Market Research Trends: What We’re Seeing Now
Market research is having its moment.
Companies have more ways than ever to understand their customers…and frankly, more reasons to! Products move faster. Markets change faster. Customer expectations shift. New competitors can emerge seemingly overnight. And AI is changing how just about everyone works.
That makes really understanding your customers more valuable than ever.
It’s also opening up a lot of new possibilities for what market research can look like.
Companies can combine qualitative and quantitative methods more fluidly, build ongoing relationships with customers they want to learn from, analyze enormous amounts of open-ended feedback, and get answers fast enough to influence decisions while they’re still being made.
Of course, AI is part of that story. It can help researchers work through enormous amounts of information faster—transcribing interviews, organizing open-ended responses, summarizing existing research, and handling some of the more time-consuming parts of analysis.
But we’ve also found there are places where AI still falls short. Identifying the patterns that actually matter, understanding the nuance behind what someone said, and recognizing when an unexpected finding is worth digging into still require a human researcher.
But AI is just a part of a much bigger story.
What we find most fascinating is that the lines between qualitative and quantitative research are getting blurrier. Also, proprietary customer knowledge is becoming increasingly valuable, especially as brands look for ways to create original ideas and content rather than simply regurgitating what everyone else is saying. Expectations around respondent experience, privacy, and data quality are on the rise. And researchers have better tools than ever to connect the dots between what customers say, what they do, and what businesses already know about their behavior.
To us, that’s what makes this such an interesting moment for market research. We have more tools, more data, more ways to reach people, and more ways to make sense of what we learn.
But the goal hasn’t changed.
Good market research has always been about giving businesses more than just data; it’s about giving them the evidence they need to make better decisions. That’s always been a differentiator for Bixa—the fact that we can turn data into actionable “so whats” and next steps.
What’s changing now is how many ways we have to get there.
Here are seven trends shaping what that looks like in 2026:
1. Research is becoming more continuous
Traditional market research has often been organized around discrete, fixed-scope projects.
A company has a specific question, so it commissions a study. Researchers spend several weeks recruiting participants, collecting data, analyzing the findings, and then they put together a report. The company reviews that report and makes a decision on what to do next.
And sometimes, that model still makes perfect sense.
But more and more, we’re seeing companies build research into their business on a routine cadence.
Instead of checking in with customers once every year or two, they’re tracking the same questions monthly or quarterly. They’re watching how brand perceptions change. They’re measuring shifts in customer needs and behaviors. They’re going back to the same audiences over time, rather than starting from scratch every time a new question comes up.
Brand tracking is a great example. A single brand study gives you a snapshot that says “This is where our brand stands today.” But a tracker measures over time, and gives you the movie, not just a thumbnail. You can see how awareness is growing, how perceptions are changing, how competitors are moving, and whether the things you’re doing in the market are actually having an impact downstream.
But this isn’t just for brand research. We’re seeing companies set up quarterly customer pulse studies far more often than they used to. They are asking us to maintain ongoing insight communities, regularly talk with key customer segments, and create recurring research programs around the business questions they need to keep an eye on.
And what they are finding is that there’s a major compounding benefit to conducting research this way. Each study doesn’t have to exist in isolation. What you learn this quarter gives you context for what you see next quarter. An unexpected finding can become something you deliberately track. And over time, you build a much richer understanding of your customers than you could get from any single project.
Is this more expensive than running a survey once a year? Of course. But for the right business questions, it’s also far more valuable.
A once-a-year study tells you where things stand at one moment in time. What it can’t tell you is when something started to change—or why. If brand consideration drops 10 points between annual studies, you know something happened. You just don’t know whether it happened two weeks ago or ten months ago, whether the decline was gradual or sudden, or what else was happening in the market when it occurred.
With consistent research, you can actually see those shifts as they happen (and often faster than your sales data reflects the same changes). You can connect them to a campaign, a product launch, a pricing change, a new competitor, or something happening in the broader market.
And more importantly, you have a chance to DO something about what you’re learning instead of discovering it months later.
That’s where the additional investment starts to pay for itself. You’re not simply buying more research. You’re building a system that helps you understand what’s changing, what’s driving it, and when you may need to respond.
To us, that’s the bigger shift: research is becoming less of something companies commission when they have a question, and more of something they use consistently to stay close to their customers.
We’re shifting away from: “What research question should we ask next?”
And toward: “What do we need to keep learning from our customers over time?”
