Plg Metrics

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:

1. What is your business model? (freemium, free trial, open-source, reverse trial, usage-based)
2. What is your primary growth loop? (viral, content-led, sales-assisted, product-led)
3. What is your product's core value action? (the thing users do that delivers value)
4. Who is your ideal user vs. buyer? (same person or different?)
5. What is your current stage? (pre-PMF, early growth, scaling, mature)
6. Do you have a sales team layered on top of PLG? (pure PLG vs. product-led sales)
7. What analytics tools do you currently use?
8. 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.

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.

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.

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

1. List the 5-10 most important actions in your product
2. Assign weights based on correlation with retention (use regression analysis)
3. Define thresholds for each action (e.g., "3+ projects created = 10 points")
4. Sum weighted scores and normalize to 0-100
5. 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.

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.

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.

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

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

North Star Examples by Product Type

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

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

1. Establish baselines: Measure current state for at least 4-8 weeks to establish stable baselines
2. Benchmark comparison: Compare your metrics against the benchmarks above and category-specific data
3. Gap analysis: Identify your largest gaps between current state and benchmarks
4. Prioritize: Focus on the 2-3 metrics with the largest gap AND the highest impact on your North Star
5. 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)
6. Decompose: Break the target into weekly milestones so you can track progress
7. 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