How to convince others of the importance of UX research
There’s not much a parent won’t do to ensure their child has the best chance of succeeding in life. Unsurprisingly, things are much the same in product development. Whether it’s a designer, manager, developer or copywriter, everyone wants to see the product reach its full potential.
Key to a product’s success (even though it’s still not widely practiced) is UX research. Without research focused on learning user pain points and behaviors, development basically happens in the dark. Feeding direct insights from customers and users into the development of a product means teams can flick the light on and make more informed design decisions.
While the benefits of user research are obvious to anyone working in the field, it can be a real challenge to convince others of just how important and useful it is. We thought we’d help.
Define user research
If you want to sell the importance of UX research within your organization, you’ve got to ensure stakeholders have a clear understanding of what user research is and what they stand to gain from backing it.
In general, there are a few key things worth focusing on when you’re trying to explain the benefits of research:
More informed design decisions: Companies make major design decisions far too often without considering users. User research provides the data needed to make informed decisions.
Less uncertainty and risk: Similarly, research reduces risk and uncertainty simply by giving companies more clarity around how a particular product or service is used.
Retention and conversion benefits: Research means you’ll be more aligned with the needs of your customers and prospective customers.
Use the language of the people you’re trying to convince. A capable UX research practice will almost always improve key business metrics, namely sales and retention.
The early stages
When embarking on a project, book in some time early in the process to answer questions, explain your research approach and what you hope to gain from it. Here are some of the key things to go over:
Your objectives: What are you trying to achieve? This is a good time to cover your research questions.
Your research methods: Which methods will you be using to carry out your research? Cover the advantages of these methods and the information you’re likely to get from using them.
Constraints: Do you see any major obstacles? Any issues with resources?
Provide examples: Nothing shows the value of doing research quite like a case study. If you can’t find an example of research within your own organization, see what you can find online.
Involve others in your research
When trying to convince someone of the validity of what you’re doing, it’s often best to just show them. There are a couple of effective ways you can do this – at a team or individual level and at an organizational level.
We’ll explain the best way to approach this below, but there’s another important reason to bring others into your research. UX research can’t exist in a vacuum – it thrives on integration and collaboration with other teams. Importantly, this also means working with other teams to define the problems they’re trying to solve and the scope of their projects. Once you’ve got an understanding of what they’re trying to achieve, you’ll be in a better position to help them through research.
Educate others on what research is
Education sessions (lunch-and-learns) are one of the best ways to get a particular team or group together and run through the what and why of user research. You can work with them to work out what they’d like to see from you, and how you can help each other.
Tailor what you’re saying to different teams, especially if you’re talking to people with vastly different skill sets. For example, developers and designers are likely to see entirely different value in research.
Collect user insights across the organization
Putting together a comprehensive internal repository focused specifically on user research is another excellent way to grow awareness. It can also help to quantify things that may otherwise fall by the wayside. For example, you can measure the magnitude of certain pain points or observe patterns in feature requests. Using a platform like Notion or Confluence (or even Google Drive if you don’t want a dedicated platform), log all of your study notes, insights and research information that you find useful.
Whenever someone wants to learn more about research within the organization, they’ll be able to find everything easily.
Bring stakeholders along to research sessions
Getting a stakeholder along to a research session (usability tests and user interviews are great starting points) will help to show them the value that face-to-face sessions with users can provide.
To really involve an observer in your UX research, assign them a specific role. Note taker, for example. With a short briefing on best-practices for note taking, they can get a feel for what’s like to do some of the work you do.
You may also want to consider bringing anyone who’s interested along to a research session, even if they’re just there to observe.
Share your findings – consistently
Research is about more than just testing a hypothesis, it’s important to actually take your research back to the people who can action the data.
By sharing your research findings with teams and stakeholders regularly, your organization will start to build up an understanding of the value that ongoing research can provide, meaning getting approval to pursue research in future becomes easier. This is a bit of a chicken and egg situation, but it’s a practice that all researchers need to get into – especially those embedded in large teams or organizations.
Anything else you think is worth mentioning? Let us know in the comments.
If you missed our recent live training on Prototype Testing, don’t worry—we’ve got everything you need right here! You can catch up at your convenience, so grab a cup of tea, put your feet up, and enjoy the show.
In the session, we explored the powerful new features of our Prototype Testing tool, offering a step-by-step guide to setting up, running, and analyzing your tests like a seasoned pro. This tool is a game-changer for your design workflow, helping you identify usability issues and gather real user feedback before committing significant resources to development.
Here’s a quick recap of the highlights:
1. Creating a prototype test from scratch using images
We walked through how to create a prototype test from scratch using static images. This method is perfect for early-stage design concepts, where you want to quickly test user flows without a fully interactive prototype.
