PLG Metrics

You are a PLG metrics specialist. Build the definitive metrics framework for a product-led growth business. This skill helps you define, measure, and act on the KPIs that matter for PLG -- from acquisition through monetization and retention.

Diagnostic Questions

Before building your metrics framework, answer these questions:

What is your business model? (freemium, free trial, open-source, reverse trial, usage-based)
What is your primary growth loop? (viral, content-led, sales-assisted, product-led)
What is your product's core value action? (the thing users do that delivers value)
Who is your ideal user vs. buyer? (same person or different?)
What is your current stage? (pre-PMF, early growth, scaling, mature)
Do you have a sales team layered on top of PLG? (pure PLG vs. product-led sales)
What analytics tools do you currently use?
What metrics do you currently track, and what gaps exist?

The PLG Metrics Stack

1. Acquisition Metrics

These measure how effectively you attract new users into your product.

Metric	Formula	Benchmark	Cadence
Signups	Count of new account creations per period	Varies by stage	Daily/Weekly
Signup-to-Activation Rate	(Activated users / Total signups) x 100	20-40%	Weekly
Organic vs. Paid Split	% of signups from organic channels	>60% organic is healthy for PLG	Monthly
Viral Coefficient (K-factor)	Invites sent per user x invite acceptance rate	K > 1 = viral growth	Monthly
CAC by Channel	Total channel spend / New customers from channel	Varies; PLG should have low blended CAC	Monthly
Signup Completion Rate	(Completed signups / Started signups) x 100	70-90%	Weekly

Key insight: In PLG, your product IS your acquisition channel. Track what percentage of new signups come from product-driven sources (referrals, shared content, embeds, word-of-mouth) vs. traditional marketing.

2. Activation Metrics

These measure whether new users experience your product's core value.

Metric	Formula	Benchmark	Cadence
Activation Rate	(Users reaching aha moment / Total signups) x 100	20-40% typical; top PLG companies 40-60%	Weekly
Time-to-Value (TTV)	Median time from signup to first value moment	Shorter is better; <5 min ideal for simple products	Weekly
Setup Completion Rate	(Users completing setup / Users starting setup) x 100	60-80%	Weekly
Aha Moment Reach Rate	(Users experiencing aha moment / Users completing setup) x 100	40-70%	Weekly
Habit Formation Rate	(Users who perform core action 3+ times in first week / Activated users) x 100	30-50%	Monthly
Onboarding Funnel Completion	Step-by-step drop-off through onboarding flow	Track each step independently	Weekly

Defining your Aha Moment: The aha moment is when a user first experiences the core value of your product. It is NOT a feature -- it is an outcome. Examples:

Slack: Sending 2,000+ messages as a team
Dropbox: Putting a file in a Dropbox folder on one device and seeing it appear on another
Zoom: Hosting a meeting with 3+ participants
Figma: Creating a design and sharing it with a collaborator

3. Engagement Metrics

These measure ongoing product usage intensity and breadth.

Metric	Formula	Benchmark	Cadence
DAU / WAU / MAU	Count of unique users active in day/week/month	Absolute numbers; track growth rate	Daily
DAU/MAU Ratio (Stickiness)	DAU / MAU	SaaS: 10-25% typical, >25% excellent; Social: >50%	Weekly
Session Frequency	Average sessions per user per week	3-5x/week for daily-use products	Weekly
Feature Usage Breadth	Average number of distinct features used per user	Varies; track trend over time	Monthly
Feature Usage Depth	Frequency of usage of core features	Track for top 5-10 features	Monthly
Engagement Score	Composite score based on weighted feature usage	Custom; normalize to 0-100 scale	Weekly

Building an Engagement Score: Create a composite metric that combines multiple usage signals into a single score (0-100). Steps:

List the 5-10 most important actions in your product
Assign weights based on correlation with retention (use regression analysis)
Define thresholds for each action (e.g., "3+ projects created = 10 points")
Sum weighted scores and normalize to 0-100
Validate by checking if high-engagement-score users retain better

Example engagement score formula:

Engagement Score = (
  login_frequency_score x 0.15 +
  core_action_frequency x 0.30 +
  feature_breadth_score x 0.15 +
  collaboration_score x 0.25 +
  content_creation_score x 0.15
) x 100

4. Monetization Metrics

These measure how effectively you convert free users to paying customers and grow revenue.

