March 21, 2025
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The Evolution of UX Research: Digital Twins and the Future of User Insight

Introduction

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:

  1. 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.
  2. 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.
  3. Small sample sizes create risk. Budget and timeline constraints often mean testing with small groups, leaving room for blind spots and bias.
  4. 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.

  1. 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.
  2. 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.
  3. 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.
  4. 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. 

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Efficient Research: Maximizing the ROI of Understanding Your Customers

Introduction

User research is invaluable, but in fast-paced environments, researchers often struggle with tight deadlines, limited resources, and the need to prove their impact. In our recent UX Insider webinar, Weidan Li, Senior UX Researcher at Seek, shared insights on Efficient Research—an approach that optimizes Speed, Quality, and Impact to maximize the return on investment (ROI) of understanding customers.

At the heart of this approach is the Efficient Research Framework, which balances these three critical factors:

  • Speed – Conducting research quickly without sacrificing key insights.
  • Quality – Ensuring rigor and reliability in findings.
  • Impact – Making sure research leads to meaningful business and product changes.

Within this framework, Weidan outlined nine tactics that help UX researchers work more effectively. Let’s dive in.

1. Time Allocation: Invest in What Matters Most

Not all research requires the same level of depth. Efficient researchers prioritize their time by categorizing projects based on urgency and impact:

  • High-stakes decisions (e.g., launching a new product) require deep research.
  • Routine optimizations (e.g., tweaking UI elements) can rely on quick testing methods.
  • Low-impact changes may not need research at all.

By allocating time wisely, researchers can avoid spending weeks on minor issues while ensuring critical decisions are well-informed.

2. Assistance of AI: Let Technology Handle the Heavy Lifting

AI is transforming UX research, enabling faster and more scalable insights. Weidan suggests using AI to:

  • Automate data analysis – AI can quickly analyze survey responses, transcripts, and usability test results.
  • Generate research summaries – Tools like ChatGPT can help synthesize findings into digestible insights.
  • Speed up recruitment – AI-powered platforms can help find and screen participants efficiently.

While AI can’t replace human judgment, it can free up researchers to focus on higher-value tasks like interpreting results and influencing strategy.

3. Collaboration: Make Research a Team Sport

Research has a greater impact when it’s embedded into the product development process. Weidan emphasizes:

  • Co-creating research plans with designers, PMs, and engineers to align on priorities.
  • Involving stakeholders in synthesis sessions so insights don’t sit in a report.
  • Encouraging non-researchers to run lightweight studies, such as A/B tests or quick usability checks.

When research is shared and collaborative, it leads to faster adoption of insights and stronger decision-making.

4. Prioritization: Focus on the Right Questions

With limited resources, researchers must choose their battles wisely. Weidan recommends using a prioritization framework to assess:

  • Business impact – Will this research influence a high-stakes decision?
  • User impact – Does it address a major pain point?
  • Feasibility – Can we conduct this research quickly and effectively?

By filtering out low-priority projects, researchers can avoid research for research’s sake and focus on what truly drives change.

5. Depth of Understanding: Go Beyond Surface-Level Insights

Speed is important, but efficient research isn’t about cutting corners. Weidan stresses that even quick studies should provide a deep understanding of users by:

  • Asking why, not just what – Observing behavior is useful, but uncovering motivations is key.
  • Using triangulation – Combining methods (e.g., usability tests + surveys) to validate findings.
  • Revisiting past research – Leveraging existing insights instead of starting from scratch.

Balancing speed with depth ensures research is not just fast, but meaningful.

6. Anticipation: Stay Ahead of Research Needs

Proactive researchers don’t wait for stakeholders to request studies—they anticipate needs and set up research ahead of time. This means:

  • Building a research roadmap that aligns with upcoming product decisions.
  • Running continuous discovery research so teams have a backlog of insights to pull from.
  • Creating self-serve research repositories where teams can find relevant past studies.

