March 21, 2025
—
10

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

Header graphic for the article 'The Evolution of UX Research: Digital Twins and the Future...'

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. 

‍

Share this article
Author
Optimal
Workshop

Related articles

View all blog articles
Header graphic for the article 'Optimal vs. Maze: Deep User Insights or Surface-Level Design Feedback'
Learn more
1 min read

Optimal vs. Maze: Deep User Insights or Surface-Level Design Feedback

‍

Product teams face an important decision when selecting the right user research platform: do they prioritize speed and simplicity, or invest in a more comprehensive platform that offers real research depth and insights? This choice becomes even more critical as user research scales and those insights directly influence major product decisions.

‍

Maze has gained popularity in recent years among design and product teams for its focus on rapid prototype testing and design workflow integration. However, as teams scale their research programs and require more sophisticated insights, many discover that Maze's limitations outweigh its simplicity. Platform stability issues, restricted tools and functionality, and a lack of enterprise features creates friction that end up compromising insight speed, quality and overall business impact.

‍

Why Choose Optimal instead of Maze?

‍

‍Platform Depth

‍

Test Design Flexibility

‍

Optimal Offers Comprehensive Test Flexibility: Optimal has a Figma integration, image import capabilities, and fully customizable test flows designed for agile product teams.

‍

Maze has Rigid Question Types: In contrast, Maze's focus on speed comes with design inflexibility, including rigid question structures and limited customization options that reduce overall test effectiveness.

‍

Live Site Testing

‍

Optimal Delivers Comprehensive Live Site Testing: Optimal's live site testing allows you to test your actual website or web app in real-time with real users, gathering behavioral data and usability insights post-launch without any code requirements. This enables continuous testing and iteration even after products are in users' hands.

‍

Maze Offers Basic Live Website Testing: While Maze provides live website testing capabilities, its focus remains primarily on unmoderated studies with limited depth for ongoing site optimization.

‍

Interview and Moderated Research Capabilities

‍

Optimal Interviews Transforms Research Analysis: Optimal's new Interviews tool revolutionizes how teams extract insights from user research. Upload interview videos and let AI automatically surface key themes, generate smart highlight reels, create timestamped transcripts, and produce actionable insights in hours instead of weeks. Every insight comes with supporting video evidence, making it easy to back up recommendations with real user feedback and share compelling clips with stakeholders.

‍

Maze Interview Studies Requires Enterprise Plan: Maze's Interview Studies feature for moderated research is only available on their highest-tier Organization plan, putting live moderated sessions out of reach for small and mid-sized teams. Teams on lower tiers must rely solely on unmoderated testing or use separate tools for interviews.

‍

Prototype Testing Capabilities

‍

Optimal has Advanced Prototype Testing: Optimal supports sophisticated prototype testing with full Figma integration, comprehensive interaction capture, and flexible testing methods that accommodate modern product design and development workflows.

‍

Maze has Limited Prototype Support: Users report difficulties with Maze's prototype testing capabilities, particularly with complex interactions and advanced design systems that modern products require.

‍

Analysis and Reporting Quality

‍

Optimal has Rich, Actionable Insights: Optimal delivers AI-powered analysis with layered insights, export-ready reports, and sophisticated visualizations that transform data into actionable business intelligence.

‍

Maze Only Offers Surface-Level Reporting: Maze provides basic metrics and surface-level analysis without the depth required for strategic decision-making or comprehensive user insight.

‍

Enterprise Features

‍

Dedicated Enterprise Support

‍

Optimal Provides Dedicated Enterprise Support: Optimal offers fast, personalized support with dedicated account teams and comprehensive training resources built by user experience experts that ensure your team is set up for success.

‍

Maze has a Reactive Support Model: Maze provides responsive support primarily for critical issues but lacks the proactive, dedicated support enterprise product teams require.

‍

Enterprise Readiness

‍

Optimal is an Enterprise-Built Platform: Optimal was designed for enterprise use with comprehensive security protocols, compliance certifications, and scalability features that support large research programs across multiple teams and business units. Optimal is currently trusted by some of the world's biggest brands including Netflix, Lego and Nike.

‍

Maze is Built for Individuals: Maze was built primarily for individual designers and small teams, lacking the enterprise features, compliance capabilities, and scalability that large organizations need.

‍

Enterprises Need Reliable, Scalable User Insights

‍

While Maze's focus on speed appeals to design teams seeking rapid iteration, enterprise product teams need the stability and reliability that only mature platforms provide. Optimal delivers both speed and dependability, enabling teams to iterate quickly without compromising research quality or business impact. Platform reliability isn't just about uptime, it's about helping product teams make high quality strategic decisions and to build organizational confidence in user insights. Mature product, design and UX teams need to choose platforms that enhance rather than undermine their research credibility.

‍

Don't let platform limitations compromise your research potential.

‍

Ready to see how leading brands including Lego, Netflix and Nike achieve better research outcomes? Experience how Optimal's platform delivers user insights that adapt to your team's growing needs.

‍

Header graphic for the article 'Best UX Research Methods for Every Phase of Product Development'
Learn more
1 min read

Best UX Research Methods for Every Phase of Product Development

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.

‍

‍

‍

Final Thoughts: Why Continuous UX Research Matters

‍

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.

Header graphic for the article 'A beginner’s guide to qualitative and quantitative research'
Learn more
1 min read

A beginner’s guide to qualitative and quantitative research

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.

‍

‍

Qualitative research

‍

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.

‍

‍

When to use qualitative research

‍

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:

‍

  • 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?

‍

‍

How to gather qualitative data

‍

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:

‍

  • 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.

‍

‍

How many participants will you need?

‍

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.

‍

‍

Quantitative research

‍

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.

‍

‍

When to use quantitative research

‍

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:

‍

  • 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?

‍

‍

How to gather quantitative data

‍

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

‍

Here are some of the most popular quantitative research methods:

‍

  • 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.

‍

‍

How many participants will you need?

‍

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.

‍

‍

Mixed methods research

‍

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.

‍

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:

‍

‍

‍

Where to from here?

‍

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.

‍

Seeing is believing

Explore our tools and see how Optimal makes gathering insights simple, powerful, and impactful.