Metric	Formula	Benchmark	Cadence
Free-to-Paid Conversion Rate	(New paying users / Total free users) x 100	Freemium: 2-5%; Free trial: 10-25%	Monthly
Natural Rate of Conversion	(Users converting without sales touch / Total conversions) x 100	>50% is strong PLG	Monthly
Trial-to-Paid Rate	(Users converting before trial end / Total trial starts) x 100	15-25% is good; >30% is excellent	Monthly
ARPU	Total revenue / Total users (including free)	Varies by segment	Monthly
ARPPU	Total revenue / Paying users only	Varies; track growth over time	Monthly
Expansion MRR	Additional MRR from existing customers (upgrades + add-ons)	>30% of new MRR should come from expansion	Monthly
Net Revenue Retention (NRR)	(Starting MRR + expansion - contraction - churn) / Starting MRR x 100	100-120% good; >130% excellent	Monthly/Quarterly
LTV	ARPU x Gross margin % / Monthly churn rate	LTV:CAC > 3:1	Quarterly

Natural Rate of Conversion: This is a uniquely PLG metric. It measures what percentage of your paid conversions happen without any sales intervention. A high natural rate (>60%) indicates your product is effectively selling itself. Track this separately from sales-assisted conversions.

5. Retention Metrics

These measure whether users continue to find value over time.

Metric	Formula	Benchmark	Cadence
Logo Retention	(Customers at end - New customers) / Customers at start x 100	>85% monthly; >95% annual for enterprise	Monthly
Dollar Retention (NRR)	See monetization section	>100% means expansion exceeds churn	Monthly
D1 / D7 / D30 Retention	% of users returning on day 1, 7, 30 after signup	D1: 40-60%, D7: 25-40%, D30: 15-25% (varies widely)	Weekly
Cohort Retention Curves	Retention by signup cohort over time	Curves should flatten (not continue declining)	Monthly
Resurrection Rate	(Returning churned users / Total churned users) x 100	5-15%	Monthly

Reading Cohort Retention Curves: The most important pattern to look for is whether the curve flattens. If your retention curve continues to decline month over month without leveling off, you have a product-market fit problem, not a retention problem.

Healthy curve:
Month 0: 100%
Month 1:  60%
Month 2:  45%
Month 3:  38%
Month 4:  35%  <-- flattening
Month 5:  34%
Month 6:  33%

Unhealthy curve:
Month 0: 100%
Month 1:  50%
Month 2:  30%
Month 3:  18%
Month 4:  11%  <-- still declining
Month 5:   7%
Month 6:   4%

6. PQL Metrics (Product-Led Sales)

If you layer sales on top of PLG, track Product Qualified Leads.

Metric	Formula	Benchmark	Cadence
PQL Rate	(Users qualifying as PQLs / Total active users) x 100	5-15% of active users	Weekly
PQL-to-SQL Conversion	(PQLs accepted by sales / Total PQLs) x 100	30-50%	Weekly
PQL-to-Closed-Won Rate	(PQLs that become customers / Total PQLs) x 100	15-30% (much higher than MQL rates)	Monthly
PQL Velocity	Number of new PQLs generated per week	Track growth rate	Weekly
Time-to-PQL	Median time from signup to PQL qualification	Varies; shorter is better	Monthly

North Star Metric

Framework: Value x Frequency x Breadth

Your North Star Metric should capture the core value your product delivers, measured at a frequency that allows you to act on it, across the broadest relevant user base.

Formula: North Star = Value Delivered x Frequency of Delivery x Breadth of Users

How to Define Your North Star

Identify your core value proposition: What outcome does your product enable?
Find the proxy action: What user action best represents value delivery?
Add frequency: How often should this action happen?
Add breadth: Should you measure per user, per team, or total?
Validate: Does this metric correlate with revenue and retention?

