PLG Metrics That Actually Matter: Beyond the Vanity Numbers

Your product has 1,000 metrics. Here are the 4 that actually predict growth and how to use them.

Nov 05, 2024

This is the second installment of the Product Led Growth focus I promised - and started - last week.

If you're drowning in metrics but starving for insights, you're not alone. While everyone talks about Product-Led Growth (PLG), few focus on the metrics that truly drive sustainable growth.

Let's cut through the noise and focus on what matters for your new PLG business!

The Problem with Traditional PLG Metrics

We've all been there: Monday morning executive meetings with impressive graphs of user signups, website visits, and trial starts. But these metrics often tell an incomplete—and sometimes misleading—story.

Take Dropbox's early days. They could have focused on their viral coefficient and raw signup numbers, which looked fantastic. Instead, they zeroed in on what percentage of users actually stored a file within their first session. This laser focus on meaningful activation, rather than vanity metrics, helped them build their $11B business.

The trap many founders fall into is optimizing for metrics that look good in pitch decks but don't correlate with sustainable growth. Let's focus instead on metrics that actually predict business success.

The Four Metrics That Move the Needle

While most teams track dozens of metrics, winning PLG companies focus relentlessly on these four. Here's why they matter and how market leaders use them.

1. Time to Value (TTV)

Example: Calendly

What makes Calendly's PLG strategy successful is its relentless focus on reducing friction to first value. Their approach focuses on:

Minimizing steps to first meeting scheduled
Simple user interface requiring minimal training
Instant utility without complex setup
Quick integration with existing calendars

Common TTV Patterns:

High-performing PLG companies typically get users to first value in under 5 minutes
Each additional step in onboarding typically reduces activation by 20-30%
Mobile-first products often achieve faster TTV than desktop-first solutions

2. Activation Quality Score (AQS)

Example: Notion

Notion's success stems from their deep understanding of activation patterns:

Individual users typically start with basic documents
Team activation involves sharing and collaboration
Power users progress to templates and databases
Enterprise adoption shows cross-department usage

Typical Activation Patterns:

Basic users: 1-2 core features in the first week
Power users: 3-5 core features in the first week
Team adoption: Multiple users active within the first month
Enterprise: Cross-functional usage within the first quarter

3. Product-Qualified Lead (PQL) Conversion Rate

Example: Slack

Slack's adoption typically follows this pattern:

Small team adoption starts with a single department
Usage spreads organically to adjacent teams
Integration usage indicates deeper implementation
Cross-team communication signals enterprise potential

Common PQL Patterns:

Teams with multiple active users in the first week show higher conversion rates
Integration adoption correlates strongly with long-term retention
Usage across departments typically indicates enterprise sale potential
Weekend usage often signals strong product-market fit

4. Net Dollar Retention (NDR) by Acquisition Channel

Example: MongoDB

MongoDB's growth patterns reveal:

Developer community adoption leads to organic growth
Bottom-up adoption often precedes enterprise deals
Strong correlation between early developer adoption and later enterprise expansion
Technical community engagement predicts long-term value

Typical Channel NDR Patterns:

Community-led channels often show higher NDR than paid acquisition
Product-educated customers typically expand more than sales-led customers
Technical users often drive deeper implementation and expansion
Referral channels frequently show superior retention rates

The critical insight at this point is that the most predictive indicator of long-term success for PLG SaaS businesses isn't just feature adoption - it's the speed, sequence, and depth of adoption. Users who progress through the different stages faster (especially in the first two weeks) show dramatically higher conversion and retention rates. Recognizing this, SaaS startups should monitor these patterns while designing onboarding pathways, structuring metrics and controls, along with planning intervention points to support users in moving past practical hurdles.

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Building Your PLG Metrics Framework

We know building a metrics framework that scales isn't theoretical physics, but it's not exactly a walk in the park either. Here are three battle-tested steps. No fluff, just a practical framework that grows with your company.

Step 1: Create Your Metrics Hierarchy 🎯

North Star: Choose one metric that best indicates sustainable growth
Leading Indicators: 2-3 metrics that predict North Star movement
Supporting Metrics: 3-5 diagnostic measures for troubleshooting

Step 2: Set Up Proper Tracking 📊

Invest in reliable analytics infrastructure
Ensure consistent data collection
Make data accessible to all stakeholders
Automate regular reporting

Step 3: Make Metrics Actionable ⚡

Assign metric owners
Set clear improvement targets
Create intervention playbooks
Review and adjust regularly

Common PLG Patterns to Watch

As SaaS companies increasingly embrace PLG, certain patterns have become reliable indicators of success. These recurring approaches help to identify opportunities, optimize product experience, and accelerate growth through user-driven adoption.

By effectively recognizing and implementing these patterns, a startup can create more predictable paths to scale while maintaining capital efficiency.

Here are three key patterns we've observed across successful PLG companies:

Adoption Patterns

Individual → Team → Department → Organization
Basic → Advanced → Custom use cases
Manual → Automated → Integrated workflows

Growth Patterns

Higher retention among teams vs individual users
Increased stickiness with each integration
Expansion correlating with feature adoption depth

Warning Patterns

Decreasing engagement after initial adoption
Limited spread beyond an initial team
Low usage of sticky features
Poor integration adoption rates

Key Takeaways

Focus on Value Delivery Look for patterns that indicate value realization, not just usage.
Quality Over Quantity Track depth of adoption rather than just breadth.
Predict Future Behavior Identify leading indicators that consistently predict growth.
Connect to Revenue Ensure (and measure) a clear correlation between usage patterns and revenue impact.

In any case, remember: the best metrics framework is one that your team really uses. Start small, focus on accuracy, and expand as needed.

Your PLG metrics should tell a story—not just about who your users are and what they're doing, but about where your product and business are headed. Choose your metrics wisely, measure them accurately, and act on them consistently.

Now, your turn: what metrics is your team tracking?

Sílvia Almeida

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