April 24, 2019
6 min

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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5 key areas for effective ResearchOPs

Simply put, ResearchOps is about making sure your research operations are robust, thought through and managed. 

Having systems and processes around your UX research and your team keep everyone (and everything) organized. Making user research projects quicker to get started and more streamlined to run. And robust sharing, socializing, and knowledge storage means that everyone can understand the research insights and findings and put these to use - across the organization. And even better, find these when they need them. 

Using the same tools across the team allows the research team to learn from each other, and previous research projects and be able to compare apples with apples, with everyone included. Bringing the team together across tools, research and results.

We go into more detail in our ebook ResearchOps Checklist about exactly what you can do to make sure your research team is running at its best. Let’s take a quick look at 5 way to ensure you have the grounding for a successful ResearchOps team.

1. Knowledge management 📚

What do you do with all of the insights and findings of a user research project? How do you store them, how do you manage the insights, and how do you share and socialize?

Having processes in place that manage this knowledge is important to the longevity of your research. From filing to sharing across platforms, it all needs to be standardized so everyone can search, find and share.

2. Guidelines and process templates 📝

Providing a framework for how to run research projects is are important. Building on the knowledge base from previous research can improve research efficiencies and cut down on groundwork and administration. Making research projects quicker and more streamlined to get underway.

3. Governance 🏛

User research is all about people, real people. It is incredibly important that any research be legal, safe, and ethical. Having effective governance covered is vital.

4. Tool stack 🛠

Every research team needs a ‘toolbox’ that they can use whenever they need to run card sorts, tree tests, usability tests, user interviews, and more. But which software and tools to use?

Making sure that the team is using the same tools also helps with future research projects, learning from previous projects, and ensuring that the information is owned and run by the organization (rather than whichever individuals prefer). Reduce logins and password shares, and improve security with organization-wide tools and platforms. 

5. Recruitment 👱🏻👩👩🏻👧🏽👧🏾

Key to great UX research is the ability to recruit quality participants - fast! Having strong processes in place for screening, scheduling, sampling, incentivizing, and managing participants needs to be top of the list when organizing the team.

Wrap Up 💥

Each of these ResearchOps processes are not independent of the other. And neither do they flow from one to the other. They are part of a total wrap around for the research team, creating processes, systems and tools that are built to serve the team. Allowing them to focus on the job of doing great research and generating insights and findings that develop the very best user experience. 

Afterall, we are creating user experiences that keep our users engaged and coming back. Why not look at the teams user experience and make the most of that. Freeing time and space to socialize and share the findings with the organization. 

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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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1 min read

User research and agile squadification at Trade Me

Hi, I’m Martin. I work as a UX researcher at Trade Me having left Optimal Experience (Optimal Workshop's sister company) last year. For those of you who don’t know, Trade Me is New Zealand’s largest online auction site that also lists real estate to buy and rent, cars to buy, jobs listings, travel accommodation and quite a few other things besides. Over three quarters of the population are members and about three quarters of the Internet traffic for New Zealand sites goes to the sites we run.

Leaving a medium-sized consultancy and joining Trade Me has been a big change in many ways, but in others not so much, as I hadn’t expected to find myself operating in a small team of in-house consultants. The approach the team is taking is proving to be pretty effective, so I thought I’d share some of the details of the way we work with the readers of Optimal Workshop’s blog. Let me explain what I mean…

What agile at Trade Me looks like

Over the last year or so, Trade Me has moved all of its development teams over to Agile following a model pioneered by Spotify. All of the software engineering parts of the business have been ‘squadified’. These people produce the websites & apps or provide and support the infrastructure that makes everything possible.Across Squads, there are common job roles in ‘Chapters’ (like designers or testers) and because people are not easy to force into boxes, and why should they be, there are interest groups called ‘Guilds’.The squads are self-organizing, running their own processes and procedures to get to where they need to. In practice, this means they use as many or as few of the Kanban, Scrum, and Rapid tools they find useful. Over time, we’ve seen that squads tend to follow similar practices as they learn from each other.

