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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1 min read

Moderated vs unmoderated research: which approach is best?

Knowing and understanding why and how your users use your product is invaluable for getting to the nitty gritty of usability. Delving deep with probing questions into motivation or skimming over looking for issues can equally be informative. 

Put super simply, usability testing literally is testing how usable your product is for your users. If your product isn’t usable users often won’t complete their task, let alone come back for more. No one wants to lose users before they even get started. Usability testing gets under their skin and really into the how, why and what they want (and equally what they don’t).

As we have been getting used to video calling regularly and using the internet for interactions, usability testing has followed suit. Being able to access participants remotely has allowed us to diversify the participant pool by not being restricted to those that are close enough to be in-person. This has also allowed an increase in the number of participants per test, as it becomes more cost-effective to perform remote usability testing.

But if we’re remote, does this mean it can’t be moderated? No - remote testing, along with modern technology, can mean that remote testing can be facilitated and moderated. But what is the best method - moderated or unmoderated?

What is moderated remote research testing? 🙋🏻

In traditional usability testing, moderated research is done in person. With the moderator and the participant in the same physical space. This, of course, allows for conversation and observational behavioral monitoring. Meaning the moderator can note not only what the participant answers but how and even make note of the body language, surroundings, and other influencing factors. 

This has also meant that traditionally, the participant pool has been limited to those that can be available (and close enough) to make it into a facility for testing. And being in person has meant it takes time (and money) to perform these tests.

As technology has moved along and the speed of internet connections and video calling has increased, this has opened up a world of opportunities for usability testing. Allowing usability testing to be done remotely. Moderators can now set up testing remotely and ‘dial in’ to observe participants anywhere they are. And potentially even running focus groups or other testing in a group format across the internet. 

Pros:

- In-depth gathering of insights through a back-and-forth conversation and observing of the participants.

- Follow-up questions don’t underestimate the value of being available to ask questions throughout the testing. And following up in the moment.

- Observational monitoring noticing and noting the environment and how the participants are behaving, can give more insight into how or why they choose to make a decision.

- Quick remote testing can be quicker to start, find participants, and complete than in-person. This is because you only need to set up a time to connect via the internet, rather than coordinating travel times, etc.

- Location (local and/or international) Testing online removes reliance on participants being physically present for the testing. This broadens your ability to broaden the pool, and participants can be either within your country or global. 

Cons:

- Time-consuming having to be present at each test takes time. As does analyzing the data and insights generated. But remember, this is quality data.

- Limited interactions with any remote testing there is only so much you can observe or understand across the window of a computer screen. It can be difficult to have a grasp on all the factors that might be influencing your participants.

What is unmoderated remote research testing? 😵💫

In its most simple sense, unmoderated user testing removes the ‘moderated’ part of the equation. Instead of having a facilitator guide participants through the test, participants are left to complete the testing by themselves and in their own time. For the most part, everything else stays the same. 

Removing the moderator, means that there isn’t anyone to respond to queries or issues in the moment. This can either delay, influence, or even potentially force participants to not complete or maybe not be as engaged as you may like. Unmoderated research testing suits a very simple and direct type of test. With clear instructions and no room for inference. 

Pros:

- Speed and turnaround,  as there is no need to schedule meetings with each and every participant. Unmoderated usability testing is usually much faster to initiate and complete.

- Size of study (participant numbers) unmoderated usability testing allows you to collect feedback from dozens or even hundreds of users at the same time. 


- Location (local and/or international) Testing online removes reliance on participants being physically present for the testing, which broadens your participant pool.  And unmoderated testing means that it literally can be anywhere while participants complete the test in their own time.

Cons:

- Follow-up questions as your participants are working on their own and in their own time, you can’t facilitate and ask questions in the moment. You may be able to ask limited follow-up questions.

- Products need to be simple to use unmoderated testing does not allow for prototypes or any product or site that needs guidance. 

- Low participant support without the moderator any issues with the test or the product can’t be picked up immediately and could influence the output of the test.

When should you do which? 🤔

Each moderated and unmoderated remote usability testing have its use and place in user research. It really depends on the question you are asking and what you are wanting to know.