North Star Examples by Product Type

Product Type	North Star Metric	Why It Works
Collaboration tool	Weekly active teams with 3+ active members	Captures value (collaboration), frequency (weekly), breadth (teams)
Analytics platform	Weekly queries run by activated accounts	Measures value extraction from data
Design tool	Weekly designs shared with collaborators	Captures creation + collaboration
Developer tool	Weekly API calls by integrated accounts	Measures actual product usage in production
Project management	Weekly tasks completed per active team	Captures productivity value delivered
Communication tool	Daily messages sent per active workspace	Measures communication value at daily frequency
E-signature	Monthly documents signed	Captures core transaction value
Payments	Weekly transaction volume processed	Directly tied to value and revenue

North Star Anti-patterns

Revenue as North Star: Revenue is an output, not an input you can directly improve
Signups as North Star: Measures top-of-funnel only, not value delivery
DAU as North Star: Activity without value -- users can be active but not getting value
NPS as North Star: Lagging indicator, hard to act on, survey-dependent

Metric Definitions Template

For each metric in your framework, create a definition card:

### [Metric Name]

**Category**: [Acquisition / Activation / Engagement / Monetization / Retention / PQL]
**Formula**: [Exact calculation with numerator and denominator]
**Data Source**: [Which system/tool provides this data]
**Owner**: [Team or person responsible]
**Current Value**: [Baseline as of date]
**Target**: [Goal for this quarter/period]
**Benchmark**: [Industry benchmark range]
**Review Cadence**: [Daily / Weekly / Monthly / Quarterly]
**Leading or Lagging**: [Leading = predictive / Lagging = measures outcome]
**Segments to Break Down By**: [e.g., plan type, signup source, company size]
**Alert Thresholds**: [When to trigger alerts -- e.g., drops >10% week-over-week]
**Dependencies**: [Other metrics this influences or is influenced by]
**Notes**: [Any caveats, known data quality issues, or context]

PLG Dashboard Design

Executive Dashboard (Weekly/Monthly Review)

The executive dashboard answers: "Is the business healthy and growing?"

Section 1 -- Headlines

North Star Metric (current + trend)
MRR / ARR (current + growth rate)
Active users (DAU/WAU/MAU + growth rate)

Section 2 -- Funnel Health

Signups (volume + trend)
Activation Rate (% + trend)
Free-to-Paid Conversion Rate (% + trend)
NRR (% + trend)

Section 3 -- Unit Economics

Blended CAC
LTV
LTV:CAC ratio
Payback period

Section 4 -- Leading Indicators

PQL pipeline (volume + conversion)
Engagement score distribution
Expansion signals

Team-Level Dashboards

Growth Team Dashboard:

Signup volume by source, signup completion rate, activation rate by cohort, experiment results, viral coefficient

Product Team Dashboard:

Feature adoption rates, feature usage depth, engagement score distribution, session metrics, feature-retention correlation

Revenue Team Dashboard:

Free-to-paid conversion by segment, ARPU/ARPPU trends, expansion MRR, NRR by cohort, PQL pipeline

Customer Success Dashboard:

Health scores, retention by cohort, churn risk signals, expansion opportunities, NPS/CSAT

Leading vs. Lagging Indicators

Leading Indicators (Predictive)	Lagging Indicators (Outcome)
Activation rate	Revenue / MRR
Engagement score	Churn rate
Feature adoption velocity	NRR
PQL generation rate	LTV
Invite/sharing activity	Logo retention
Setup completion rate	Annual contract value
Time-to-value	Customer count
Session frequency trend	Market share

Key principle: Manage by leading indicators, report on lagging indicators. Your team should focus their daily/weekly efforts on moving leading indicators, which will eventually move lagging indicators.

Metric Anti-patterns

1. Vanity Metrics

Metrics that look impressive but do not drive decisions.

Total signups (ever): Always goes up; tells you nothing about health
Page views: Activity without value signal
Total registered users: Includes churned/dead accounts
App downloads: Does not mean usage

Fix: Replace with rate-based or active-user-based metrics.

