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When the user wants to define PLG metrics, build a growth dashboard, or set KPI targets -- including activation rate, free-to-paid conversion, NRR, or North Star metric...
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.
Before building your metrics framework, answer these questions:
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.
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:
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:
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
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.
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%
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 |
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
| 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 |
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]
The executive dashboard answers: "Is the business healthy and growing?"
Section 1 -- Headlines
Section 2 -- Funnel Health
Section 3 -- Unit Economics
Section 4 -- Leading Indicators
Growth Team Dashboard:
Product Team Dashboard:
Revenue Team Dashboard:
Customer Success Dashboard:
| 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.
Metrics that look impressive but do not drive decisions.
Fix: Replace with rate-based or active-user-based metrics.
Optimizing a single metric at the expense of the whole system.
Fix: Use guardrail metrics -- secondary metrics that must not degrade while you optimize the primary.
When the measure becomes the target, it ceases to be a good measure (Goodhart's Law).
Fix: Audit metric definitions regularly. Use composite metrics that are harder to game. Separate the metric from incentive structures.
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.
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]
When using this skill, produce two deliverables:
A comprehensive document defining every metric the company tracks, using the metric definition template above. Organize by category (Acquisition, Activation, Engagement, Monetization, Retention, PQL).
A specification for building dashboards, including:
Related skills: activation-metrics, retention-analysis, growth-modeling, product-analytics
