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PLG Experimentation: How to Optimize Faster
Filippo Ferri
March 02, 2025
👋 Welcome to Momentum Inspired!
The best Product-Led Growth (PLG) companies don’t rely on guesswork—they experiment relentlessly to optimize activation, engagement, and monetization.
The right PLG experiments drive higher conversion rates without adding friction.
A structured testing process helps identify what works before scaling.
Small changes compound over time, leading to massive growth gains.
But what should you test? And how do you run experiments efficiently?
Let’s break it down! 🚀
The 5-Step PLG Experimentation Framework
Identify the Bottleneck → Where Are Users Dropping Off?
Before running experiments, pinpoint where your product is leaking users.
📌 Look at these key areas:
Activation Rate: Are users completing the first key action?
Retention Rate: Are users coming back after Week 1?
Conversion Rate: Are free users upgrading at the right time?
✅ Your Move: Use analytics tools (Mixpanel, Amplitude, or GA4) to spot drop-offs.
Form a Hypothesis → What Small Change Could Improve Metrics?
🚀 Good PLG experiments start with a clear hypothesis:
👉 “If we [change X], then we expect [Y] to happen because [Z].”
📌 Example:
“If we reduce the number of onboarding steps, then activation will increase because users will reach value faster.”
“If we highlight premium features in-app, then more free users will upgrade because they’ll see the benefits.”
✅ Your Move: Test one variable at a time to keep results clear.
Run a Small-Scale Test → Start with a Limited Audience
📌 Best Ways to Experiment:
A/B Testing: Show two versions of a feature to different users.
Feature Flags: Test changes on a small % of users before a full rollout.
Cohort Analysis: Compare user behavior before & after a change.
📌 Example:
Slack A/B tested different invite flows → The winning flow increased team sign-ups by 15%.
✅ Your Move: Start small → Test on 10-20% of users before scaling.
Analyze Results → Did the Experiment Improve Metrics?
🚀 Key Questions to Ask:
Did activation, retention, or conversions improve?
Were there unintended side effects?
Do the results justify scaling the change?
📌 Example:
Dropbox tested a new onboarding sequence → It decreased friction and reduced referrals.
✅ Your Move: Always look for hidden trade-offs before making a final decision.
Iterate, Scale, or Kill the Test → What’s Next?
🔹 If the experiment worked → Roll it out to 100% of users.
🔹 If the test failed → Learn from it & try a new variation.
🔹 If it was inconclusive → Run a more extended test or refine the hypothesis.
📌 Example:
Notion tested a freemium pricing tweak. The initial test failed, but a second iteration doubled conversions.
✅ Your Move: Keep a log of past experiments—learn from each one to refine future tests.
Case Study: How Duolingo Used PLG Experimentation to 10X Retention
Duolingo optimized its onboarding, notifications, and gamification through rapid testing.
🔹 The Challenge: Users churned after 3-5 days.
🔹 The Fix:
✅ A/B tested onboarding screens → Simplified the first experience.
✅ Gamified streaks & push notifications → Increased daily engagement.
✅ Optimized reward system → More users completed lessons.
🎯 The Result? Daily active users (DAU) skyrocketed, reducing churn by 50%.
🚀 Your Takeaway: Small PLG experiments compound over time—focus on incremental gains!
Quick Wins to Improve Your PLG Experimentation Today
✅ Audit your onboarding funnel → Where do users drop off?
✅ Test a new upsell trigger → Can free users be nudged at a better moment?
✅ Run a simple A/B test → Start with a tiny change to increase conversions.
✅ Track every experiment → Keep a PLG Experiment Log to learn from past tests.
📣 Let’s Talk!
What’s the most successful PLG experiment you’ve run? Reply to this email—I’d love to feature your insights in a future issue!
Until next time,
Filippo
P.S. Know someone optimizing their product? Share this issue with them! 🚀
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