How our UX team fits in

Our UX team of three sits outside the squads, but we work with them and with the product owners across the business.How does this work? It might seem counter-intuitive to have UX outside of the tightly-integrated, highly-focused squads, sometimes working with product owners working on stuff that might have little to do with what’s being currently developed in the squads. This comes down to the way Trade Me divides down the UX responsibilities within the organization. Within each squad there is a designer. He or she is responsible for how that feature or app looks, and, more importantly, how it acts — interaction design as well as visual design.Then what do we do, if we are the UX team?

We represent the voice of Trade Me’s users

By conducting research with Trade Me’s users we can validate the squads’ day-to-day decisions, and help frame decisions on future plans. We do this by wearing two hats. Wearing the pointy hats of structured, detailed researchers, we look into long-term trends: the detailed behaviours and goals of our different audiences. We’ve conducted lots of one-on-one interviews with hundreds of people, including top sellers, motor parts buyers, and job seekers, as well as running surveys, focus groups and user testing sessions of future-looking prototypes. For example, we recently spent time with a number of buyers and sellers, seeking to understand their motivations and getting under their skin to find out how they perceive Trade Me.

This kind of research enables Trade Me to anticipate and respond to changes in user perception and satisfaction.Swapping hats to an agile beanie (and stretching the metaphor to breaking point), we react to the medium-term, short-term and very short-term needs of the squads testing their ideas, near-finished work and finished work with users, as well as sometimes simply answering questions and providing opinion, based upon our research. Sometimes this means that we can be testing something in the afternoon having only heard we are needed in the morning. This might sound impossible to accommodate, but the pace of change at Trade Me is such that stuff is getting deployed pretty much every day, many of which affects our users directly. It’s our job to ensure that we support our colleagues to do the very best we can for our users.

How our ‘drop everything’ approach works in practice

Screen Shot 2014-07-11 at 10.00.21 am

We recently conducted five or six rounds (no one can quite remember, we did it so quickly) of testing of our new iPhone application (pictured above) — sometimes testing more than one version at a time. The development team would receive our feedback face-to-face, make changes and we’d be testing the next version of the app the same or the next day. It’s only by doing this that we can ensure that Trade Me members will see positive changes happening daily rather than monthly.

How we prioritize what needs to get done

To help us try to decide what we should be doing at any one time we have some simple rules to prioritise:

  • Core product over other business elements
  • Finish something over start something new
  • Committed work over non-committed work
  • Strategic priorities over non-strategic priorities
  • Responsive support over less time-critical work
  • Where our input is crucial over where our input is a bonus

Applying these rules to any situation makes the decision whether to jump in and help pretty easy.At any one time, each of us in the UX team will have one or more long-term projects, some medium-term projects, and either some short-term projects or the capacity for some short-term projects (usually achieved by putting aside a long-term project for a moment).

We manage our time and projects on Trello, where we can see at a glance what’s happening this and next week, and what we’ve caught sniff of in the wind that might be coming up, or definitely is coming up.On the whole, both we and the squads favour fast response, bulleted list, email ‘reports’ for any short-term requests for user testing.  We get a report out within four hours of testing (usually well within that). After all, the squads are working in short sprints, and our involvement is often at the sharp end where delays are not welcome. Most people aren’t going to read past the management summary anyway, so why not just write that, unless you have to?

How we share our knowledge with the organization

Even though we mainly keep our reporting brief, we want the knowledge we’ve gained from working with each squad or on each product to be available to everyone. So we maintain a wiki that contains summaries of what we did for each piece of work, why we did it and what we found. Detailed reports, if there are any, are attached. We also send all reports out to staff who’ve subscribed to the UX interest email group.

Finally, we send out a monthly email, which looks across a bunch of research we’ve conducted, both short and long-term, and draws conclusions from which our colleagues can learn. All of these latter activities contribute to one of our key objectives: making Trade Me an even more user-centred organization than it is.I’ve been with Trade Me for about six months and we’re constantly refining our UX practices, but so far it seems to be working very well.Right, I’d better go – I’ve just been told I’m user testing something pretty big tomorrow and I need to write a test script!

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