The Evolution of UX Research: Digital Twins and the Future of User Insight

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.

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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.

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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.

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Define user research

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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.

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In general, there are a few key things worth focusing on when you’re trying to explain the benefits of research:

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• 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.

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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.

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The early stages

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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:

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• 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.

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Involve others in your research

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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.

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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.

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Educate others on what research is

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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.

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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.

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Collect user insights across the organization

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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.

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Whenever someone wants to learn more about research within the organization, they’ll be able to find everything easily.

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Bring stakeholders along to research sessions

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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.

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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.

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You may also want to consider bringing anyone who’s interested along to a research session, even if they’re just there to observe.

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Share your findings – consistently

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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.

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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.

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Anything else you think is worth mentioning? Let us know in the comments.

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Read more

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• Selling your design recommendations to clients and colleagues – Guest writer Jeff Tang outlines some of his techniques for presenting UX recommendations and answering objections.
• Quantifying the ROI of UX – Ashlea McKay delves into one of the toughest UX questions to answer: “What do we get for our money?”
• How to lead a UX team – With user-centered design growing in organizations around the world, we’ll need capable leaders to run UX teams.
• How do I explain what UX is? – Ashlea McKay covers how you can explain UX to people who may not necessarily have familiarity with the field.

Introduction

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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.

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But in 2025, that assumption is being challenged.

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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.

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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?

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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.

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The Traditional UX Research Model: Why Change?

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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.

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This approach works well, but it has challenges:

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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.
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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.
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3. Small sample sizes create risk. Budget and timeline constraints often mean testing with small groups, leaving room for blind spots and bias.
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4. Long feedback loops slow decision-making. By the time research is completed, product teams may have already moved on, limiting its impact.

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In short: traditional UX research provides depth and authenticity, but it’s not always fast or scalable.

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Digital twins and synthetic users aim to change that.

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What Are Digital Twins and Synthetic Users?

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While the terms digital twins and synthetic users are sometimes used interchangeably, they are distinct concepts.

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Digital Twins: Simulating Real-World Behavior

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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.

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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.

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Synthetic Users: AI-Generated Research Participants

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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.

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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.

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Think of it this way:

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• 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.

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Both approaches are still evolving, but their potential applications in UX research are already taking shape.

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Where Digital Twins and Synthetic Users Fit into UX Research

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The appeal of AI-generated users is undeniable. They can:

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• 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.

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These capabilities make digital twins particularly useful for:

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• 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.

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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.

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The Risks and Limitations of AI-Driven UX Research

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For all their promise, digital twins and synthetic users introduce new challenges.

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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.
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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.
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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.
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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.

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A Hybrid Future: AI + Human UX Research

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Rather than viewing digital twins as a replacement for human research, the best UX teams will integrate them as a complementary tool.

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Where AI Can Lead:

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• Large-scale pattern identification
• Early-stage usability evaluations
• Speeding up research cycles
• Automating repetitive testing

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Where Humans Remain Essential:

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• Understanding emotion, frustration, and delight
• Detecting unexpected behaviors
• Validating insights with real-world context
• Ethical considerations and cultural nuance

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The future of UX research is not about choosing between AI and human research—it’s about blending the strengths of both.

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Final Thoughts: Proceeding With Caution and Curiosity

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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.

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Instead, UX researchers should view these technologies as powerful, but imperfect tools—best used in combination with traditional research methods.

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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.

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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.

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This article was researched with the help of Perplexity.ai. 

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In the field of user research, every method is either qualitative, quantitative – or both. Understandably, there’s some confusion around these 2 approaches and where the different methods are applicable. This article provides a handy breakdown of the different terms and where and why you’d want to use qualitative or quantitative research methods.

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Qualitative research

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Let’s start with qualitative research, an approach that’s all about the ‘why’. It’s exploratory and not about numbers, instead focusing on reasons, motivations, behaviors and opinions – it’s best at helping you gain insight and delve deep into a particular problem. This type of data typically comes from conversations, interviews and responses to open questions. The real value of qualitative research is in its ability to give you a human perspective on a research question. Unlike quantitative research, this approach will help you understand some of the more intangible factors – things like behaviors, habits and past experiences – whose effects may not always be readily apparent when you’re conducting quantitative research. A qualitative research question could be investigating why people switch between different banks, for example.