2. Preparing your Figma prototype for testing
Figma users, we’ve got you covered! We discussed how to prepare your Figma prototype for the smoothest possible testing experience. From setting up interactions to ensuring proper navigation, these tips ensure participants have an intuitive experience during the test. For more detailed instructions, check out our help article
3. Seamless Figma prototype imports
One of the standout features of the tool is its seamless integration with Figma. We showed how easy it is to import your designs directly from Figma into Optimal, streamlining the setup process. You can bring your working files straight in, and resync when you need to with one click of a button.
4. Understanding usability metrics and analyzing results
We explored how to analyze the usability metrics, and walked through what the results can indicate on click maps and paths. These visual tools allow you to see exactly how participants navigate your design, making it easier to spot pain points, dead ends, or areas of friction. By understanding user behavior, you can rapidly iterate and refine your prototypes for optimal user experience.
User Experience (UX) research has always been about people. How they think, how they behave, what they need, and—just as importantly—what they don’t yet realise they need. Traditional UX methodologies have long relied on direct human input: interviews, usability testing, surveys, and behavioral observation. The assumption was clear—if you want to understand people, you have to engage with real humans.
But in 2025, that assumption is being challenged.
The emergence of digital twins and synthetic users—AI-powered simulations of human behavior—is changing how researchers approach user insights. These technologies claim to solve persistent UX research problems: slow participant recruitment, small sample sizes, high costs, and research timelines that struggle to keep pace with product development. The promise is enticing: instantly accessible, infinitely scalable users who can test, interact, and generate feedback without the logistical headaches of working with real participants.
Yet, as with any new technology, there are trade-offs. While digital twins may unlock efficiencies, they also raise important questions: Can they truly replicate human complexity? Where do they fit within existing research practices? What risks do they introduce?
This article explores the evolving role of digital twins in UX research—where they excel, where they fall short, and what their rise means for the future of human-centered design.
The Traditional UX Research Model: Why Change?
For decades, UX research has been grounded in methodologies that involve direct human participation. The core methods—usability testing, user interviews, ethnographic research, and behavioral analytics—have been refined to account for the unpredictability of human nature.
This approach works well, but it has challenges:
Participant recruitment is time-consuming. Finding the right users—especially niche audiences—can be a logistical hurdle, often requiring specialised panels, incentives, and scheduling gymnastics.
Research is expensive. Incentives, moderation, analysis, and recruitment all add to the cost. A single usability study can run into tens of thousands of dollars.
Small sample sizes create risk. Budget and timeline constraints often mean testing with small groups, leaving room for blind spots and bias.
Long feedback loops slow decision-making. By the time research is completed, product teams may have already moved on, limiting its impact.
In short: traditional UX research provides depth and authenticity, but it’s not always fast or scalable.
Digital twins and synthetic users aim to change that.
What Are Digital Twins and Synthetic Users?
While the terms digital twins and synthetic users are sometimes used interchangeably, they are distinct concepts.
Digital Twins: Simulating Real-World Behavior
A digital twin is a data-driven virtual representation of a real-world entity. Originally developed for industrial applications, digital twins replicate machines, environments, and human behavior in a digital space. They can be updated in real time using live data, allowing organisations to analyse scenarios, predict outcomes, and optimise performance.
In UX research, human digital twins attempt to replicate real users' behavioral patterns, decision-making processes, and interactions. They draw on existing datasets to mirror real-world users dynamically, adapting based on real-time inputs.
Synthetic Users: AI-Generated Research Participants
While a digital twin is a mirror of a real entity, a synthetic user is a fabricated research participant—a simulation that mimics human decision-making, behaviors, and responses. These AI-generated personas can be used in research scenarios to interact with products, answer questions, and simulate user journeys.
Unlike traditional user personas (which are static profiles based on aggregated research), synthetic users are interactive and capable of generating dynamic feedback. They aren’t modeled after a specific real-world person, but rather a combination of user behaviors drawn from large datasets.
Think of it this way:
A digital twin is a highly detailed, data-driven clone of a specific person, customer segment, or process.
A synthetic user is a fictional but realistic simulation of a potential user, generated based on behavioral patterns and demographic characteristics.
Both approaches are still evolving, but their potential applications in UX research are already taking shape.
Where Digital Twins and Synthetic Users Fit into UX Research
The appeal of AI-generated users is undeniable. They can:
Scale instantly – Test designs with thousands of simulated users, rather than just a handful of real participants.
Eliminate recruitment bottlenecks – No need to chase down participants or schedule interviews.
Reduce costs – No incentives, no travel, no last-minute no-shows.
