April 24, 2019
1 min read

6 things to consider when setting up a research practice

With UX research so closely tied to product success, setting up a dedicated research practice is fast becoming important for many organizations. It’s not an easy process, especially for organizations that have had little to do with research, but the end goal is worth the effort.

But where exactly are you supposed to start? This article provides 6 key things to keep in mind when setting up a research practice, and should hopefully ensure you’ve considered all of the relevant factors.

1) Work out what your organization needs

The first and most simple step is to take stock of the current user research situation within the organization. How much research is currently being done? Which teams or individuals are talking to customers on an ongoing basis? Consider if there are any major pain points with the current way research is being carried out or bottlenecks in getting research insights to the people that need them. If research isn't being practiced, identify teams or individuals that don't currently have access to the resources they need, and consider ways to make insights available to the people that need them.

2) Consolidate your insights

UX research should be communicating with nearly every part of an organization, from design teams to customer support, engineering departments and C-level management. The insights that stem from user research are valuable everywhere. Of course, the opposite is also true: insights from support and sales are useful for understanding customers and how the current product is meeting people's needs.

When setting up a research practice, identify which teams you should align with, and then reach out. Sit down with these teams and explore how you can help each other. For your part, you’ll probably need to explain the what and why of user research within the context of your organization, and possibly even explain at a basic level some of the techniques you use and the data you can obtain.

Then, get in touch with other teams with the goal of learning from them. A good research practice needs a strong connection to other parts of the business with the express purpose of learning. For example, by working with your organization’s customer support team, you’ll have a direct line to some of the issues that customers deal with on a regular basis. A good working relationship here means they’ll likely feed these insights back to you, in order to help you frame your research projects.

By working with your sales team, they’ll be able to share issues prospective customers are dealing with. You can follow up on this information with research, the results of which can be fed into the development of your organization’s products.

It can also be fruitful to develop an insights repository, where researchers can store any useful insights and log research activities. This means that sales, customer support and other interested parties can access the results of your research whenever they need to.

When your research practice is tightly integrated other key areas of the business, the organization is likely to see innumerable benefits from the insights>product loop.

3) Figure out which tools you will use

By now you’ve hopefully got an idea of how your research practice will fit into the wider organization – now it’s time to look at the ways in which you’ll do your research. We’re talking, of course, about research methods and testing tools.

We won’t get into every different type of method here (there are plenty of other articles and guides for that), but we will touch on the importance of qualitative and quantitative methods. If you haven’t come across these terms before, here’s a quick breakdown:

  • Qualitative research – Focused on exploration. It’s about discovering things we cannot measure with numbers, and often involves speaking with users through observation or user interviews.
  • Quantitative research – Focused on measurement. It’s all about gathering data and then turning this data into usable statistics.

All user research methods are designed to deliver either qualitative or quantitative data, and as part of your research practice, you should ensure that you always try to gather both types. By using this approach, you’re able to generate a clearer overall picture of whatever it is you’re researching.

Next comes the software. A solid stack of user research testing tools will help you to put research methods into practice, whether for the purposes of card sorting, carrying out more effective user interviews or running a tree test.

There are myriad tools available now, and it can be difficult to separate the useful software from the chaff. Here’s a list of research and productivity tools that we recommend.

Tools for research

Here’s a collection of research tools that can help you gather qualitative and quantitative data, using a number of methods.

  • Treejack – Tree testing can show you where people get lost on your website, and help you take the guesswork out of information architecture decisions. Like OptimalSort, Treejack makes it easy to sort through information and pairs this with in-depth analysis features.
  • dScout – Imagine being able to get video snippets of your users as they answer questions about your product. That’s dScout. It’s a video research platform that collects in-context “moments” from a network of global participants, who answer your questions either by video or through photos.
  • Ethnio – Like dScout, this is another tool designed to capture information directly from your users. It works by showing an intercept pop-up to people who land on your website. Then, once they agree, it runs through some form of research.
  • OptimalSort – Card sorting allows you to get perspective on whatever it is you’re sorting and understand how people organize information. OptimalSort makes it easier and faster to sort through information, and you can access powerful analysis features.
  • Reframer – Taking notes during user interviews and usability tests can be quite time-consuming, especially when it comes to analyze the data. Reframer gives individuals and teams a single tool to store all of their notes, along with a set of powerful analysis features to make sense of their data.
  • Chalkmark – First-click testing can show you what people click on first in a user interface when they’re asked to complete a task. This is useful, as when people get their first click correct, they’re much more likely to complete their task. Chalkmark makes the process of setting up and running a first-click test easy. What’s more, you’re given comprehensive analysis tools, including a click heatmap.

Tools for productivity

These tools aren’t necessarily designed for user research, but can provide vital links in the process.

  • Whimsical – A fantastic tool for user journeys, flow charts and any other sort of diagram. It also solves one of the biggest problems with online whiteboards – finicky object placement.
  • Descript – Easily transcribe your interview and usability test audio recordings into text.
  • Google Slides – When it inevitably comes time to present your research findings to stakeholders, use Google Slides to create readable, clear presentations.

4) Figure out how you’ll track findings over time

With some idea of the research methods and testing tools you’ll be using to collect data, now it’s time to think about how you’ll manage all of this information. A carefully ordered spreadsheet and folder system can work – but only to an extent. Dedicated software is a much better choice, especially given that you can scale these systems much more easily.