Moderated testing allows you to gather in-depth insights, follow up with questions, and engage the participants in the moment. The facilitator has the ability to guide participants to what they want to know, to dig deeper, or even ask why at certain points. This method doesn’t need as much careful setup as the participants aren’t on their own. While this is all done online, it does still allow connection and conversation. This method allows for more investigative research. Looking at why users might prefer one prototype to another. Or possibly tree testing a new website navigation to understand where they might get lost and querying why the participant made certain choices.

Unmoderated testing, on the other hand, is literally leaving the participants to it. This method needs very careful planning and explaining upfront. The test needs to be able to be set and run without a moderator. This lends itself more to wanting to know a direct answer to a query. Such as a card sort on a website to understand how your users might sort information. Or a first click to see how/where users will click on a new website.

Wrap Up 🌯

With the ability to expand our pool of participants across the globe with all of the advances (and acceptance of) technology and video calling etc, the ability to expand our understanding of users’ experiences is growing. Remote usability testing is a great option when you want to gather information from users in the real world. Depending on your query, moderated or unmoderated usability testing will suit your study. As with all user testing, being prepared and planning ahead will allow you to make the most of your test.

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Mixed methods research in 2021

User experience research is super important to developing a product that truly engages, compels and energises people. We all want a website that is easy to navigate, simple to follow and compels our users to finish their tasks. Or an app that supports and drives engagement.

We’ve talked a lot about the various types of research tools that help improve these outcomes. 

There is a rising research trend in 2021.

Mixed method research - what is more compelling than these user research quantitative tools? Combining these with awesome qualitative research! Asking the same questions in various ways can provide deeper insights into how our users think and operate. Empowering you to develop products that truly talk to your users, answer their queries or even address their frustrations.

Though it isn’t enough to simply ‘do research’, as with anything you need to approach it with strategy, focus and direction. This will funnel your time, money and energy into areas that will generate the best results.

Mixed Method UX research is the research trend of 2021

With the likes of Facebook, Amazon, Etsy, eBay, Ford and many more big organizations offering newly formed job openings for mixed methods researchers it becomes very obvious where the research trend is heading.

It’s not only good to have, but now becoming imperative, to gather data, dive deeper and generate insights that provide more information on our users than ever before. And you don't need to be Facebook to reap the benefits. Mixed method research can be implemented across the board and can be as narrow as finding out how your homepage is performing through to analysing in depth the entirety of your product design.

And with all of these massive organizations making the move to increase their data collection and research teams. Why wouldn’t you?

The value in mixed method research is profound. Imagine understanding what, where, how and why your customers would want to use your service. And catering directly for them. The more we understand our customers, the deeper the relationship and the more likely we are to keep them engaged.

Although of course by diving deep into the reasons our users like (or don’t like) how our products operate can drive your organization to target and operate better at a higher level. Gearing your energies to attracting and keeping the right type of customer, providing the right level of service and after care. Potentially reducing overheads, by not delivering to expected levels.

What is mixed method research?

Mixed methods research isn’t overly complicated, and doesn’t take years for you to master. It simply is a term used to refer to using a combination of quantitative and qualitative data. This may mean using a research tool such as card sorting alongside interviews with users. 

Quantitative research is the tangible numbers and metrics that can be gathered through user research such as card sorting or tree testing.

Qualitative research is research around users’ behaviour and experiences. This can be through usability tests, interviews or surveys.

For instance you may be asking ‘how should I order the products on my site?’. With card sorting you can get the data insights that will inform how a user would like to see the products sorted. Coupled with interviews you will get the why.

Understanding the thinking behind the order, and why one user likes to see gym shorts stored under shorts and another would like to see them under active wear. With a deeper understanding of how and why users decide how content should be sorted are made will create a highly intuitive website. 

Another great reason for mixed method research would be to back up data insights for stakeholders. With a depth and breadth of qualitative and quantitative research informing decisions, it becomes clearer why changes may need to be made, or product designs need to be challenged.

