Your product analytics dashboard has 47 metrics. Leadership asks "how's growth?" and you're not sure which number to share.
Daily active users are up 15%. But signup-to-activation rate is down 8%. Free-to-paid conversion is flat. Retention improved slightly. Revenue grew but below target.
Which metrics actually matter? Which are vanity numbers that look good but don't drive business outcomes?
This is the product analytics challenge for PLG companies: traditional SaaS metrics (MRR, churn, CAC) still matter, but product-led growth adds a layer of product usage metrics that determine whether those business metrics succeed or fail.
After building analytics stacks for multiple PLG companies, I've learned: teams that track the right product metrics and connect them to business outcomes grow 3-5x faster than teams drowning in dashboards full of vanity metrics.
Here's which metrics actually drive PLG growth decisions.
The PLG Metrics Hierarchy
Not all metrics are created equal. Structure them in hierarchy:
Tier 1: North Star Metrics (The One Thing)
Your North Star metric captures the core value your product delivers. When this metric goes up, everything else—revenue, retention, growth—follows.
Characteristics of good North Star metrics:
- Reflects value delivered to customers (not just company revenue)
- Measurable across all customer segments
- Grows as customers get more value
- Leadsindicator of retention and monetization
Examples by product type:
Communication tools: Messages sent, active conversations
- Slack: Messages sent across workspaces
- Why it works: More communication = more value = better retention
Collaboration tools: Shared documents, collaborative edits
- Figma: Files with multi-user collaboration
- Why it works: Collaboration = team value = expansion
Analytics platforms: Active dashboards viewed, insights generated
- Mixpanel: Weekly active insights users
- Why it works: Insights usage = value realization = retention
Content creation: Content pieces created and shared
- Canva: Designs created and downloaded
- Why it works: Creation + distribution = value delivery
Productivity tools: Tasks completed, workflows automated
- Asana: Tasks completed weekly
- Why it works: Completion = productivity = perceived value
Tier 2: Growth Metrics (The Funnel)
These track how users move through your product-led funnel:
Signups → Activation → Engagement → Monetization → Expansion
Each stage needs specific metrics:
Signups metrics:
- New signups per week
- Signup rate by channel
- Cost per signup by channel
Activation metrics:
- Activation rate (% reaching value milestone)
- Time to activation
- Activation by user segment
Engagement metrics:
- Daily/Weekly/Monthly active users
- Feature adoption rates
- Session frequency and length
Monetization metrics:
- Free-to-paid conversion rate
- Time from signup to paid conversion
- Average contract value by tier
Expansion metrics:
- Net revenue retention (NRR)
- Expansion MRR
- User count growth within accounts
Tier 3: Diagnostic Metrics (The Why)
These help you understand why Tier 1 and 2 metrics move:
- Drop-off rates at each onboarding step
- Feature usage patterns by customer segment
- Support ticket volume and resolution time
- NPS scores and customer satisfaction
- Product performance metrics (load time, errors)
Diagnostic metrics don't drive strategy alone but help diagnose problems in growth metrics.
The Must-Track PLG Metrics
Metric 1: Activation Rate
Formula: (Users who reached activation milestone / Total signups) × 100
Why it matters: Activated users retain at 3-5x higher rates than non-activated users. Improving activation rate is the highest-leverage growth work.
How to use it:
- Track by signup source to identify quality channels
- Segment by user persona to find best-fit customers
- Measure impact of onboarding changes
Target: 25-50% depending on product complexity
Metric 2: Product Qualified Lead (PQL) Score
Formula: Composite score based on engagement + intent + fit signals
Why it matters: Identifies which free users are ready for sales outreach or self-serve upgrade prompts.
How to use it:
- Route high-scoring users to sales
- Trigger automated upgrade campaigns
- Prioritize product improvements for high-PQL segments
Target: 10-20% of activated users become PQLs monthly
Metric 3: Free-to-Paid Conversion Rate
Formula: (Free users who upgraded to paid / Total activated free users) × 100
Why it matters: Direct driver of revenue growth. Separates successful freemium from failed freemium.
How to use it:
- Test pricing and packaging changes
- Measure impact of upgrade prompts
- Identify segments with high/low conversion
Target: 15-30% within 90 days for freemium, 30-50% for free trials
Metric 4: Time to Value
Formula: Median time from signup to activation milestone
Why it matters: Faster activation = better retention. Every hour of delay = higher abandonment.
How to use it:
- Identify friction points in onboarding
- Test onboarding flow variations
- Benchmark against category standards
Target: Under 10 minutes for simple products, under 30 minutes for complex products
Metric 5: Weekly Active Users (WAU)
Formula: Unique users who performed key action in past 7 days
Why it matters: More reliable than DAU for B2B products. Indicates habit formation.