A dedicated home for your research data serves a few distinct purposes. There’s the obvious benefit of being able to access all of your findings whenever you need them, which means it’s much easier to create personas if the need arises. A dedicated home also means your findings will remain accessible and useful well into the future.

When it comes to software, Reframer stands as one of the better options for creating a detailed customer insights repository as you’re able to capture your sessions directly in the tool and then apply tags afterwards. You can then easily review all of your observations and findings using the filtering options. Oh, and there’s obviously the analysis side of the tool as well.

If you’re looking for a way to store high-level findings – perhaps if you’re intending to share this data with other parts of your organization – then a tool like Confluence or Notion is a good option. These tools are basically wikis, and include capable search and navigation options too.

5) Where will you get participants from?

A pool of participants you can draw from for your user research is another important part of setting up a research practice. Whenever you need to run a study, you’ll have real people you can call on to test, ask questions and get feedback from.

This is where you’ll need to partner other teams, likely sales and customer support. They’ll have direct access to your customers, so make sure to build a strong relationship with these teams. If you haven’t made introductions, it can helpful to put together a one-page sheet of information explaining what UX research is and the benefits of working with your team.

You may also want to consider getting in some external help. Participant recruitment services are a great way to offload the heavy lifting of sourcing quality participants – often one of the hardest parts of the research process.

6) Work out how you'll communicate your research

Perhaps one of the most important parts of being a user researcher is taking the findings you uncover and communicating them back to the wider organization. By feeding insights back to product, sales and customer support teams, you’ll form an effective link between your organization’s customers and your organization. The benefits here are obvious. Product teams can build products that actually address customer pain points, and sales and support teams will better understand the needs and expectations of customers.

Of course, it isn’t easy to communicate findings. Here are a few tips:

  • Document your research activities: With a clear record of your research, you’ll find it easier to pull out relevant findings and communicate these to the right teams.
  • Decide who needs what: You’ll probably find that certain roles (like managers) will be best served by a high-level overview of your research activities (think a one-page summary), while engineers, developers and designers will want more detailed research findings.

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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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Different ways to test information architecture

We all know that building a robust information architecture (IA) can make or break your product. And getting it right can rely on robust user research. Especially when it comes to creating human-centered, intuitive products that deliver outstanding user experiences.

But what are the best methods to test your information architecture? To make sure that your focus is on building an information architecture that is truly based on what your users want, and need.

What is user research? 🗣️🧑🏻💻

With all the will in the world, your product (or website or mobile app) may work perfectly and be as intuitive as possible. But, if it is only built on information from your internal organizational perspective, it may not measure up in the eyes of your user. Often, organizations make major design decisions without fully considering their users. User research (UX) backs up decisions with data, helping to make sure that design decisions are strategic decisions. 

Testing your information architecture can also help establish the structure for a better product from the ground up. And ultimately, the performance of your product. User experience research focuses your design on understanding your user expectations, behaviors, needs, and motivations. It is an essential part of creating, building, and maintaining great products. 

Taking the time to understand your users through research can be incredibly rewarding with the insights and data-backed information that can alter your product for the better. But what are the key user research methods for your information architecture? Let’s take a look.

Research methods for information architecture ⚒️

There is more than one way to test your IA. And testing with one method is good, but with more than one is even better. And, of course, the more often you test, especially when there are major additions or changes, you can tweak and update your IA to improve and delight your user’s experience.

Card Sorting 🃏

Card sorting is a user research method that allows you to discover how users understand and categorize information. It’s particularly useful when you are starting the planning process of your information architecture or at any stage you notice issues or are making changes. Putting the power into your users’ hands and asking how they would intuitively sort the information. In a card sort, participants sort cards containing different items into labeled groups. You can use the results of a card sort to figure out how to group and label the information in a way that makes the most sense to your audience. 

There are a number of techniques and methods that can be applied to a card sort. Take a look here if you’d like to know more.

Card sorting has many applications. It’s as useful for figuring out how content should be grouped on a website or in an app as it is for figuring out how to arrange the items in a retail store.You can also run a card sort in person, using physical cards, or remotely with online tools such as OptimalSort.

Tree Testing 🌲

Taking a look at your information architecture from the other side can also be valuable. Tree testing is a usability method for evaluating the findability of topics on a product. Testing is done on a simplified text version of your site structure without the influence of navigation aids and visual design.

Tree testing tells you how easily people can find information on your product and exactly where people get lost. Your users rely on your information architecture – how you label and organize your content – to get things done.

Tree testing can answer questions like:

  • Do my labels make sense to people?
  • Is my content grouped logically to people?
  • Can people find the information they want easily and quickly? If not, what’s stopping them?

Treejack is our tree testing tool and is designed to make it easy to test your information architecture. Running a tree test isn’t actually that difficult, especially if you’re using the right tool. You’ll  learn how to set useful objectives, how to build your tree, write your tasks, recruit participants, and measure results.

Combining information architecture research methods 🏗

If you are wanting a fully rounded view of your information architecture, it can be useful to combine your research methods.

Tree testing and card sorting, along with usability testing, can give you insights into your users and audience. How do they think? How do they find their way through your product? And how do they want to see things labeled, organized, and sorted? 

If you want to get fully into the comparison of tree testing and card sorting, take a look at our article here, which compares the options and explains which is best and when. 

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1 min read

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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