Like many designers early in their careers, I believed my role began and ended with wireframes, pixels, and prototypes. My manager thought otherwise. He was obsessed with UX analytics. Every design change came with the same question — “What changed? Did conversions improve?”
I didn’t get it. Wasn’t that the product manager’s job? Why was I, the designer, expected to generate weekly reports, track user behavior, and measure drop-offs? I just wanted to design and go home.
Fast forward to a job interview for a senior product designer role. I shared examples of how I had used analytics to identify friction points and drive measurable improvements. The result? Two different units wanted me on their team. The manager even asked if I would consider joining the UX research and product analytics team instead.
That moment validated everything I had learned, and it all started with being pushed to understand and use analytics. I got the offer and a significant pay bump.
One moment that really shifted my perspective was working on the Treasury Bill purchase flow. The original experience was clunky and buried behind confusing labels like “Discounted Instruments.” Most users never even found it.
Old flow:
Using analytics, we saw huge drop-offs early in the flow. So we simplified and relabeled the path:
New flow:
That’s four fewer steps, and the product sold out within two days of every listing.
Without analytics, I might have just made the old screens prettier. Instead, I solved the right problem. That’s when it clicked:
Analytics turns guesswork into clarity. It stops you from solving the wrong problems.
This is hands down my most valuable skill.
I start by identifying high-impact user actions, like Treasury or mutual fund purchases, and break them down step-by-step. I tag events like Clicked_Redeem_Button or Entered_PIN, and monitor conversion at each step.
Here’s how I set up funnels:
1. Identify the high-impact action — First, I determine what user action aligns with business goals, usually revenue-linked activities like Buy Treasury Bills
2. Map the flow — I outline the full journey users take to complete the action. For example, a redemption flow would be: Tap “My Portfolio” > Select an active investment > Click “Redeem” > Enter amount and PIN > Confirm Redemption
3. Create an event plan — I list out each step and assign specific tracking events to them. Example: Clicked_Redeem_Button > Entered_Redeem_Amount > Entered_PIN > Clicked_Confirm
4. Document in an event tracking dictionary — I log all of this into an Excel tracking sheet. This keeps things structured, especially when collaborating with developers
Here’s a snippet from my actual event tracking dictionary:
Each row in that sheet includes:
5. Implement + QA
I work with developers to tag these events in the product, test them end-to-end, and make sure everything is flowing into the analytics dashboard (e.g., LogRocket).
The key is that this setup lets me quantify drop-offs and make data-backed design decisions, instead of relying on hunches or surface-level feedback.
Heatmaps reveal where users click, scroll, and pause. They’ve helped me identify dead zones, poor CTA placement, and misleading layouts, things raw numbers can’t always explain.
When I notice drop-offs, I triangulate that data with CX team feedback or quick research
One time, we saw redemption rates dropping. CX feedback revealed customers weren’t getting OTPs. It wasn’t a UX issue; it was a vendor problem. That cross-analysis saved us from redesigning the wrong flow. This habit has made me more detailed and thorough. My last line manager called it out in my final appraisal before I left.
I learned how to speak “data” in plain English. I led weekly insight reports, escalated product issues clearly, and even added a “Task Owner” column in Jira to boost accountability.
In my last job interview, I didn’t just present wireframes. I walked through real business problems, how I diagnosed them using analytics, and how the resulting design changes drove measurable improvement. By the final round, leadership already knew my profile. The head of the unit asked if I could consider joining the UX research and analytics team.
Two departments competed to have me, and I got to choose. That was the impact of being a designer who could speak data.
When I joined the new team, they were just beginning to formalize their analytics practice. Because I was already familiar with tools like LogRocket, I was able to:
Suddenly, everyone, from PMs to developers, could see exactly where users were getting stuck, and what we were doing about it. Designers stopped guessing and started diagnosing.
This shift changed everything.
The hands-on analytical skills I have built aren’t just “UX skills.” They apply to product, growth, strategy, and operations. I now set up funnels, prioritize high-value actions, run team retrospectives, and use Jira to manage design experiments.
Today, I’m not just a designer. I have developed the skills of a UX consultant and more. I could be a product manager, growth strategist, or even an operations lead.
All because I learned to use data to design with intention.
If you’re a designer thinking, “Analytics isn’t my thing,” I get it. I thought the same. But learning how to track, analyze, and act on user behavior won’t just make your designs better. It’ll make you better and far more indispensable.
LogRocket's Galileo AI watches sessions and understands user feedback for you, automating the most time-intensive parts of your job and giving you more time to focus on great design.
See how design choices, interactions, and issues affect your users — get a demo of LogRocket today.
Growth design isn’t just about getting more users to convert. It’s about using research, experimentation, and data to improve activation, retention, and long-term user value without sacrificing trust or the quality of the experience.
Conversion optimization works best when it improves the experience, not just the metric. This guide covers experimentation, accessibility, personalization, guardrails, and AI-assisted CRO without crossing into manipulation.
We thought a robo advisor would help users invest. Instead, four months of work led to almost no adoption. Here’s what we learned about validating assumptions, listening to users, and building the right thing.
After years of designing and building digital products, I’m sharing 10 UX lessons that challenged my assumptions about minimalism, AI, familiarity, and more.
Learn more →