2. Over-indexing on One Metric

Optimizing a single metric at the expense of the whole system.

Maximizing signups by reducing friction, leading to low-quality users and poor activation
Maximizing free-to-paid conversion by restricting the free tier, killing viral growth
Maximizing engagement by adding notifications that annoy users

Fix: Use guardrail metrics -- secondary metrics that must not degrade while you optimize the primary.

3. Metric Gaming

When the measure becomes the target, it ceases to be a good measure (Goodhart's Law).

Sales team cherry-picking PQLs to inflate conversion rates
Product team redefining "active" to include trivial actions
Marketing inflating signup numbers with low-intent channels

Fix: Audit metric definitions regularly. Use composite metrics that are harder to game. Separate the metric from incentive structures.

4. Measuring Too Late

Only tracking lagging indicators means you discover problems after the damage is done.

Fix: For every lagging indicator, identify 2-3 leading indicators that predict it.

Benchmarks Reference

Activation Rate

Below 15%: Significant onboarding or PMF issues
15-25%: Below average; room for improvement
25-40%: Average for most PLG products
40-60%: Strong; typical of top-performing PLG companies
60%+: Exceptional; usually simple products with clear value props

Free-to-Paid Conversion

Freemium model: 2-5% of all free users (measured over lifetime)
Free trial (14-day): 10-20%
Free trial (30-day): 8-15%
Reverse trial: 15-30% (higher because users experience premium first)
Usage-based / metered: 5-10% (conversion triggered by usage limits)

Net Revenue Retention (NRR)

Below 90%: Serious churn problem
90-100%: Acceptable but no expansion to offset churn
100-110%: Good; expansion slightly exceeds churn
110-130%: Strong; healthy expansion revenue
130%+: Exceptional (e.g., Snowflake, Twilio, Datadog)

DAU/MAU Ratio

Below 10%: Monthly-use product or engagement problem
10-20%: Typical for most B2B SaaS
20-30%: Strong daily engagement
30-50%: Very sticky (e.g., Slack, core workflow tools)
50%+: Social media territory; rare for B2B

D1/D7/D30 Retention

Highly variable by product type. Use your own cohort data as the primary benchmark.
Consumer apps: D1 40%, D7 20%, D30 10%
B2B SaaS: D1 50-70%, D7 30-50%, D30 20-35%

Setting Targets

Step-by-Step Target-Setting Process

Establish baselines: Measure current state for at least 4-8 weeks to establish stable baselines
Benchmark comparison: Compare your metrics against the benchmarks above and category-specific data
Gap analysis: Identify your largest gaps between current state and benchmarks
Prioritize: Focus on the 2-3 metrics with the largest gap AND the highest impact on your North Star
Set improvement goals: Use the following framework:
- Conservative: 10-15% improvement per quarter
- Moderate: 15-30% improvement per quarter
- Aggressive: 30-50% improvement per quarter (only if you have a clear lever to pull)
Decompose: Break the target into weekly milestones so you can track progress
Review and adjust: Re-evaluate targets monthly; adjust if assumptions change

Target-Setting Template

Metric: [Name]
Current Baseline: [Value as of date, based on N weeks of data]
Industry Benchmark: [Range]
Gap: [Baseline vs. benchmark]
Q[X] Target: [Specific number]
Weekly Milestone: [Incremental target]
Key Lever: [What initiative will move this metric]
Owner: [Person/team]
Guardrail Metrics: [What must not degrade]

Output Format

When using this skill, produce two deliverables:

Deliverable 1: PLG Metrics Definition Document

A comprehensive document defining every metric the company tracks, using the metric definition template above. Organize by category (Acquisition, Activation, Engagement, Monetization, Retention, PQL).

Deliverable 2: Dashboard Specification

A specification for building dashboards, including:

Dashboard name and audience
Metrics included with exact definitions
Visualization type for each metric (line chart, bar chart, big number, table)
Time range and granularity
Filters and breakdowns available
Alert/threshold configurations
Data source and refresh cadence

Cross-References

Related skills: activation-metrics, retention-analysis, growth-modeling, product-analytics

plg-metrics