How to Use User Research to Improve Website Design: 10 Expert Tips
User research transforms guesswork into informed design decisions that actually resonate with your audience. This article brings together insights from industry experts who share practical strategies for understanding user behavior and applying those findings to create better websites. Learn ten actionable tips that cover everything from blending data types to validating assumptions through real user feedback.
Listen First, Then Build Targeted Solutions
As a digital marketer for a chronic-pain massager brand, my user research process always starts with listening, digging into customer support logs, product reviews, and social comments to understand what people love and where they struggle. I combine that with heatmaps and session recordings to see how real shoppers navigate the site and where they hesitate during the buying journey. From there, I run quick surveys asking customers what nearly stopped them from purchasing and what information they wished they had upfront.
One clear example of feedback-driven change came when multiple users said they couldn't easily tell which massager was best for neck pain versus lower-back pain, so we redesigned the product pages with a clean "Pain Area Selector." After launching that feature, we saw a noticeable lift in conversions because shoppers instantly felt more confident choosing the right device for their specific pain.
Define Goals and Gather Rich Insights
I follow a structured process while conducting user research to inform my website design decisions. I start by defining clear research goals and try to grasp what things I need to learn about user behaviour and needs. After that I go for the qualitative and quantitative methods like user interviews, surveys, and usage testing. These methods help in collecting rich valuable insights. Then I pair it with behavioural data such as heatmaps and session recordings. It assists in finding out how users interact with the website in real time. After that I analyse the data to identify pain points and areas of improvement. The final step is collaborating with the design team to implement the finalized changes and test their impact with continuous feedback.
Blend Quantitative Data With Qualitative Feedback
The purpose of my user research process is simple: creative vision into meaningful results. We begin the process using quantitative data, becoming familiar with heatmaps, Google Analytics, Click Maps, and conversion funnel analysis to see where users are encountering friction. We supplement this with qualitative feedback via rapid user interviews and video recordings of user sessions. This mix of data is helpful for us to observe both what users are doing and potentially why they are behaving that way. A story that summarizes this approach led to a design change for a B2B client whose analytics discussed significant drop-off on their services page. The original design was overly complicated visually. Upon conducting a five-second test, users articulated they were unable to identify the core service immediately. Based on user confusion, the design change was made to remove all but the styling of the service descriptions, the CTA was moved above the fold, and the descriptive content had only three bullet points. There was a 34% increase in lead form submissions within 30 days of implementing this change, which confirmed that innovation must first support clarity.
Remove Friction Through Emotional Understanding
For me, user research always starts with clarity, understanding who the website is really for and what action we want them to take. I combine qualitative insights (client interviews, behavior mapping, scroll-depth analysis) with quantitative data (traffic flow, bounce rates, conversion tracking). My approach is equal parts storytelling and science: uncovering the emotional drivers behind user behavior and translating that into clean, high-performing design.
A good example came from a FemFounder redesign a few years ago. We noticed from heatmaps and user feedback that visitors loved our content but weren't opting in to downloads or email sequences. Instead of adding more popups—which many brands do—we simplified navigation, introduced micro-CTAs within blog content, and reframed the value proposition around transformation rather than templates. Opt-ins increased by over 32% within a month, and average time on page doubled.
That experience reinforced my belief that great design is not about adding more—it's about removing friction and designing for how people actually think and feel.
Track Eye Movements to Validate Assumptions
I use a method called Eye-Tracking Heatmap Sprints, which helps us see exactly where users' attention goes during short testing sessions. We record eye movements as users complete specific tasks, then visualize the data through heatmaps. It's a fast, focused way to validate design assumptions—what we think stands out versus what actually does.
For a SaaS client, we noticed through the heatmaps that users' eyes skipped over the primary call-to-action on the homepage. Most attention was landing on a testimonial carousel instead. During follow-up interviews, users said the headline didn't make it clear what the software did, so they ignored the button. That direct link between eye behavior and feedback made the problem obvious.
We reworked the hero section—simplified the headline, increased contrast around the CTA, and moved the testimonials lower on the page. After launching the update, click-through rates to the signup page rose 27% within two weeks. The data helped prove that design clarity, not just aesthetics, drives engagement.
