Brand Experience, Customer Experience, User Experience – These are all parts of the experiences that people have when they interact with a product, a service, or a brand. This field of experiences is very large and covers many different areas.
Experience Research tries to shine a light on this complex field. It helps us understand these experiences better. The main tools used in experience research are market research and UX (user experience) research.
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| New approaches in UX research - How AI Will Break Traditional Barriers in the Future? |
But even today, there are still many challenges and problems that block us from fully understanding our target groups. We still do not always see clearly what people want, need, or feel. How will artificial intelligence (AI) help remove these problems in the future? How will it help us understand people better?
How do Market Research and UX Research differ from each other?
Market research tries to find out if a product, idea, or service can be successful in the market. It checks if it fits customer needs and matches what customers want. Market research studies what the product is, who wants it, when they want it, and where they want it.
It takes a close look at everyone involved in the market. It also looks at the market itself, how much people want the product, and who else is offering similar products. The main goal of market research is to get helpful information. This information helps teams plan how to promote and sell the product better.
UX research mainly focuses on how easy it is to use digital products like websites and mobile apps. It pays close attention to the way users interact with these products and the reasons behind their actions. UX research carefully studies how users behave, what they expect, what they prefer, and what motivates them while using a product.
The main goal of UX research is to fully understand both current and future users. By understanding them better, the product team can gain useful insights. These insights help the team make smart decisions during the product development process. UX researchers do not work alone. They closely cooperate with the product team throughout the entire project.
Usually, UX researchers and product teams follow flexible and step-by-step methods. These methods are known as agile processes. In these processes, teams regularly test ideas, gather feedback, and improve the product together based on what they learn from real users.
At the core, both fields have one thing in common: they want to understand the person who interacts with them and with their product. They want to give this person the best experience possible.
One field uses the word “users.” The other uses the word “customers.” But both words describe the same people. These people have their own personal needs. The goal is to understand and meet these needs.
The real challenge is not to focus on an average or imaginary customer. The real challenge is to create a picture of a “real” customer. This real picture should show what actual people truly need and expect.
The question is not whether we should choose market research or UX research. The real question is how both areas can work well together and support each other. On one side, market research can learn from UX research. This is because both already use some of the same methods, involve similar participants, and apply shared tools. On the other side, Artificial Intelligence (AI) can support both fields. AI can help break the old, traditional barriers that still separate these two disciplines today.
How AI-Powered UX Research Breaks Traditional Barriers
AI (Artificial Intelligence) is becoming a powerful tool. It can help bring market research and UX (User Experience) research closer together. It also makes it easier for both teams to work with each other. Here are six important future predictions about how AI will change research.
1. AI Makes UX Research Much Faster
AI can make the entire UX research and market research process much quicker. Normally, doing a user research study takes many weeks or even months. But AI tools can now do many parts of this work automatically. They save time and make it simpler. That means you can now get important findings in just a few days—or even overnight.
UX researchers and market researchers do not need to spend as much time as before. They can get useful results faster. This allows them to run surveys more often, covering more topics. They can also make decisions more quickly. One big time-saver is that AI makes it much faster to analyze survey results.
2. AI Allows Deep Interviews at a Big Scale
AI tools help you design test ideas and study survey results. This allows researchers to run deep, open-ended interviews with many people at once. Before, these deep interviews were only done with small groups because they took a lot of time. But now, AI helps run them with large groups—and faster too.
In the past, many interesting questions were not asked. This was because there wasn’t enough time or people to ask them. But now, researchers can finally ask these questions. These answers help them understand users better. AI also helps mix deep interviews and large surveys. For example, our method called "HiQuant" lets us do A/B testing, even when only a few users visit the site.
When someone gives answers in interviews by speaking, AI tools can write down everything they say. These tools can then look at the spoken answers and find important patterns. AI also creates summaries of what users said. It adds time markers, so researchers can go to the exact part of the video where something was said. They can check or review anything they want easily.
AI sorts the findings based on how important they are for user experience. It also adds labels (tags) to each one. This makes it easy to understand and organize all the insights. Researchers can still control everything. They can see the raw data, make changes, or check anything.
This method gives researchers two big benefits. They get deep, detailed feedback from users, and they also get strong data from large numbers of people. This also helps fix the problem of representativeness, which means getting feedback that truly reflects many types of users.
3. AI Creates New Methods Like Unmoderated Interviews
AI tools now let researchers move from written surveys to spoken answers—without needing a human to guide the session. That means no more long, boring written forms. In something called "unmoderated interviews," users get written questions one at a time. They answer by speaking. They don’t need to talk to anyone live.
Users can take their time to think, change their minds, or say their thoughts clearly. They can complete the whole interview at their own pace. No one is there to rush them or make them nervous.
AI tools then listen to their answers and analyze them automatically. This process needs fewer people to moderate the interviews. It also makes collecting data faster and easier. The results are still very good.
If needed, the same people can also answer normal multiple-choice questions. This helps get both deep and clear data.
AI tools will enable UX researchers and market researchers to gain more insights. A deep understanding of users will no longer be reserved only for experts.
4. AI Creates a Safe Space for Sensitive Topics
Unmoderated interviews also offer another helpful benefit. Since users don’t talk to a real person, they feel more relaxed. They speak more openly and honestly about private or sensitive topics. They are not afraid or shy.
In one of our online studies, we used this method. Users gave us feedback like this:
“I feel free and relaxed. I don’t worry about saying something wrong.”
“It’s much better. I can be more open when I answer.”
“I liked this kind of interview. There was no real person, so I didn’t overthink my answers.”
Some users said they missed talking to a real person. They wanted to ask questions to be sure their answers were good. Others wanted a real person for the social feeling. The first two problems are easy to fix. Good interview design and clear questions can help.
The social side is a personal choice. Some people like having someone there. Others prefer to talk alone. Researchers should decide whether to include a live moderator depending on the topic and the needs of the study.
5. AI Makes It Harder to Cheat in Surveys
Speaking your answers instead of writing them also helps stop cheating. Right now, people can use AI bots to write fake survey answers. But it’s much harder to fake spoken answers. AI can help tell the difference.
Today, some people use tools like ChatGPT to give fake replies in surveys. But now, there are special AI tools that detect these fake answers. These tools are made for online surveys. They help find and stop dishonest behavior using AI.
6. AI Makes UX Research Available to Everyone
AI tools are becoming easier for everyone to use. You don’t need to be a professional researcher anymore. People from any department in a company can now plan tests, run them, and understand the results. This means more people will use UX research to help their work.
This also means UX research will support more business and market decisions than ever before. Still, expert researchers are important. They can coach others, share their knowledge, and make sure all research is done well. Companies need to clearly decide which studies can be done by other departments. More complex or sensitive topics should still be handled by trained researchers.
When everyone uses the same AI tools, the whole company benefits. It stops data from being trapped in one department. It helps different teams work better together. It also makes it easy to share knowledge and insights across the whole company.
AI Supports Decisions Based on Real User Needs
All of these changes help companies make better choices. When businesses use AI tools to understand users, they gain a big advantage. UX researchers and marketers can learn more about users than ever before.
AI makes research easier to access. So, deep knowledge about users is no longer limited to a small expert group. Everyone in a company can use it. This leads to a full, complete picture of users in many situations. As a result, companies make better, data-driven decisions. They can meet the needs of users more effectively and succeed in a competitive world.
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