How to do mixed method research

Take a look at our article for more examples of the uses of mixed method research. 

Simply put mixed method research means coupling quantitative research, such as tree testing, card sorting or first click testing, with qualitative research such as surveys, interviews or diary entry.

Say, for instance, the product manager has identified that there is an issue with keeping users engaged on the homepage of your website. We would start with asking where they get stuck, and when they are leaving.

This can be done using a first-click tool, such as Chalkmark, which will map where users head when they land on your homepage and beyond. 

This will give you the initial qualitative data. However, it may only give you some of the picture. Coupled with qualitative data, such as watching (and reporting on) body language. Or conducting interviews with users directly after their experience so we can understand why they found the process confusing or misleading.

A fuller picture, means a better understanding.

Key is to identify what your question is and honing in on this through both methods. Ultimately, we are answering your question from both sides of the coin.

Upcoming research trends to watch

Keeping an eye on the progression of the mixed method research trend, will mean keeping an eye on these:

1. Integrated Surveys

Rather than thinking of user surveys as being a one time, in person event, we’re seeing more and more often surveys being implemented through social media, on websites and through email. This means that data can be gathered frequently and across the board. This longitude data allows organizations to continuously analyse, interpret and improve products without really ever stopping. 

Rather than relying on users' memories for events and experiences data can be gathered in the moment. At the time of purchase or interaction. Increasing the reliability and quality of the data collected. 

2. Return to the social research

Customer research is rooted in the focus group. The collection of participants in one space, that allows them to voice their opinions and reach insights collectively. This did used to be an overwhelming task with days or even weeks to analyse unstructured forums and group discussions.

However, now with the advent of online research tools this can also be a way to round out mixed method research.

3. Co-creation

The ability to use your customers input to build better products. This has long been thought a way to increase innovative development. Until recently it too has been cumbersome and difficult to wrangle more than a few participants. But, there are a number of resources in development that will make co-creation the buzzword of the decade.

4. Owned Panels & Community

Beyond community engagement in the social sphere. There is a massive opportunity to utilise these engaged users in product development. Through a trusted forum, users are far more likely to actively and willingly participate in research. Providing insights into the community that will drive stronger product outcomes.

What does this all mean for me

So, there is a lot to keep in mind when conducting any effective user research. And there are a lot of very compelling reasons to do mixed method research and do it regularly. 

To remain innovative, and ahead of the ball it remains very important to be engaged with your users and their needs. Using qualitative and qualitative research to inform product decisions means you can operate knowing a fuller picture.

One of the biggest challenges with user research can be the coordination and participant recruitment. That’s where we come in.

Taking the pain out of the process and streamlining your research. Take a look at our Qualitative Research option, Reframer. Giving you an insight into how we can help make your mixed method research easier and analyse your data efficiently and in a format that is easy to understand.

User research doesn’t need to take weeks or months. With our participant recruitment we can provide reliable and quality participants across the board that will provide data you can rely on.

Why not get in deeper with mixed method research today!

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1 min read

Tips for recruiting quality research participants

If there’s one universal truth in user research, it’s that at some point you’re going to need to find people to actually take part in your studies. Be it a large number of participants for quantitative research or a select number for in-depth, in-person user interviews. Finding the right people (and number) of people can be a hurdle.

With the right strategy, you can source exactly the right participants for your next research project.

We share a practical step-by-step guide on how to find participants for user experience research.

The difficulties/challenges of user research recruiting 🏋️

It has to be acknowledged that there are challenges when recruiting research participants. You may recognize some of these:

  • There are so many channels and methods you can use to find participants, different channels will work better for different projects.
  • Repeatedly using the same channels and methods will result in diminishing returns (i.e. burning out participants).
  • It’s a lengthy and complex process, and some projects don’t have the luxury of time.
  • Offering the right incentives and distributing them is time-consuming.
  • It’s hard to manage participants during long-term or recurring studies, such as customer research projects.

We’ll simplify the process, talk about who the right participants are, and unpack some of the best ways to find them. Removing these blocks can be the easiest way to move forward.