By anticipating research needs, UX teams can reduce last-minute requests and deliver insights exactly when they’re needed.

7. Justification of Methodology: Explain Why Your Approach Works

Stakeholders may question research methods, especially when they seem time-consuming or expensive. Weidan highlights the importance of educating teams on why specific methods are used:

  • Clearly explain why qualitative research is needed when stakeholders push for just numbers.
  • Show real-world examples of how past research has led to business success.
  • Provide a trade-off analysis (e.g., “This method is faster but provides less depth”) to help teams make informed choices.

A well-justified approach ensures research is respected and acted upon.

8. Individual Engagement: Tailor Research Communication to Your Audience

Not all stakeholders consume research the same way. Weidan recommends adapting insights to fit different audiences:

  • Executives – Focus on high-level impact and key takeaways.
  • Product teams – Provide actionable recommendations tied to specific features.
  • Designers & Engineers – Share usability findings with video clips or screenshots.

By delivering insights in the right format, researchers increase the likelihood of stakeholder buy-in and action.

9. Business Actions: Ensure Research Leads to Real Change

The ultimate goal of research is not just understanding users—but driving business decisions. To ensure research leads to action:

  • Follow up on implementation – Track whether teams apply the insights.
  • Tie findings to key metrics – Show how research affects conversion rates, retention, or engagement.
  • Advocate for iterative research – Encourage teams to re-test and refine based on new data.

Research is most valuable when it translates into real business outcomes.

Final Thoughts: Research That Moves the Needle

Efficient research is not just about doing more, faster—it’s about balancing speed, quality, and impact to maximize its influence. Weidan’s nine tactics help UX researchers work smarter by:


✔️  Prioritizing high-impact work
✔️  Leveraging AI and collaboration
✔️  Communicating research in a way that drives action

By adopting these strategies, UX teams can ensure their research is not just insightful, but transformational.

Watch the full webinar here

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1 min read

UX research methods for each product phase

What is UX research? 🤔

User experience (UX) research, or user research as it’s commonly referred to, is an important part of the product design process. Primarily, UX research involves using different research methods to gather information about how your users interact with your product. It is an essential part of developing, building and launching a product that truly meets the requirements of your users. 

UX research is essential at all stages of a products' life cycle:

  1. Planning
  2. Building
  3. Introduction
  4. Growth & Maturity

While there is no one single time to conduct UX research it is best-practice to continuously gather information throughout the lifetime of your product. The good news is many of the UX research methods do not fit just one phase either, and can (and should) be used repeatedly. After all, there are always new pieces of functionality to test and new insights to discover. We introduce you to best-practice UX research methods for each lifecycle phase of your product.

1. Product planning phase 🗓️

While the planning phase it is about creating a product that fits your organization, your organization’s needs and meeting a gap in the market it’s also about meeting the needs, desires and requirements of your users. Through UX research you’ll learn which features are necessary to be aligned with your users. And of course, user research lets you test your UX design before you build, saving you time and money.

Qualitative Research Methods

Usability Testing - Observational

One of the best ways to learn about your users and how they interact with your product is to observe them in their own environment. Watch how they accomplish tasks, the order they do things, what frustrates them, and what makes the task easier and/or more enjoyable for your subject. The data can be collated to inform the usability of your product, improving intuitive design, and what resonates with users.

Competitive Analysis

Reviewing products already in the market can be a great start to the planning process. Why are your competitors’ products successful and how well do they behave for users. Learn from their successes, and even better build on where they may not be performing the best and find your niche in the market.

Quantitative Research Methods

Surveys and Questionnaires

Surveys are useful for collecting feedback or understanding attitudes. You can use the learnings from your survey of a subset of users to draw conclusions about a larger population of users.

There are two types of survey questions:

Closed questions are designed to capture quantitative information. Instead of asking users to write out answers, these questions often use multi-choice answers.

Open questions are designed to capture qualitative information such as motivations and context.  Typically, these questions require users to write out an answer in a text field.

