Testing User Flow in UX

A few years back, I was working with an e-commerce client who was struggling with low conversion rates. We decided to take a deep dive into user behavior to identify pain points. Using Hotjar, we were able to see exactly how users were interacting with their website. We noticed that many users were dropping off during the checkout process. By analyzing heatmaps and user recordings, we identified areas where the checkout flow could be simplified. We used Google Optimize to test different checkout variations, such as reducing form fields and streamlining the payment process. These small UX improvements led to an 17% increase in conversions. Have you ever used user testing tools to identify and fix conversion bottlenecks on your website?

OpenOnco quality control: testing a closely integrated diagnostics database and codebase: LLM Data Review + UI Regression Testing. OpenOnco grew from prototype to production in about a month: 80+ diagnostic tests, complex filtering, PDF export, comparison tools. 12K lines of code. Manual QA stopped working, fortunately some smart software folks advised us. Here's our system: (1) Multi-LLM Data Verification Before each deploy, I run the full database through Claude, Grok, GPT-5, and Gemini 3. Each model reviews test data for: → Inconsistencies between related tests → Outdated info vs. current clinical guidelines → Missing fields that should be populated → Logical errors (FDA-approved test with no approval date) Different models catch different things. Claude finds logical inconsistencies. GPT-5 catches formatting. Grok flags outdated clinical data. Gemini spots missing cross-references. (2) Automated UI Regression Testing Regression testing: "Did my changes break something that was working?" For us this means testing actual user workflows — clicking buttons, filling forms, navigating between pages — and verifying the interface behaves correctly every time. We test the actual UI, not just components in isolation: → Filter interactions: Click "IVD Kit" filter → verify correct tests appear → click "MRD" category → verify intersection is correct → clear filters → verify all tests return → Test card workflows: Click test card → modal opens with correct data → click "Compare" → test added to comparison → open comparison modal → verify all fields populate → Search behavior: Type "EGFR" → verify matching tests surface → clear search → verify full list returns → Direct URL testing: Navigate to /mrd?test=mrd-1 → verify modal auto-opens with correct test → Navigate to /tds?compare=tds-1,tds-2,tds-3 → verify comparison modal loads with all three → PDF export: Generate comparison PDF → verify page count matches content → verify no repeated pages (caught a real bug where Page 1 rendered on every page) → Mobile responsiveness: Run full suite at 375px, 768px, 1024px, 1440px breakpoints We run these tests using Playwright — an open-source browser automation framework. It launches real browsers (Chrome, Firefox, Safari), executes user actions, and asserts outcomes. Tests run on every push via GitHub Actions; deploy is blocked if anything fails. Full suite takes ~4 minutes 🤯🤯🤯 The combination of LLM data review + real UI regression testing catches what unit tests miss: so far, hundreds of issues 👍🏼👍🏼👍🏼

UX Designers, So, you've started using AI to see if you can leverage it to amplify what you can do. The answer is yes, but... If you've never been part of the (SDLC) or (PDLC). You'll get through it, but it won't be easy and not to fun at first. If you're in a well established company with a huge design system. Suddenly adding in AI might make life a real pain. It depends on how adaptive the company and others are. If you're starting something from scratch. Well, now you can do whatever you want to. This is where the fun, frustration and learning comes in. Buckle Up.. To give you an example. I've been working on something and it's almost ready for people to test. I was going through and manually testing the user flows. As something was found. Claude inside of Cursor would find the issue after I point it out. It suggests a fix. I review and approve and continue from there. This was taking a lot of time as you might imagine. So, this morning at 2am with what felt like sand in my eyes. "There has to be a way I can automate this..?" Prompt: As you know. I've been testing the user flows manually, and we've been fixing the issues along the way. Do you know if a way that we can automate this without having to send out various emails, and just do this internally? When you find an issue it gets documented in a backlog and we then work those, and run the test again? I got answers. I selected one I liked (playwright) and combined it with ReactFlow so it was visual. Created a dashboard for it. Long story short. I can now run 100% automated user flow tests, see them in action in real-time, see where the issues are and then go fix them. All done in less than 6 hours and at $0 except for my time. So, can you build something like this with the help of AI? Yes, I did and it fully works. #ux #uxdesigner #uxdesign

Sometimes QA teams skip this test type. Yet it’s the one that impacts users the most. Here’s your quick Usability Testing Mini Guide: ✅ 1. Define clear usability goals Decide what “good” looks like. Measure task success rate, completion time, and satisfaction. ✅ 2. Pick the right method Moderated, unmoderated, or remote. Match the test to your goals and resources. ✅ 3. Use realistic user scenarios Focus on actual workflows like “checkout,” “apply filter,” or “create account.” ✅ 4. Recruit real users Get both new and experienced users to uncover different challenges. ✅ 5. Let them think aloud Silence speaks volumes. Watch where users hesitate or get stuck. ✅ 6. Track key metrics Completion time, number of retries, and error rates show real patterns. ✅ 7. Capture quotes and emotions A comment like “I can’t find the button” is pure gold for UX improvement. ✅ 8. Watch sessions back Tools like Hotjar or Lookback help you see recurring pain points. ✅ 9. Prioritize issues by impact Fix blockers in navigation, content, or layout first. ✅ 10. Retest fixes Validate that your changes actually solved the problem before closing it. A technically perfect product can still fail if users find it confusing. Usability testing ensures your product feels as good as it functions.

Funnel analysis is essential for understanding where and why users drop off in structured workflows like onboarding, checkout, or sign-up flows. Unlike clickstream analysis, which maps the broader user journey, or session analysis, which focuses on individual interactions, funnel analysis zeroes in on goal-driven processes, tracking user progression and highlighting abandonment points. What’s evolving today is how we approach funnel analysis. With more natural behavioral data and machine learning enhancements, we’re moving beyond static drop-off reporting. AI-driven insights now allow teams to predict drop-offs before they occur, identifying early warning signs like hesitation patterns or inefficient navigation loops. This proactive approach enables UX researchers to refine workflows dynamically, improving user retention before friction escalates. Advanced segmentation is also revolutionizing funnel tracking. Instead of analyzing drop-offs solely through broad demographic data, researchers can now segment users based on behavioral clusters - how they interact with key touchpoints, their engagement duration, or even their likelihood of return. This behavioral-first approach allows for personalized interventions that cater to different user types, ensuring a more seamless experience for all. Beyond traditional conversion tracking, we’re incorporating statistical methods like survival analysis to estimate how long users remain engaged in a funnel and Markov modeling to understand the probability of transitioning between different steps. Instead of treating drop-offs as simple yes/no outcomes, these approaches quantify the likelihood of users completing a process based on their prior actions, leading to more precise and actionable insights. Funnel analysis is no longer just about counting conversions, it’s about deeply understanding user intent, predicting disengagement, and designing experiences that encourage progression. The shift from static reporting to predictive UX optimization is already underway.