

A worked example: a SaaS company reported monthly sign-ups and revenue only. Sign-ups had doubled in a year while revenue grew slowly. A PLG dashboard with cohort views showed activation steady but free-to-paid conversion falling for recent cohorts, because a new free plan covered most small-team needs, while sales-assisted accounts expanded well. The monthly review led to tighter free limits, new upgrade prompts and more PQL coverage for larger accounts. Revenue per sign-up recovered over two quarters, and the board received a clearer story.

The core metrics follow the journey: sign-ups; activation rate within a set window; median time to activation; week-4 retention of activated users; team formation rate for accounts with two or more active users; free-to-paid or trial-to-paid conversion within 30, 60 or 90 days; revenue per sign-up; expansion of paying accounts; logo and revenue retention; net revenue retention a year on; PQL conversion; and CAC payback.

The overview dashboard shows each metric for this month, last month, the same month last year and the target, with a note explaining any change.

A cohort table lists sign-up months with sign-ups, activation, week-4 retention, paid conversion by day 90 and revenue per sign-up. When activation holds steady while conversion falls across recent cohorts, the problem points at the free model or upgrade prompts rather than onboarding.

Slowdowns follow patterns. Sign-ups up and revenue flat points to activation or conversion. Stable activation with falling conversion points to the free model or prompts. Stable conversion with falling revenue retention points to retention or expansion. Strong self-serve with stuck large accounts points to sales assist and PQL coverage.

User and account metrics answer different questions: activation and weekly active users show whether people get value; active users per account and team formation show whether usage spreads; paid accounts, seats, expansion and net revenue retention show whether revenue grows.

Definitions are written down once and shared: which event counts as activation, the time window, whether a metric is measured for users or accounts and how trials and free plans are treated. Data quality checks confirm event tracking, identity across devices and account mapping between product, billing and CRM before anyone trusts a dashboard. Self-serve and sales-assisted motions are reported separately for conversion, deal size and retention, so the team can see where humans add value and whether sales is taking credit for accounts that would have upgraded anyway. Benchmarks start with your own history, because external figures vary widely by product and definition. The review rhythm runs weekly team metrics, a monthly PLG review across product, growth, marketing and sales and a quarterly board view. Each review records its decision, the metric it targets, the owner and the date to check results in a decision log, which over time shows which changes moved PLG metrics and stops teams repeating failed ideas. When the activation definition changes, past cohorts are restated or the break is clearly marked; long conversion cycles use 90 or 180-day windows with leading indicators; seasonal products compare cohorts year on year; and companies with several products keep separate funnels with a shared account view.

Changelog

1.0.0, 10 October 2026: first release.

Use, adapt and share this skill under CC BY 4.0: credit Afeez Adebayo and link to this page.

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