2. Qualitative and quantitative research are becoming less siloed
For decades, qualitative and quantitative market research have been treated like separate disciplines.
Qualitative research gives you depth, and hinges on interviews, focus groups, observation, and open-ended exploration.
Quantitative research gives you scale, and is driven by surveys, numerical measurement, and statistical validation.
But real business questions rarely fit neatly into one bucket or the other.
A client almost never comes to us and says, “I have a qualitative research question.” (It would be great if they did, ha! But it almost never happens that way.) Far more often, our clients come to us because customers aren’t converting. Or they’re considering a new product. Or they need to know which audience to target.
Our job is to figure out what evidence will actually help them answer one of these high-impact questions.
For example, a company’s customer retention may have recently tumbled. Quantitative data can tell company leaders which customer segments are leaving, when they leave, and how large the decline is. But it takes qualitative research to help them understand why.
On the other hand, qualitative interviews might uncover an unexpected problem with a product’s design. Great. Now we know the problem exists. But is it affecting 5% of customers or 50%? That’s a completely different business problem—and one quantitative research can help answer.
That’s why the distinction between “qual researcher” and “quant researcher” matters less than it used to. The methodology should follow the question, not the other way around.
Sometimes that means qual followed by quant. Sometimes quant uncovers a question that sends researchers back into qual. And sometimes the best answer comes from putting behavioral data, interviews, survey results, and information the business already has side by side.
The goal isn’t to choose the “right” methodology. It’s to build the right body of evidence for the high-impact decision at hand.
3. In an AI world where information is increasingly commoditized, original customer knowledge becomes more valuable
Companies have never had access to more data, but more and more, everyone has access to the same information. It’s being regurgigated online and through LLMs like an endless potluck where everyone somehow brought the same casserole.
Ask ChatGPT a question about your market and your competitors can ask it the exact same question. Search for industry trends and everyone else can find the same reports. AI has made it remarkably easy to find, summarize, and synthesize what’s already known.
What it can’t give you is something no one else knows.
And that makes information that isn’t available to everyone a whole lot more valuable.
Interview your customers and you might uncover a need your competitors haven’t noticed. Conduct a segmentation study and you may discover that the market breaks apart very differently than everyone assumes. Track your brand over time and you can see shifts in perception that aren’t visible anywhere else. Survey your target market and you can answer the specific questions your business is wrestling with—not the questions someone else happened to research and publish.
That information is proprietary to your business. And more and more every day, it’s clear that’s where the real value is.
There’s a content advantage here, too. Have you noticed that 99% of the blog posts out there seem to be a photocopy of a photocopy of a photocopy? AI has made it ridiculously easy to produce more content, but if everyone is creating from the same pool of publicly available information (and specifically, the publicly available information that’s being pulled into the LLMs), we wind up with a whole lot of companies saying essentially the same things.
Original research gives companies something genuinely new to say. It can uncover unexpected findings, challenge conventional wisdom, reveal the language customers actually use, and give a brand an original point of view backed by evidence.
The competitive advantage isn’t having access to more information anymore. Everyone has information.The advantage is knowing something meaningful about your customers that your competitors don’t.
4. Research quality can’t ignore the respondent experience
Researchers outreach a lot of respondents.
Click a series of links, they ask.
Now answer 45 questions.
Do you “somewhat agree” or “strongly agree” with each of these 17 statements that sound almost exactly the same?
Oh, and when you’re finished with that, here’s a giant grid with 20 more.
Still with us? Perfect. Only 32% of the survey left!
We would never design a customer experience this way. So why do we keep designing research experiences this way?
At the end of the day, research participants are consumers too. They spend all day using apps and websites that have been obsessively optimized to make things fast, easy, intuitive, and sometimes even fun. And then all of a sudden, we throw at them a 25-minute survey that feels like doing their taxes…at the DMV.
You might be thinking “Aw, that’s so nice” but respondent experience isn’t just about being nice to the people taking your survey (though that’s a bonus). Respondent experience drastically affects the quality of the data we get back.
Give someone a tedious survey and they’ll start speeding through it. Give them the same question six different ways and shockingly, they may stop thinking carefully about every answer. Ask them to complete an overly complicated diary activity every day for two weeks and eventually you’re going to lose people—or get increasingly half-hearted responses from the ones who stick around.
This is why our team is so excited about approaches that make participating in research feel more natural—like mobile-first research, conversational surveys, video responses, shorter iterative activities, and asynchronous qualitative research that lets people respond in the context of their actual lives.
