Radical Collaboration: how teamwork really can make the dream work

Wireframes. Mockups. HTML. Fonts. Elements. Users. If you’re familiar with user interface design, these terms will be your bread and butter.An integral part of any website or application, user interface design is also arguably one of the most important. This is because your design is what your users see and interact with. If your site or app functions poorly and looks terrible, that’s what your users are going to remember.

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But isn’t UX design and UI design the same thing? Or is there just an extremely blurred line between the two? What’s involved with UI design and, more importantly, what makes good design?

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What is UI design exactly?

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If you’re wondering how to test UI on your website, it’s a good idea to first learn some of the differences between UX and UI design. Although UI design and UX design look similar when written down, they’re actually two totally separate things. However, they should most definitely complement each other.

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UX design, according to Nielsen Norman Group, “encompasses all aspects of the end-user's interaction with the company, its services, and its products.” Meanwhile, UI design focuses more on a user’s interaction, the overall design, look and feel of a system. The two still sound similar, right?For those of you still trying to wrap your ahead around the difference, Nielsen Norman Group has a great analogy up on its site that helps to explain it:

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"As an example, consider a website with movie reviews. Even if the UI for finding a film is perfect, the UX will be poor for a user who wants information about a small independent release if the underlying database only contains movies from the major studios.”

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This just goes to show the complementary relationship between the two and why it’s so important.User interface was popularized in the early 1970s, partly thanks to Fuji Xerox’s ‘Xerox Alto Workstation’ — an early personal computer dubbed “the origin of the PC”. This machine used various icons, multi windows, a mouse, and e-mail, which meant that some sort of design and design principles were needed to create consistency for the future. It was here that human-centred UI was born. UI design also covers graphical user interface design (GUI design). A GUI is the software or interface that works as the medium between a user and the computer.

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It uses a number of graphical elements, such as screen cursors, menus, and icons so that users can easily navigate a system. This is also something that has stemmed from Fuji Xerox back in the late 1970s and early 1980s.Since then, UI has developed quickly and so has its design principles. When the Xerox Alto Workstation was first born, Fuji Xerox came up with eight of its own design principles. These were:

• Metaphorically digitize the desk environment
• Operating on display instead of entering on keyboard
• What you see is what you get
• Universal but fewer commands
• Same operation for the same job at different places
• Operating computers as easily as possible
• No need to transfer to different jobs
• System customized as desired by users

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Over time, these principles have evolved and now you’ll likely find many more added to this list. Here are just a few of the most important ones identified in “Characteristics of graphical and web user interfaces” by Wilbert Galitz.

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UI design principles:

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Principle #1: Clarity

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Usability.gov says that the “best interfaces are almost invisible to the user”.Everything in the system, from visual elements, functions, and text, needs to be clear and simple. This includes layout as well as the words used — stay away from jargon and complex terms or analogies that users won’t understand.Aesthetic appeal also fits into this principle. Ensure colors and graphics are used in a simple manner, and elements are grouped in a way that makes sense.

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Principle #2: Consistency

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The system should have the same or similar functions, uses and look throughout it for consistency. For example, the same color scheme should be used throughout an app, or the terminology on a website should be consistent throughout. Users should also have an idea of what to expect when they use your system. As an example, picture a retail shopping app. You’d expect that any other retail shopping app out there will have similar basic functions: a place to log in or create an account, account settings, a way to navigate and browse stock, a way to purchase stock at the press of a button. However, this doesn’t mean copying another app or website exactly; there should just be consistency so users know what to expect when they encounter your system.Apple even states an “app should respect its users and avoid forcing them to learn new ways to do things for no other reason than to be different”.

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Principle #3: Flexibility and customizability

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Is there more than one way people can access your system and its functions? Can people perform tasks in a number of different ways, too?Providing your users with a flexible system means people are more in control of what they’re doing. Galitz mentions this can also be done through allowing system customization.Don’t forget use on other kinds of devices, too. In a time when Google is using mobile-friendliness as a ranking signal, and research from Ericsson shows smartphones accounted for 75% of all mobile phone sales in Q4 2015, you know that being flexible is important.