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When to use qualitative research

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Qualitative research is best suited to identifying how people think about problems, how they interact with products and services, and what encourages them to behave a certain way. For example, you could run a study to better understand how people feel about a product they use, or why people have trouble filling out your sign up form. Qualitative research can be very exploratory (e.g., user interviews) as well as more closely tied to evaluating designs (e.g., usability testing). Good qualitative research questions to ask include:

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• Why do customers never add items to their wishlist on our website?
• How do new customers find out about our services?
• What are the main reasons people don’t sign up for our newsletter?

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How to gather qualitative data

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There’s no shortage of methods to gather qualitative data, which commonly takes the form of interview transcripts, notes and audio and video recordings. Here are some of the most widely-used qualitative research methods:

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• Usability test – Test a product with people by observing them as they attempt to complete various tasks.
• User interview – Sit down with a user to learn more about their background, motivations and pain points.
• Contextual inquiry – Learn more about your users in their own environment by asking them questions before moving onto an observation activity.
• Focus group – Gather 6 to 10 people for a forum-like session to get feedback on a product.

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How many participants will you need?

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You don’t often need large numbers of participants for qualitative research, with the average range usually somewhere between 5 to 10 people. You’ll likely require more if you're focusing your work on specific personas, for example, in which case you may need to study 5-10 people for each persona. While this may seem quite low, consider the research methods you’ll be using. Carrying out large numbers of in-person research sessions requires a significant time investment in terms of planning, actually hosting the sessions and analyzing your findings.

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Quantitative research

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On the other side of the coin you’ve got quantitative research. This type of research is focused on numbers and measurement, gathering data and being able to transform this information into statistics. Given that quantitative research is all about generating data that can be expressed in numbers, there multiple ways you make use of it. Statistical analysis means you can pull useful facts from your quantitative data, for example trends, demographic information and differences between groups. It’s an excellent way to understand a snapshot of your users. A quantitative research question could involve investigating the number of people that upgrade from a free plan to a paid plan.

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When to use quantitative research

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Quantitative research is ideal for understanding behaviors and usage. In many cases it's a lot less resource-heavy than qualitative research because you don't need to pay incentives or spend time scheduling sessions etc). With that in mind, you might do some quantitative research early on to better understand the problem space, for example by running a survey on your users. Here are some examples of good quantitative research questions to ask:

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• How many customers view our pricing page before making a purchase decision?
• How many customers search versus navigate to find products on our website?
• How often do visitors on our website change their password?

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How to gather quantitative data

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Commonly, quantitative data takes the form of numbers and statistics.

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Here are some of the most popular quantitative research methods:

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• Card sorts – Find out how people categorize and sort information on your website.
• First-click tests – See where people click first when tasked with completing an action.
• A/B tests – Compare 2 versions of a design in order to work out which is more effective.
• Clickstream analysis – Analyze aggregate data about website visits.

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How many participants will you need?

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While you only need a small number of participants for qualitative research, you need significantly more for quantitative research. Quantitative research is all about quantity. With more participants, you can generate more useful and reliable data you can analyze. In turn, you’ll have a clearer understanding of your research problem. This means that quantitative research can often involve gathering data from thousands of participants through an A/B test, or with 30 through a card sort. Read more about the right number of participants to gather for your research.

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Mixed methods research

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While there are certainly times when you’d only want to focus on qualitative or quantitative data to get answers, there’s significant value in utilizing both methods on the same research projects.Interestingly, there are a number of research methods that will generate both quantitative and qualitative data. Take surveys as an example. A survey could include questions that require written answers from participants as well as questions that require participants to select from multiple choices.

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Looking back at the earlier example of how people move from a free plan to a paid plan, applying both research approaches to the question will yield a more robust or holistic answer. You’ll know why people upgrade to the paid plan in addition to how many. You can read more about mixed methods research in this article:

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• What is mixed methods research?

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Where to from here?

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Now that you know the difference between qualitative and quantitative research, the best way to build confidence is to start testing. Hands-on experience is the fastest path to deeper insight. At Optimal, we make it easy to run your first study, no matter your role or research experience.

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• Explore our 101 guides to user research
• Start a free trial and discover insights that drive real business impact
• How to encourage people to participate in your study – Seemingly one of the hardest parts of conducting research is finding willing participants. It’s actually not that difficult.