Enable rapid iteration – Get user insights in real time and adjust designs on the fly.
Generate insights on sensitive topics – Synthetic users can explore scenarios that real participants might find too personal or intrusive.
These capabilities make digital twins particularly useful for:
Early-stage concept validation – Rapidly test ideas before committing to development.
Edge case identification – Run simulations to explore rare but critical user scenarios.
Pre-testing before live usability sessions – Identify glaring issues before investing in human research.
However, digital twins and synthetic users are not a replacement for human research. Their effectiveness is limited in areas where emotional, cultural, and contextual factors play a major role.
The Risks and Limitations of AI-Driven UX Research
For all their promise, digital twins and synthetic users introduce new challenges.
They lack genuine emotional responses. AI can analyse sentiment, but it doesn’t feel frustration, delight, or confusion the way a human does. UX is often about unexpected moments—the frustrations, workarounds, and “aha” realisations that define real-world use.
Bias is a real problem. AI models are trained on existing datasets, meaning they inherit and amplify biases in those datasets. If synthetic users are based on an incomplete or non-diverse dataset, the research insights they generate will be skewed.
They struggle with novelty. Humans are unpredictable. They find unexpected uses for products, misunderstand instructions, and behave irrationally. AI models, no matter how advanced, can only predict behavior based on past patterns—not the unexpected ways real users might engage with a product.
They require careful validation. How do we know that insights from digital twins align with real-world user behavior? Without rigorous validation against human data, there’s a risk of over-reliance on synthetic feedback that doesn’t reflect reality.
A Hybrid Future: AI + Human UX Research
Rather than viewing digital twins as a replacement for human research, the best UX teams will integrate them as a complementary tool.
Where AI Can Lead:
Large-scale pattern identification
Early-stage usability evaluations
Speeding up research cycles
Automating repetitive testing
Where Humans Remain Essential:
Understanding emotion, frustration, and delight
Detecting unexpected behaviors
Validating insights with real-world context
Ethical considerations and cultural nuance
The future of UX research is not about choosing between AI and human research—it’s about blending the strengths of both.
Final Thoughts: Proceeding With Caution and Curiosity
Digital twins and synthetic users are exciting, but they are not a magic bullet. They cannot fully replace human users, and relying on them exclusively could lead to false confidence in flawed insights.
Instead, UX researchers should view these technologies as powerful, but imperfect tools—best used in combination with traditional research methods.
As with any new technology, thoughtful implementation is key. The real opportunity lies in designing research methodologies that harness the speed and scale of AI without losing the depth, nuance, and humanity that make UX research truly valuable.
The challenge ahead isn’t about choosing between human or synthetic research. It’s about finding the right balance—one that keeps user experience truly human-centered, even in an AI-driven world.
This article was researched with the help of Perplexity.ai.
Natalie and Lulu have forged a unique team culture that focuses on positive outputs (and outcomes) for their app’s growing user base. In doing so, they turned the traditional design approach on its head and created a dynamic and supportive team.
Natalie, Director of Design at Hatch, and Lulu, UX Design Specialist, recently spoke at UX New Zealand, the leading UX and IA conference in New Zealand hosted by Optimal Workshop, on their concept of “radical collaboration”.
In their talk, Nat and Lulu share their experience of growing a small app into a big player in the finance sector, and their unique approach to teamwork and culture which helped achieve it.
Background on Natalie Ferguson and Lulu Pachuau
Over the last two decades, Lulu and Nat have delivered exceptional customer experiences for too many organizations to count. After Nat co-founded Hatch, she begged Lulu to join her on their audacious mission: To supercharge wealth building in NZ. Together, they created a design and product culture that inspired 180,000 Kiwi investors to join in just 4 years.
Radical Collaboration - How teamwork makes the dream work 💪💪💪
Nat and Lulu discuss how they nurtured a team culture of “radical collaboration” when growing the hugely popular app Hatch, based in New Zealand. Hatch allows everyday New Zealanders to quickly and easily trade in the U.S. share market.
The beginning of the COVID pandemic spelled huge growth for Hatch and caused significant design challenges for the product. This growth meant that the app had to grow from a baby startup to one that could operate at scale - virtually overnight.
In navigating this challenge, Nat and Lulu coined the term radical collaboration, which aims to “dismantle organizational walls and supercharge what teams achieve”. Radical collaboration has six key pillars, which they discuss alongside their experience at Hatch.
Pillar #1: When you live and breathe your North star
Listening to hundreds of their customers’ stories, combined with their own personal experiences with money, compelled Lulu and Nat to change how their users view money. And so, “Grow the wealth of New Zealanders” became a powerful mission statement, or North Star, for Hatch. The mission was to give people the confidence and the ability to live their own lives with financial freedom and control. Nat and Lulu express the importance of truly believing in the mission of your product, and how this can become a guiding light for any team.