2. Product building phase 🧱

Once you've completed your product planning research, you’re ready to begin the build phase for your product. User research studies undertaken during the build phase enable you to validate the UX team’s deliverables before investing in the technical development.

Qualitative Research Methods

Focus groups

Generally involve 5-10 participants and include demographically similar individuals. The study is set up so that members of the group can interact with one another and can be carried out in person or remotely.


Besides learning about the participants’ impressions and perceptions of your product, focus group findings also include what users believe to be a product’s most important features, problems they might encounter while using the product, as well as their experiences with other products, both good and bad.

Quantitative Research Methods

Card sorting gives insight into how users think. Tools like card sorting reveal where your users expect to find certain information or complete specific tasks. This is especially useful for products with complex or multiple navigations and contributes to the creation of an intuitive information architecture and user experience.

Tree testing gives insight into where users expect to find things and where they’re getting lost within your product. Tools like tree testing help you test your information architecture.
Card sorting and tree testing are often used together. Depending on the purpose of your research and where you are at with your product, they can provide a fully rounded view of your information architecture.

3. Product introduction phase 📦

You’ve launched your product, wahoo! And you’re ready for your first real life, real time users. Now it’s time to optimize your product experience. To do this, you’ll need to understand how your new users actually use your product.

Qualitative Research Methods

Usability testing involves testing a product with users. Typically it involves observing users as they try to follow and complete a series of tasks. As a result you can evaluate if the design is intuitive and if there are any usability problems.

User Interviews - A user interview is designed to get a deeper understanding of a particular topic. Unlike a usability test, where you’re more likely to be focused on how people use your product, a user interview is a guided conversation aimed at better understanding your users. This means you’ll be capturing details like their background, pain points, goals and motivations.

Quantitative Research Methods

A/B Testing is a way to compare two versions of a design in order to work out which is more effective. It’s typically used to test two versions of the same webpage, for example, using a different headline, image or call to action to see which one converts more effectively. This method offers a way to validate smaller design choices where you might not have the data to make an informed decision, like the color of a button or the layout of a particular image.

Flick-click testing shows you where people click first when trying to complete a task on a website. In most cases, first-click testing is performed on a very simple wireframe of a website, but it can also be carried out on a live website using a tool like first-time clicking.

4. Growth and maturity phase 🪴

If you’ve reached the growth stage, fantastic news! You’ve built a great product that’s been embraced by your users. Next on your to-do list is growing your product by increasing your user base and then eventually reaching maturity and making a profit on your hard work.

Growing your product involves building new or advanced features to satisfy specific customer segments. As you plan and build these enhancements, go through the same research and testing process you used to create the first release. The same holds true for enhancements as well as a new product build — user research ensures you’re building the right thing in the best way for your customers.

Qualitative research methods

User interviews will focus on how your product is working or if it’s missing any features, enriching your knowledge about your product and users.

It allows you to test your current features, discover new possibilities for additional features and think about discarding  existing ones. If your customers aren’t using certain features, it might be time to stop supporting them to reduce costs and help you grow your profits during the maturity stage.

Quantitative research methods

Surveys and questionnaires can help gather information around which features will work best for your product, enhancing and improving the user experience. 

A/B testing during growth and maturity occurs within your sales and onboarding processes. Making sure you have a smooth onboarding process increases your conversion rate and reduces wasted spend — improving your bottom line.

Wrap up 🌮

UX research testing throughout the lifecycle of your product helps you continuously evolve and develop a product that responds to what really matters - your users.

Talking to, testing, and knowing your users will allow you to push your product in ways that make sense with the data to back up decisions. Go forth and create the product that meets your organizations needs by delivering the very best user experience for your users.

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Radical Collaboration: how teamwork really can make the dream work

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.

Contact Details:

Email: natalie@sixfold.co.nz

LinkedIn: https://www.linkedin.com/in/natalieferguson/ and https://www.linkedin.com/in/lulupach/

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?

Seeing is believing

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