Sometimes that means asking someone to record a 60-second video from their kitchen instead of scheduling a formal interview. Sometimes it means breaking a giant survey into smaller pieces. And sometimes it just means asking yourself whether you really need question 47.
The idea is refreshingly simple:
If we want people to give us thoughtful, honest answers, we need to design research experiences that make thoughtful, honest participation reasonable.
Better respondent experiences don’t just make participants happier. They make the research better.
5. Data quality is moving from a technical concern to a business risk
You can design the most beautiful research study in the world: Perfect methodology. Perfect questions. Perfect analysis.
None of it matters if the people answering your questions aren’t actually the people you think they are.
And right now, figuring that out is getting harder. Online fraud is wildly sophisticated, incentives attract professional survey takers and people who will say just about anything to qualify, and we can’t tell you how many qualitative screeners we’ve watched in the past year where the person, on a video, was literally reading off of ChatGPT.
It’s bad enough when you can see people reading—when their eyes go back and forth, or when the monitor literally reflects a Claude response in their glasses. At one point, our researchers shared a big laugh when a respondent read out loud, “If you were a marketing manager for a hypothetical company…” and didn't realize she wasn't supposed to read that part.
Suddenly, “Who exactly answered this survey?” is a much more complicated question than it sounds.
And this isn’t just a research problem. It’s a business problem.
If you’re using research to decide what product to build, what to charge, how to position your brand, where to invest millions of dollars, or whether to enter an entirely new market, you probably want to make sure the 500 “customers” behind that recommendation are, you know, actual customers.
That means some of the least glamorous parts of research are becoming some of the most important:
Who participated in the study?
How were they recruited?
How do we know they actually qualify?
What quality checks were used?
Which responses were removed—and why? And just when we thought data provenance couldn’t get any more interesting, along came synthetic respondents.
AI-generated respondents and digital twins may be genuinely useful for some applications. We personally think there may be real value in using them to explore hypotheses, pressure-test ideas, or get an early, directional read before investing in human research.
But a synthetic person is not a person.
If we’re looking at a finding based on 500 actual customers, we want to know that. If it’s based on behavioral data, we want to know that. And if 500 AI-generated personas simulated what they think those customers might say, we definitely want to know that.
That doesn’t mean one source of evidence is automatically useful and another is useless. It means we need to understand what we’re looking at before we decide how much weight to put behind it.
Because ultimately, research is supposed to reduce uncertainty around a business decision.
So if we’re uncertain about where the data came from in the first place, we have a pretty darn big problem.
Data quality and provenance can’t be something we bury in the methodology appendix anymore. If a business is going to make an important decision based on the answer, it deserves to know who—or, increasingly, what—that answer came from.
6. AI is now part of the research infrastructure
We all agree: we can officially stop talking about AI like it’s a shiny new thing researchers are experimenting with. It’s here. We’re using it. And increasingly, it’s being built directly into the research tools we already use.
Qualtrics’ 2026 research, based on more than 3,000 researchers across 17 countries, found that 95% were either regularly using or experimenting with AI to complete day-to-day tasks. At the same time, use of general-purpose AI tools, like ChatGPT, declined while AI capabilities embedded into specialized research platforms increased.
That makes sense. AI is becoming less of a separate tool you go use and more like a layer underneath the work we’re already doing.
Researchers are also turning to AI to help with tasks such as drafting surveys, processing open-ended responses, creating first drafts, automating repetitive tasks, and generally eliminating some of the nitty-gritty work that used to take forever.
We’re all for it. At Bixa, we use AI every day. There are things it makes dramatically faster—things which, frankly, we never want to do manually again. But there’s a really important distinction between using AI to help you prep research and asking AI to be the researcher: just because you’ve made a task faster doesn’t mean that the judgment behind it gets any easier.
AI can help organize 1,000 open-ended responses. That doesn’t mean we trust it to tell us which pattern actually matters. It can draft a survey in about 30 seconds. That doesn’t mean it knows whether we’re asking the right questions. It can summarize 20 interviews. That doesn’t mean it understands the contradiction buried in interview #14 that completely changes how we should think about the problem.
That’s the part that gets lost in some of the AI hype: Research isn’t valuable because someone successfully collected and summarized a bunch of information; its value comes from knowing what to do because of it.
You just can’t use AI to make the hard judgement calls for you, like:
Are we studying the right audience?