Who are the right participants for different types of research? 🤔

1. The first step to a successful participant recruitment strategy is clarifying the goals of your user research and which methods you intend to use. Ask yourself:

  • What is the purpose of our research?
  • How do we plan to understand that?

2. Define who your ideal research participant is. Who is going to have the answers to your questions?

3. Work out your research recruitment strategy. That starts by understanding the differences between recruiting for qualitative and quantitative research.

Recruiting for qualitative vs. quantitative research 🙋🏻

Quantitative research recruiting is a numbers game. For your data analysis to be meaningful and statistically significant, you need a lot of data. This means you need to do a lot of research with a lot of people. When recruiting for quantitative research, you first have to define the population (the entire group you want to study). From there, you choose a sampling method that allows you to create a sample—a randomly selected subset of the population who will participate in your study.

Qualitative recruiting involves far fewer participants, but you do need to find a selection of ‘perfect’ participants. Those that fit neatly into your specific demographic, geographic, psychographic, and behavioral criteria relevant to your study. Recruiting quality participants for qualitative studies involves non-random sampling, screening, and plenty of communication.

How many participants do you need? 👱🏻👩👩🏻👧🏽👧🏾

How many participants to include in a qualitative research study is one of the most heavily discussed topics in user research circles. In most cases, you can get away with 5 people – that’s the short answer. With 5 people, you’ll uncover most of the main issues with the thing you’re testing. Depending on your research project there could be as many as 50 participants, but with each additional person, there is an additional cost (money and time).

Quantitative research is obviously quite different. With studies like card sorts and tree tests, you need higher participant numbers to get statistically meaningful results. Anywhere from 20 - 500 participants, again coming back to the purpose of your test and your research budget. These are usually easier and quicker to implement therefore the additional cost is lower.

User research recruitment - step by step 👟

Let’s get into your research recruitment strategy to find the best participants for your research project. There are 5 clear steps to get you through to the research stage:

1. Identify your ideal participants

Who are they? What do they do? How old are they? Do they already use your product? Where do they live? These are all great questions to get you thinking about who exactly you need to answer your research questions. The demographic and geographic detail of your participants are important to the quality of your research results.

2. Screen participants

Screening participants will weed out those that may not be suitable for your specific project. This can be as simple as asking if the participants have used a product similar to yours. Or coming back to your key identified demographic requirements and removing anyone that doesn’t fit these criteria.

3. Find prospective participants

This is important and can be time-consuming. For qualitative research projects, you can look within your organization or ask over social media for willing participants. Or if you’re short on time look at a participant recruitment service, which takes your requirements and has a catalog of available persons to call on. There’s a cost involved, but the time saving can negate this. For qualitative surveys, a great option can be a live intercept on your website or app that interrupts users and asks them to complete a short questionnaire.

4. Research incentives

In some cases you will need to provide incentives. This could be offering a prize or discount for those who complete online qualitative surveys. Or a fixed sum for those that take part in longer format quantitative studies.

5. Scheduling with participants

Once you have waded through the emails, options, and communication from your inquiries make a list of appropriate participants. Schedule time to do the research, either in person or remotely. Be clear about expectations and how long it will take. And what the incentive to take part is.

Tips to avoid participant burnout 📛

You’ve got your participants sorted and have a great pool of people to call on. If you keep hitting the same group of people time and time again, you will experience the law of diminishing returns. Constantly returning to the same pool of participants will eventually lead to fatigue. And this will impact the quality of your research because it’s based on interviewing the same people with the same views.

There are 2 ways to avoid this problem:

  1. Use a huge database of potential participant targets.
  2. Use a mixture of different recruitment strategies and channels.

Of course, it might be unavoidable to hit the same audience repeatedly when you’re testing your product development among your customer base.

Wrap up 🌯

Understanding your UX research recruitment strategy is crucial to recruiting quality participants. A clear idea of your purpose, who your ideal participants are, and how to find them takes time and experience. 

And to make life easier you can always leave your participant recruitment with us. With a huge catalog of quality participants all at your fingertips on our app, we can recruit the right people quickly.

Check out more here.

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