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Examples of good UI design

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For a list of some of the best user interface examples, check out last year’s Webby Awards category for Best Interface Design. The 2016 category winner was the Reuters TV Web App, while the People’s Choice winner was AssessYourRisk.org.As an aside, this is the second year that the Webby Awards introduced this category — just goes to show how important it is to have good UI design!While you don’t want your site or application to look exactly the same as these winners, you still want yours to function well and be aesthetically pleasing.

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To help you get there, there are a number of UI design tools and UI software available.Here’s a list of some of the many out there:

• UXPin - An online UI design tool that allows you to create wireframes, mockups, and prototypes all on one platform.
• InVision - A prototyping and collaboration tool. More in-depth than Balsamiq, and it allows you to go from mockup to high-fidelity in minutes.
• Balsamiq - A simple mockups tool for wireframing, which allows users to test out ideas in the early stage of interface design.
• Atomic - An interface design tool that allows you to design in your browser and collaborate with others on your projects.

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Have you got any favorite UI design examples, or tips for beautiful design? We’d love to see them — comment below and let us know!

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Further reading

• “UI, UX: Who does what? A designer’s guide to the tech industry” - An article from Fastcodesign that provides a breakdown of some of the design roles in the tech industry.
• “7 rules for creating gorgeous UI (Part 1)” - A great guide from Erik D. Kennedy for creating good UI design, with some fantastic tips to help you get started.
• “10 usability heuristics for user interface design” - An article from Nielsen Norman Group’s Principal Jakob Nielsen explaining some of the key principles to keep in mind when designing a user interface.

Introduction

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User Experience (UX) research has always been about people. How they think, how they behave, what they need, and—just as importantly—what they don’t yet realise they need. Traditional UX methodologies have long relied on direct human input: interviews, usability testing, surveys, and behavioral observation. The assumption was clear—if you want to understand people, you have to engage with real humans.

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But in 2025, that assumption is being challenged.

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The emergence of digital twins and synthetic users—AI-powered simulations of human behavior—is changing how researchers approach user insights. These technologies claim to solve persistent UX research problems: slow participant recruitment, small sample sizes, high costs, and research timelines that struggle to keep pace with product development. The promise is enticing: instantly accessible, infinitely scalable users who can test, interact, and generate feedback without the logistical headaches of working with real participants.

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Yet, as with any new technology, there are trade-offs. While digital twins may unlock efficiencies, they also raise important questions: Can they truly replicate human complexity? Where do they fit within existing research practices? What risks do they introduce?

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This article explores the evolving role of digital twins in UX research—where they excel, where they fall short, and what their rise means for the future of human-centered design.

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The Traditional UX Research Model: Why Change?

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For decades, UX research has been grounded in methodologies that involve direct human participation. The core methods—usability testing, user interviews, ethnographic research, and behavioral analytics—have been refined to account for the unpredictability of human nature.

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This approach works well, but it has challenges:

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1. Participant recruitment is time-consuming. Finding the right users—especially niche audiences—can be a logistical hurdle, often requiring specialised panels, incentives, and scheduling gymnastics.
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2. Research is expensive. Incentives, moderation, analysis, and recruitment all add to the cost. A single usability study can run into tens of thousands of dollars.
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3. Small sample sizes create risk. Budget and timeline constraints often mean testing with small groups, leaving room for blind spots and bias.
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4. Long feedback loops slow decision-making. By the time research is completed, product teams may have already moved on, limiting its impact.

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

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Digital twins and synthetic users aim to change that.

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What Are Digital Twins and Synthetic Users?

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

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Digital Twins: Simulating Real-World Behavior

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A digital twin is a data-driven virtual representation of a real-world entity. Originally developed for industrial applications, digital twins replicate machines, environments, and human behavior in a digital space. They can be updated in real time using live data, allowing organisations to analyse scenarios, predict outcomes, and optimise performance.

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In UX research, human digital twins attempt to replicate real users' behavioral patterns, decision-making processes, and interactions. They draw on existing datasets to mirror real-world users dynamically, adapting based on real-time inputs.

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Synthetic Users: AI-Generated Research Participants

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While a digital twin is a mirror of a real entity, a synthetic user is a fabricated research participant—a simulation that mimics human decision-making, behaviors, and responses. These AI-generated personas can be used in research scenarios to interact with products, answer questions, and simulate user journeys.