How to use it:
- Track WAU growth rate
- Calculate WAU/MAU ratio (stickiness)
- Monitor changes after product updates
Target: WAU/MAU ratio above 30% indicates good engagement
Metric 6: Net Revenue Retention (NRR)
Formula: ((Starting MRR + Expansion - Churn - Contraction) / Starting MRR) × 100
Why it matters: Captures expansion revenue and churn in one number. NRR above 100% = revenue grows even without new customers.
How to use it:
- Track cohort-based NRR
- Identify expansion opportunities
- Measure customer success impact
Target: 110-130% for successful PLG companies
Metric 7: Viral Coefficient (K)
Formula: (Invitations sent per user) × (Conversion rate of invitations)
Why it matters: K > 1 = exponential growth. K < 1 but > 0 still amplifies other growth channels.
How to use it:
- Test referral program variations
- Improve invitation conversion rates
- Identify most viral user segments
Target: K = 0.3-0.7 for most PLG products (K > 1 is rare)
The Analytics Instrumentation Plan
You can't track what you don't measure. Set up tracking for:
Event tracking:
- User actions (signups, logins, feature usage)
- Workflow completions
- Social actions (shares, invitations, collaborations)
- Monetization events (trial starts, upgrades, expansions)
User properties:
- Demographics (role, company size, industry)
- Product usage (features used, frequency, recency)
- Journey stage (trial, free, paid, enterprise)
- Engagement scores
Account properties:
- Firmographics (industry, size, location)
- Product usage aggregate (total users, total activity)
- Revenue metrics (MRR, contract value, payment status)
- Health scores
Tool stack:
- Product analytics: Mixpanel, Amplitude, or Heap
- Data warehouse: Snowflake, BigQuery, or Redshift
- Business intelligence: Mode, Looker, or Metabase
- CRM integration: Sync product data to Salesforce/HubSpot
Building the PLG Analytics Dashboard
Create dashboards for different audiences:
For Product Team:
- Activation funnel with drop-off rates
- Feature adoption trends
- User retention cohorts
- Product experiment results
For Marketing Team:
- Signup volume by channel
- Activation rate by source
- PQL generation rates
- Content attribution to signups
For Sales Team:
- PQL pipeline and scores
- Account expansion opportunities
- Product usage by account
- Engagement trends for key accounts
For Leadership:
- North Star metric trend
- Growth funnel metrics
- Revenue and retention metrics
- Key experiments and their impact
Common PLG Analytics Mistakes
Mistake 1: Tracking Everything
47 metrics on your dashboard = decision paralysis. Focus on 5-7 key metrics that drive decisions.
Mistake 2: Vanity Metrics Over Actionable Metrics
"Total signups all-time" feels good but doesn't drive action. "Activation rate by signup source this month" drives optimization.
Mistake 3: Not Segmenting Data
Aggregate metrics hide insights. Segment by user persona, signup source, company size, and engagement level.
Mistake 4: Ignoring Statistical Significance
Small sample sizes create noise. Don't react to weekly fluctuations in metrics based on 20 users.
Mistake 5: Metrics Without Context
"Activation rate is 35%" means nothing without context. Compared to what? Last month? Industry benchmarks? Different segments?
The Analytics Review Cadence
Daily: Check North Star metric and critical alerts (unexpected drops, system issues)
Weekly: Review growth funnel metrics, activation rates, PQL pipeline
Monthly: Deep-dive cohort analysis, experiment results, metric trends
Quarterly: Revisit metric definitions, update dashboard priorities, benchmark against goals
Turning Metrics Into Action
Analytics don't drive growth—actions based on analytics drive growth.
Low activation rate?
- Map onboarding funnel drop-offs
- Interview users who abandoned
- Test simplified onboarding flows
Low free-to-paid conversion?
- Analyze usage patterns of converters vs. non-converters
- Test different upgrade prompts
- Adjust pricing or packaging
High churn?
- Identify early warning signals in product usage
- Build customer health scores
- Intervene with at-risk customers
Flat viral coefficient?
- Test referral incentives
- Improve invitation conversion flows
- Make sharing easier and more valuable
Every metric should connect to potential improvements.
The Reality
PLG companies live or die by product analytics. You can't optimize what you don't measure. But measuring everything creates noise that obscures signal.
The best PLG teams:
- Choose North Star metric carefully
- Track core growth funnel metrics religiously
- Segment everything to find insights
- Connect metrics to business outcomes
- Use data to drive experiments
- Measure experiment results rigorously
That's the analytics game. And it's the foundation of sustainable PLG growth.