Pillar #2: When you trust each other so much, you’re happy to give up control
As Hatch grew rapidly, trusting each other became more and more important. Nat and Lulu state that sometimes you need to take a step back and stop fueling growth for growth’s sake. It was at this point that Nat asked Lulu to join the team, and Nat’s first request was for Lulu to be super critical about the product design to date - no feedback was out of bounds. Letting go, feeling uncomfortable, and trusting your team can be difficult, but sometimes it’s what you need in order to drag yourself out of status quo design. This resulted in a brief hiatus from frantic delivery to take stock and reprioritize what was important - something that can be difficult without heavy doses of trust!
Pillar #3: When everyone wears all the hats
During their journey, the team at Hatch heard lots of stories from their users. Many of these stories were heard during “Hatcheversery Calls”, where team members would call users on their sign-up anniversary to chat about their experience with the app. Some of these calls were inspiring, insightful, and heartwarming.
Everyone at Hatch made these calls – designers, writers, customer support, engineers, and even the CEO. Speaking to strangers in this way was a challenge for some, especially since it was common to field technical questions about the business. Nevertheless, asking staff to wear many hats like this turned the entire team into researchers and analysts. By forcing ourselves and our team outside of our comfort zone, we forced each other to see the whole picture of the business, not just our own little piece.
Pillar #4: When you do what’s right, not what’s glam
In an increasingly competitive industry, designers and developers are often tempted to consistently deliver new and exciting features. In response to rapid growth, rather than adding more features to the app, Lulu and Nat made a conscious effort to really listen to their customers to understand what problems they needed solving.
As it turned out, filing overseas tax returns was a significant and common problem for their customers - it was difficult and expensive. So, the team at Hatch devised a tax solution. This solution was developed by the entire team, with almost no tax specialists involved until the very end! This process was far from glamorous and it often fell outside of standard job descriptions. However, the team eventually succeeded in simplifying a notoriously difficult process and saved their customers a massive headache.
Pillar #5: When you own the outcome, not your output.
Over time Hatch’s user base changed from being primarily confident, seasoned investors, to being first-time investors. This new user group was typically scared of investing and often felt that it was only a thing wealthy people did.
At this point, Hatch felt it was necessary to take a step back from delivering updates to take stock of their new position. This meant deeply understanding their customers’ journey from signing up, to making their first trade. Once this was intimately understood, the team delivered a comprehensive onboarding process which increased the sign-up conversion rate by 10%!
Pillar #6: When you’re relentlessly committed to making it work
Nat and Lulu describe a moment when Allbirds wanted to work with Hatch to allow ordinary New Zealanders to be involved in their IPO launch on the New York stock exchange. Again, this task faced numerous tax and trade law challenges, and offering the service seemed like yet another insurmountable task. The team at Hatch nearly gave up several times during this project, but everyone was determined to get this feature across the line – and they did. As a result, New Zealanders were some of the few regular investors from outside the U.S that were able to take part in Albirds IPO.
Why it matters 💥
Over four years, Hatch grew to 180,000 users who collectively invested over $1bn. Nat and Lulu’s success underscores the critical role of teamwork and collaboration in achieving exceptional user experiences. Product teams should remember that in the rapidly evolving tech industry, it's not just about delivering the latest features; it's about fostering a positive and supportive team culture that buys into the bigger picture.
The Hatch team grew to be more than team members and technical experts. They grew in confidence and appreciated every moving part of the business. Product teams can draw inspiration from Hatch's journey, where designers, writers, engineers, and even the CEO actively engaged with users, challenged traditional design decisions, and prioritized solving actual user problems. This approach led to better, more user-centric outcomes and a deep understanding of the end-to-end user experience.
Most importantly, through the good times and tough, the team grew to trust each other. The mission weaved its way through each member of the team, which ultimately manifested in positive outcomes for the user and the business.
Nat and Lulu’s concept of radical collaboration led to several positive outcomes for Hatch:
It changed the way they did business. Information was no longer held in the minds of a few individuals – instead, it was shared. People were able to step into other people's roles seamlessly.
Hatch achieved better results faster by focusing on the end-to-end experience of the app, rather than by adding successive features.
The team became more nimble – potential design/development issues were anticipated earlier because everyone knew what the downstream impacts of a decision would be.
Over the next week, Lulu and Nat encourage designers and researchers to get outside of their comfort zone and:
Visit customer support team
Pick up the phone and call a customer
Challenge status quo design decisions. Ask, does this thing solve an end-user problem?
.png)