Does this methodology actually answer the business question?
Is an apparent pattern meaningful or just interesting?
What evidence would make us change our recommendation?
Are we seeing something real—or are we seeing something because we went looking for it?
And most importantly: What should the company do differently on Monday morning because of what we found?
Today, asking whether a market research firm “uses AI” is becoming a little like asking whether it uses spreadsheets.
Yes. Obviously.
The much more interesting question is whether they know when to use it, when not to use it, and when a human researcher still needs to look at the answer and say, “Yeah...I don’t buy that.”
7. Research is being judged by what happens after the findings
A 70-slide deck isn’t a business outcome.
Neither is a statistically significant result. Or an “insight.”
And “that’s really interesting” is not exactly the reaction we aim for when presenting research. Instead, we want someone in the room to say, “Okay. So this is what we need to do.”
Because ultimately, the value of market research really depends on what someone does differently as a result of it.
To be clear, this isn’t a new trend. This is what good market research has always been supposed to do. What is changing is how much more explicitly researchers are being asked to connect the dots between the findings and the business decision.
And we are very much here for that.
Because companies don’t commission market research because they want research. No CEO has ever woken up in the middle of the night and thought, “You know what we really need around here? Another 70-slide deck.”
Companies commission it because they need to make real high-impact decisions:
What should we build?
Who should we sell it to?
How should we position it?
What should we charge?
Why aren’t customers converting?
Should we enter this market?
Is this idea actually worth pursuing?
That means a researcher’s job can’t end at “Here’s what we found.” A good researcher must understand the business well enough to say: “Here’s what we found, here’s why it matters, and here’s what I think you should do about it.”
And that requires skills that have very little to do with whether you know how to run a regression or moderate a focus group.
Researchers need to understand the business context. We need to distinguish between a finding that’s interesting enough to mention and one that’s important enough to change a decision. We need to know when the evidence is strong enough to make a recommendation—and when the responsible answer is, “We don’t know yet.” We need to know how to tell the story of the data, and not just read numbers off a screen.
And honestly, sometimes we need to tell a client that the thing they were absolutely convinced was true...isn’t. And you know that’s always a fun meeting.
That’s the part of research that is hardest to automate, because it requires judgment. You have to understand the research, the customer, the business, and the decision—and then connect all four.
Getting to the finding is only the first step; knowing what to do with it is where research earns its seat at the table.
So, what defines good market research?
Taken individually, these trends may look pretty different.
More consistent, ongoing research
Mixed methodologies
Original data and proprietary customer knowledge
Better participant experiences
Greater scrutiny of data quality
AI-enabled workflows
Research that actually leads somewhere
Sure, they look different…but they’re all pointing in the same direction.
Good market research isn’t about picking a methodology and executing it perfectly. It’s about figuring out what a business needs to know, what evidence will actually answer the question, and how confident we can be in the answer.
Sometimes that evidence needs to come from a carefully designed quantitative study involving hundreds or thousands of people. Sometimes 20 really good conversations will tell you more than a survey ever could. Sometimes you need both.
Sometimes the answer is already sitting in customer behavior, previous research, or data your business already has. And sometimes the answer is: we don’t know yet and we need more information.
The skill? Knowing the difference.
And what makes this moment in market research so exciting is that we have a ridiculously good toolbox.
We can track customers over time instead of relying on a single snapshot. We can combine qualitative and quantitative research in ways that give us both the what and the why. We can collect original data that nobody else has. We can design better experiences for the people participating in our research. And AI can take some of the tedious work off our plates and help us do certain parts of the job dramatically faster.
But—and we realize this is a slightly inconvenient conclusion for anyone hoping AI would just do all the research for us—the tools still aren’t the researcher.
Better tools make it easier to gather information. They make it easier to organize it. They make some things faster, cheaper, and more scalable.
They don’t automatically tell us what’s worth knowing.
That still requires understanding the business, asking the right questions, talking to the right people, knowing when the data is trustworthy, recognizing what actually matters, and ultimately figuring out what the business should do next.
Twenty years into doing this work, that’s still the part we find most interesting.
The tools are changing. The ways we can do research are changing. But good research still starts with the right question—and ends with knowing what to do with the answer.
Need better evidence for a decision your business is facing?
At Bixa, we don’t start with a methodology. We start with the decision: What does your organization need to know to make the right call? Only then do we figure out the best way to answer it.
Talk to a Bixa researcher about your next project to get started!