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Unlike traditional user personas (which are static profiles based on aggregated research), synthetic users are interactive and capable of generating dynamic feedback. They aren’t modeled after a specific real-world person, but rather a combination of user behaviors drawn from large datasets.

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Think of it this way:

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• A digital twin is a highly detailed, data-driven clone of a specific person, customer segment, or process.
• A synthetic user is a fictional but realistic simulation of a potential user, generated based on behavioral patterns and demographic characteristics.

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

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Where Digital Twins and Synthetic Users Fit into UX Research

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

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• Scale instantly – Test designs with thousands of simulated users, rather than just a handful of real participants.
• Eliminate recruitment bottlenecks – No need to chase down participants or schedule interviews.
• Reduce costs – No incentives, no travel, no last-minute no-shows.
• Enable rapid iteration – Get user insights in real time and adjust designs on the fly.
• Generate insights on sensitive topics – Synthetic users can explore scenarios that real participants might find too personal or intrusive.

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These capabilities make digital twins particularly useful for:

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• Early-stage concept validation – Rapidly test ideas before committing to development.
• Edge case identification – Run simulations to explore rare but critical user scenarios.
• Pre-testing before live usability sessions – Identify glaring issues before investing in human research.

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However, digital twins and synthetic users are not a replacement for human research. Their effectiveness is limited in areas where emotional, cultural, and contextual factors play a major role.

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The Risks and Limitations of AI-Driven UX Research

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

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1. They lack genuine emotional responses.
   AI can analyse sentiment, but it doesn’t feel frustration, delight, or confusion the way a human does. UX is often about unexpected moments—the frustrations, workarounds, and “aha” realisations that define real-world use.
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2. Bias is a real problem.
   AI models are trained on existing datasets, meaning they inherit and amplify biases in those datasets. If synthetic users are based on an incomplete or non-diverse dataset, the research insights they generate will be skewed.
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3. They struggle with novelty.
   Humans are unpredictable. They find unexpected uses for products, misunderstand instructions, and behave irrationally. AI models, no matter how advanced, can only predict behavior based on past patterns—not the unexpected ways real users might engage with a product.
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4. They require careful validation.
   How do we know that insights from digital twins align with real-world user behavior? Without rigorous validation against human data, there’s a risk of over-reliance on synthetic feedback that doesn’t reflect reality.

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A Hybrid Future: AI + Human UX Research

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

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Where AI Can Lead:

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

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Where Humans Remain Essential:

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

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

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Final Thoughts: Proceeding With Caution and Curiosity

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Digital twins and synthetic users are exciting, but they are not a magic bullet. They cannot fully replace human users, and relying on them exclusively could lead to false confidence in flawed insights.

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

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As with any new technology, thoughtful implementation is key. The real opportunity lies in designing research methodologies that harness the speed and scale of AI without losing the depth, nuance, and humanity that make UX research truly valuable.

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The challenge ahead isn’t about choosing between human or synthetic research. It’s about finding the right balance—one that keeps user experience truly human-centered, even in an AI-driven world.

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

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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.

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We’ve talked a lot about the various types of research tools that help improve these outcomes. 

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There is a rising research trend in 2021.

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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.

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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.

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Mixed Method UX research is the research trend of 2021

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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.

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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.

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And with all of these massive organizations making the move to increase their data collection and research teams. Why wouldn’t you?

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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.

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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.

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What is mixed method research?

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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. 

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Quantitative research is the tangible numbers and metrics that can be gathered through user research such as card sorting or tree testing.

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Qualitative research is research around users’ behaviour and experiences. This can be through usability tests, interviews or surveys.

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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.

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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. 

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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.

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How to do mixed method research

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Take a look at our article for more examples of the uses of mixed method research. 

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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.

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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.

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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. 

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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.

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A fuller picture, means a better understanding.

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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.

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Upcoming research trends to watch

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Keeping an eye on the progression of the mixed method research trend, will mean keeping an eye on these:

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1. Integrated Surveys

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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. 

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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. 

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2. Return to the social research

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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.

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However, now with the advent of online research tools this can also be a way to round out mixed method research.

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3. Co-creation

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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.

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4. Owned Panels & Community

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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.

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What does this all mean for me

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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. 

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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.

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One of the biggest challenges with user research can be the coordination and participant recruitment. That’s where we come in.

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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.

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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.

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Why not get in deeper with mixed method research today!