What Microsoft Clarity Shows You (and What It Doesn’t)
Most product teams I talk to start with Microsoft Clarity. And for good reason: it’s free, quick to set up, and gives you the basics right out of the box. But once you’re building more complex products or trying to understand why users behave the way they do, you’ll hit its limits fast.
Where Microsoft Clarity Shines
✅ Free and easy to install: no budget conversations needed to get started
✅ Beginner-friendly dashboards: click heatmaps, rage-click tracking, and replays in one place
✅ Lightweight: doesn’t slow down your site
✅ Privacy-first: no PII, GDPR/CCPA compliant by default
If your goal is to watch some replays, see where people click, and get a basic feel for behavior on your site, Clarity is great. It’s also a good option if you’re just starting out and don’t want to commit to heavier tooling.
Where Microsoft Clarity Falls Short
❌ No awareness of your actual UI: it sees “clicks” but not “invite teammate button” or “checkout flow”
❌ Only shows what happened, not why: you can’t really test hypotheses on whether friction was a bug, design issue, or user knowledge gap
❌ Dashboards are generic: no view of your Golden Path or conversion-critical flows
❌ Limited filtering/segmentation: tricky once you have multiple personas or product areas
For simple websites or lightweight analytics, that’s fine. But as soon as you’re building a product with real workflows, you need more context.
What Autoplay Brings to the Table
This is where Autoplay goes further. We built it for teams that don’t just want to watch what users did, but want to understand intent and test hypotheses.
🎯 UI-aware analysis: we understand your buttons, flows, and product language, not just click coordinates
🎯 Hypothesis testing built-in: quickly see if friction comes from a bug, a UX gap, or a user decision
🎯 Golden Path tracking: define your critical flows (onboarding, campaign creation, checkout) and instantly see where users deviate or hesitate
🎯 Clustering & patterns: surface recurring issues at scale without having to tag sessions one by one
🎯 AI-guided investigation: instead of manually guessing, let the system suggest likely causes and hypotheses
🎯 Real-time friction signals: hesitation scores, rage clicks, drop-offs, all tied back to the flow they happened in
🎯 Faster product feedback loops: filters, clusters, and suggested issues make testing and validating changes quicker
How to Use Both
For some teams, the right answer isn’t either/or.
Use Clarity if you just want quick wins: heatmaps, replays, and lightweight dashboards. It’s especially useful for marketing sites and simple flows.
Use Autoplay once you’re asking deeper questions:
Why are users abandoning onboarding halfway through?
What workarounds are users taking to complete a workflow?
Is this issue a bug, a design gap, or just low user knowledge?
What are users trying to do - and is the product helping them accomplish their goal?
From Observation to Hypothesis Testing
Clarity is a great place to start. It shows you what happened, gives you heatmaps and replays, and helps you spot friction in the first place.
But the next step is moving from observation to understanding. That’s where Autoplay shifts the lens.
Instead of just asking “where did users drop off?”, you can begin asking “why did they?”
Autoplay treats every friction point as the start of a hypothesis. Maybe users hesitate on a teammate invite because the value isn’t clear. Maybe they abandon a campaign setup because the flow feels too complex.
Whatever the theory, Autoplay gives you the structure to test it: surfacing all similar sessions, clustering patterns across personas, and tracking whether the issue improves once you ship a change.
That cycle - observe, hypothesize, test, validate - is what turns raw session data into real product decisions.
Use Clarity to get the lay of the land. Use Autoplay when you’re ready to prove or disprove why things happen, and to keep closing the loop as your product evolves.