Start Conversations Before You Review Analytics
Our process for user research starts with simple conversations before any analytics. We ask real users what they expected to find, what confused them, and what almost made them leave. Those insights guide the next round of data review so we can see patterns behind their experiences.
For example, early feedback on Aitherapy's site showed that people were unsure whether they were talking to an AI or a real therapist. That confusion made them hesitant to start a conversation. Based on that, we redesigned the homepage to clearly explain how Aitherapy works and what it can and cannot do. Engagement increased immediately because people felt more informed and safe.
The lesson was that user research is not just about usability, it is about trust. When design decisions are rooted in listening rather than assumptions, the website becomes a space people feel comfortable exploring.
Combine Expert Audits With User Voices
I use a process called Heuristic Co-Scoring, where we combine expert UX evaluations with real user feedback. It starts with a structured heuristic audit, then, we layer in user input through short testing sessions. The "co-scoring" part comes from aligning what users struggle with and what our evaluators flag, so we can rank issues based on both frequency and impact.
For one client, an e-commerce brand, this process revealed that users were consistently hesitating at checkout. Our heuristic review pointed to visual clutter, but the user feedback made it clear that the real issue was uncertainty about shipping costs. People said they didn't trust the final total until the last step. That insight reshaped our focus—we redesigned the cart to display shipping estimates earlier and simplified the cost breakdown.
After the change, cart abandonment dropped by 18% in the first month. It was a clear example of how combining heuristic scoring with user voices can highlight what analytics alone can't: not just where friction happens, but why.
Check People Also Ask Questions Early
When I start keyword research for a new site or blog, I focus on understanding the audience first. Then I look for keywords that match real intent by checking competitor pages, a few research tools, and any existing search data. Once I have a solid list, I group everything into themes so the content plan feels structured instead of scattered.
One tip that really streamlined my workflow is checking the People Also Ask questions right away. It gives you an instant snapshot of what users actually want to know and helps shape topics in a natural, human way without getting lost in endless spreadsheets.
Iterate Upload Flow by Observing Real Reactions
I'm Chris Rodgers. I'm the Founder and CEO at CSP Agency. Here's how I do user research when designing websites and an example where I took what users said and changed a flow.
>>Iterate upload flow for AI video transcription by watching user reactions in real time
Our standard practice is to do user research twice: once to find points of friction, and again to verify that you've fixed them, before rolling out a fix more widely.
Case in point: the upload flow for a site using AI to transcribe videos and allow users to chat about their content. When this site launched, we discovered through a mix of quantitative data collection and qualitative interviews that the point of greatest confusion and frustration was when users uploaded videos. The AI couldn't start chatting about the video until it had ingested it, processed it, transcribed it, and analyzed it and users didn't know this. To them it appeared to be a black box. They expected to be able to chat immediately, but they couldn't, and a lot of paid users were dropping off at that point.
So we started design by illustrating the problem frame by frame in wireframes. During moderated usability tests, we worked through wireframes that made the back end states visible. We added upload status messages like "Uploading," "Processing," "Transcribing," and "Ready to chat." We added percents to the buffering message. Essentially we made the AI's workflow into a narrative. Plus, to set expectations for new users, we added a pre-loaded video, so users could immediately try out the chat function.
Subsequent moderated usability tests measured usability, emotional reaction, and clarity of understanding at every stage. Dropout at the upload step decreased by two thirds. Time to first chat message went from 5 minutes to less than 2. We were accelerating the user's experience "aha" moment.
My advice to any team is to use research as not just a discovery tool but as a continuous feedback loop; the things you notice in the details often aren't just nice to have, but the quickest lever for growth.
Start Small, Validate, Then Scale Meaningfully
My approach to user research is this: start small to find the truth, then scale to make it meaningful.
Early user conversations don't scale, and that's exactly why they're powerful. They help you build real empathy and understand what the actual problem is.
But I don't stop at interviews. Deep insight only matters if you can validate it in the real world. So once I find a pattern, I turn it into the smallest possible change we can release something tiny, testable, and low-risk.
From there, the loop becomes simple:
* Start with real conversations to build empathy.
* Translate insights into a small experiment.
* Release it early (even if it feels too small).
* Watch what happens and iterate.
Nothing beats the clarity of real behaviour after a small release. Interviews give direction, but releases give truth. I rely on both to make decisions that feel grounded, empathetic, and actually effective.
