How to Use Data to Drive Better Decisions in PLG

👋 Welcome to Inspired Momentum!

In a Product-Led Growth (PLG) strategy, data isn’t just a nice-to-have—it’s the foundation for making informed decisions that drive acquisition, activation, retention, and expansion. But with so much data available, how do you know what to focus on?

In this issue, we’ll explore:

1. The key types of data that power PLG.

2. How to turn data into actionable insights.

3. A step-by-step approach to making data-driven decisions.

Let’s dive in!

📊 Why Data is Essential in PLG

PLG relies on users experiencing value directly from your product, which means tracking their behavior and engagement is critical. Data helps you:

✅ Identify friction points in onboarding.
✅ Optimize user experiences for higher activation.
✅ Improve retention by understanding why users churn.
✅ Prioritize product features based on actual usage.

Without a data-driven approach, you’re left making decisions based on guesswork rather than actual user behavior.

🔑 The 4 Types of Data That Drive PLG Success

1. Acquisition Data (Who’s Signing Up and How?)

   Key Metrics:

   • Traffic sources (Where are users coming from?)

   • Signup conversion rates (How many visitors create an account?)

   • Cost per acquisition (What’s the cost of acquiring new users?)

   💡 Actionable Insight: If conversion rates are low, optimize landing pages and signup flows to reduce friction.

2. Activation Data (How Fast Do Users Experience Value?)

   Key Metrics:

   • Time-to-Value (How quickly do users experience their first ‘aha’ moment?)

   • Activation rate (How many users complete key onboarding steps?)

   • Drop-off points (Where do users abandon the onboarding flow?)

   💡 Actionable Insight: If activation rates are low, simplify onboarding and highlight core product benefits earlier.

3. Retention & Engagement Data (Are Users Sticking Around?)

   Key Metrics:

   • Retention rate (What percentage of users return after X days?)

   • Churn rate (How many users stop using the product?)

   • Feature adoption (Which features are used most/least?)

   💡 Actionable Insight: If churn is high, analyze why users leave (via surveys or session recordings) and improve weak areas of the product experience.

4. Expansion & Revenue Data (How Does Growth Happen?)

   Key Metrics:

   • Product Qualified Leads (PQLs) (Users who reach a high likelihood of conversion)

   • Expansion revenue (Upsells, cross-sells, and upgrades)

   • Net Revenue Retention (How much revenue comes from existing customers?)

   💡 Actionable Insight: If expansion revenue is low, experiment with targeted upsell prompts based on user behavior.

📈 A Simple Framework for Data-Driven Decision-Making

1️⃣ Collect the Right Data – Ensure you’re tracking essential PLG metrics with tools like Amplitude, Mixpanel, or Google Analytics.
2️⃣ Analyze Trends & Patterns – Look for drop-off points, usage spikes, and retention curves.
3️⃣ Ask the Right Questions—Instead of asking, “Why is churn high?” ask, “What’s the last action users take before they leave?”
4️⃣ Run Experiments & Iterate – Test small changes (e.g., onboarding tweaks, feature placement) and measure the impact.
5️⃣ Share Insights Across Teams – Align product, marketing, and sales around key data findings to drive coordinated growth efforts.

🚀 Real-World Example: How Figma Used Data to Scale PLG

Figma analyzed user behavior and found that teams collaborating on multiple files early on had higher retention. They optimized their onboarding to:

✅ Encourage users to invite teammates immediately.
✅ Highlight real-time collaboration as a key benefit.
✅ Surface shared projects in the dashboard for quick access.

The Result: Higher activation rates, more muscular retention, and a viral growth loop fueled by collaboration.

Your Takeaway: Use data to identify what behaviors correlate with long-term engagement and optimize for those actions.

💡 Quick Wins: Start Using Data to Improve PLG Today

✅ Identify one key metric to track and improve this quarter.
✅ Review your onboarding funnel and look for drop-off points.
✅ Set up a simple user feedback loop to gather qualitative insights.

📣 Let’s Chat!

What’s the most significant data challenge in your PLG strategy? Reply to this email—I’d love to help troubleshoot and feature your insights in a future issue!

Until next time,
Filippo

P.S. Do you Know a fellow founder or product manager struggling with data? Share this issue and help them unlock data-driven growth!