5 key areas for effective ResearchOPs

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.

‍

Continue reading

‍

• Selling your design recommendations to clients and colleagues – Guest writer Jeff Tang outlines some of his techniques for presenting UX recommendations and answering objections.
• Quantifying the ROI of UX – Ashlea McKay delves into one of the toughest UX questions to answer: “What do we get for our money?”
• How do I explain what UX is? – Ashlea McKay covers how you can explain UX to people who may not necessarily have familiarity with the field.

Introduction

‍

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. 

‍

A great user experience (UX) is one of the largest drivers of growth and revenue through user satisfaction. However, when budgets get tight, or there is a squeeze on timelines, user research is one of the first things to go. Often at the cost of user satisfaction.  

‍

This short sighted view can mean project managers are preoccupied with achieving milestones and short term goals. And UX teams get stuck researching products they weren’t actually involved with developing. As a result no one has the space and understanding to really develop a product that speaks to users needs, desires and wants. There must  be a better way to produce a product that is user-driven.  Thankfully there is.

‍

What is user research and why should project managers care about it? 👨🏻💻

‍

User research is an important part of the product development process. Primarily, user research involves using different research methods to gather information about your end users. 

‍

Essentially it aims to create the best possible experience for your users by listening and learning directly from those that already or potentially will use your product. You might conduct interviews to help you understand a particular problem, carry out a tree test to identify bottlenecks or problems in your navigation, or do some usability testing to directly observe your users as they perform different tasks on your website or in your app. Or a combination of these to understand what users really want.

‍

To a project manager and team, this likely sounds fairly familiar, that any project can’t be managed in a silo. Regular check-ins and feedback are essential to making smart decisions. The same with UX research. It can make the whole process quicker and more efficient. By taking a step back, digging into your users’ minds, and gaining a fuller understanding of what they want upfront, it can curtail short-term views and decisions.

‍

Bringing more user research into your development process has major benefits for the team, and the ultimately the quality of that final product. There are three key benefits:

‍

1. Saves your development team time and effort. Ensuring the team is working on what users want, not wasting time on features that don’t measure up.
2. Gives your users a better experience by meeting their requirements.
3. Helps your team innovate quickly by understanding what users really want.

‍

As a project manager, making space and planning for user research can be one of the best ways to ensure the team is creating a product that truly is user-driven.

‍

How to bring research into your product development process 🤔

‍

There are a couple of ways you can bring UX research into your product development process. 

‍

1. Start with a dedicated research project.
2. Integrate UX research throughout the development project.

‍

It can be more difficult to integrate UX research throughout the process, as it means planning the project with various stages of research built in to check the development of features. But ultimately this approach is likely to turn out the best product. One that has been considered, checked and well thought out through the whole product development process. To help you on the way we have laid out 6 key steps to help you integrate UX research into your product development process.

‍

6 key steps to integrate UX research 👟

‍

Step 1: Define your research questions

‍

Take a step back, look at your product and define your research questions. 

‍

It may be tempting just to ask, ‘do users like our latest release?’ This however does not get to why or what your users like or don’t like. Try instead:

• What do our users really want from our product?
• Where are they currently struggling while using our website?
• How can we design a better product for our users?

‍

These questions help to form the basis of specific questions about your product and specific areas of research to explore which in turn help shape the type of research you undertake.

‍

Step 2: Create your research plan

‍

With a few key research questions to focus on, it’s time to create your research plan.

‍

A great research plan covers your project’s goals, scope, timing, and deliverables. It’s essential for keeping yourself organized but also for getting key stakeholder signoff.

‍

Step 3: Prepare any research logistics

‍

Every project plan requires attention to detail including a user research project. And with any good project there are a set of steps to help make sense of it.

‍

1. Method: Based on your questions, what is the best user research method to use? 
2. Schedule: When will the research take place? How long will it go on for? If this is ongoing research, plan how it will be implemented and how often.
3. Location: Where will the research take place? 
4. Resources: What resources do you need? This could be technical support or team members.
5. Participants: Define who you want to research. Who is eligible to take part in this research? How will you find the right people?
6. Data: How will you capture the research data? Where will it be stored? How will you analyze the data and create insights and reports that can be used?
7. Deliverables: What is the ultimate goal for your research project?

‍

Step 4: Decide which method will be used

‍

Many user research methods benefit from an observational style of testing. Particularly if you are looking into why users undertake a specific task or struggle.

‍

Typically, there are two approaches to testing:

‍

1. Moderated testing is when a moderator is present during the test to answer questions, guide the participant, or dig deeper with further questions.
2. Unmoderated testing is when a participant is left on their own to carry out the task. Often this is done remotely and with very specific instructions.Your key questions will determine which method will works best for your research.  Find our more about the differences.

‍

Step 5: Run your research session

‍

It’s time to gather insights and data. The questions you are asking will influence how you run your research sessions and the methods you’ve chosen. 

‍

If you are running surveys you will be asking users through a banner or invitation to fill out your survey. Unmoderated and very specific questions. Gathering qualitative data and analyzing patterns.

‍

If you’re using something qualitative like interviews or heat mapping, you’ll want to implement software and gather as much information as possible.

‍

Step 6: Prepare a research findings report and share with stakeholders

‍

Analyze your findings, interrogate your data and find those insights that dive into the way your users think. How do they love your product? But how do they also struggle?

‍

Pull together your findings and insights into an easy to understand report. And get socializing. Bring your key stakeholders together and share your findings. Bringing everyone across the findings together can bring everyone on the journey. And for the development process can mean decisions can be user-driven. 

‍

Wrap Up 🥙

‍

Part of any project, UX research should be essential to developing a product that is user-driven. Integrating user research into your development process can be challenging. But with planning and strategy it can be hugely beneficial to saving time and money in the long run.