What is market research? Types, methods, and examples

Marketing managers, brand leads, product marketers, and founders are all running studies to answer the same question: “Is this worth pushing into the market?”

Done well, market research gives you a clear view of who’s out there, what they care about, and what opportunities are on the table. It helps you avoid launching the wrong product, entering the wrong market, or pouring budget into a campaign that won’t land. It also shapes your positioning and pricing and helps you prioritize the ideas that are most likely to work.

In this guide, we walk through the core types of market research, the methods you can use, where it differs from user and product research, and examples to inspire your next study.

Market research is the structured process of understanding your market, customers, and competitors to make better business decisions. It helps you identify who your customers are, how many of them there are, what problems they are trying to solve, and how much demand exists. It also helps understand how your brand and product compare to alternatives through systematic data collection and market analysis.

While user research focuses on how people use your product, market research zooms out to look at demand, consumer behavior, brand perception, and competitive positioning. The value is in how they connect. Market research helps you decide which opportunities, audiences, and value propositions to focus on, and user research gets into the details of how those decisions play out in the product experience.

For Product and Marketing teams, market research answers questions like:

These insights shape your positioning, pricing, and where you invest your energy and budget.

Market research, user research, customer research, and product research are used interchangeably, but they solve different problems:

Here's a quick breakdown:

A researcher or insights lead often sits across this whole picture, collecting data from each research type, framing the combined evidence as risks, options, and trade-offs, and making it easier for PMs, designers, and marketers to act on the same set of facts.

Market research targets broad market segments, while user research focuses on the profiles that could or do use your product. They answer very different questions. If you start with the wrong one, you either over-engineer a bad idea or under-invest in a great one.

Choose market research when:

Market research behaves like a decision engine. It compares options side by side and uses quantitative methods to answer: “Which one wins in this market, for this audience, under these conditions?”

Choose user research when:

User research behaves like an experience microscope. It zooms into the fine detail of behavior and usability, not the macro question of whether the concept should exist in the first place.

You need both.

When this flow works well, insights move in both directions. Concept tests and demand studies inform design briefs and roadmap priorities. In turn, usability studies and in-product behavior give Marketing and Sales concrete input for campaign copy, sales enablement, and positioning, so the story you tell outside the product reflects what actually happens inside it.

Most teams work with four core types of market research:

In practice, you rarely use just one. A typical study might combine secondary market research to understand the global market landscape, primary research to hear from potential customers, qualitative methods to explore attitudes, and quantitative methods to validate findings at scale.

Let’s look at each of these market research types and where they fit in your research process.

Primary research is any market research where you collect new data directly from your target audience instead of relying on existing reports. You design the study, recruit participants, and control the questions, timing, and context.

Marketing teams use it for:

The method you choose—quantitative or qualitative—depends on your research question. Testing concepts often uses quantitative comparison, while understanding motivations requires qualitative depth.

Secondary research means using data that already exists rather than collecting it yourself. You're digging into data from published reports, industry analysis, existing studies, and market intelligence to frame your strategy before committing resources to primary research.

Marketing professionals rely heavily on this approach. They analyze in-house consumer data and also use syndicated data sources—paid reports from firms such as Statista, Mintel, and Nielsen that provide detailed charts, competitor benchmarks, and market forecasts.

Beyond paid syndicated data, teams also draw on public-domain sources and academic and industry reports to build market context. The goal is to gain a broader perspective, frame categories, understand where the market is heading, and identify gaps competitors aren't addressing.

Quantitative research tells the macro story. It gives you numbers, statistical confidence, and the ability to say "X% of our target audience prefers this option". It's about scale, repeatability, and proving decisions with data that leadership can defend.

In a market research context, quantitative studies usually look like:

Sample sizes for quantitative research are usually large. Teams often aim for 100–5,000 respondents, depending on budget, how many segments they care about, and how precise they need the numbers to be for a market research report.

Qualitative research focuses on understanding the ‘why’. It captures emotions, perceptions, and motivations through conversations and narrative feedback.

In market research, qualitative work typically uses:

Sample sizes are typically smaller than in quantitative research—around 5–15 participants per segment—because qualitative research prioritizes identifying patterns in how people think and feel, not measuring how many people think that way.

Running market research doesn't have to be overwhelming. With an AI-first user research platform like Maze, you can move from question to actionable insights in days. Here's a quick framework you can follow, whether you're testing a campaign concept, validating pricing, or measuring brand perception.

Start with the question you're trying to answer. Are you testing brand perception? Exploring demand for a new product concept? Understanding why customers choose competitors?

Good objectives are specific and measurable. For example:

You also need to decide what success looks like. Write down three to five KPIs you'll track after launch so you know if your decisions paid off. These might include metrics like brand recall lift, market share growth in target segments, conversion rate on new messaging, or customer acquisition cost in priority markets.

In practice, writing your learning goals directly into your research platform ensures they stay front and center throughout the study. Maze's AI moderator uses these goals to guide interviews and tailor follow-up questions automatically. Your objectives also shape how the AI analyzes responses and generates thematic insights.

You need to get specific about demographics (age, location, income), behaviors (purchase frequency, product usage), and psychographics (values, lifestyle, attitudes). For example, don't say ‘parents’, say ‘parents of children under five who buy organic food and live in suburban areas.’ The more precise you are, the better your insights will match real market behavior.

Create a screener to qualify participants with questions like:

Screener questions help ensure you’re talking to the right profiles within the market. In Maze, when you set up your study, you can add these screener questions in the recruitment settings.

Your research question helps determine your method.

Need to understand why people feel a certain way? Go qualitative. Need to prove ‘how many’ prefer option A over B? Go quantitative.

You need to match your method to your objective:

Often you'll use both—run qualitative first to explore, then quantitative to validate at scale.

Maze supports AI-moderated interviews for scalable conversations that probe deeper based on responses and unmoderated tests where participants complete tasks at their own pace. You can also run surveys with multiple-choice and rating scales, and diary studies where participants log feedback over days or weeks.

Now you’re ready to launch your market research study and collect responses. Decide on your sample size first. You’ll need around five to 15 participants for qualitative research and 100 to 5,000 for quantitative, depending on how many segments you're testing and the statistical confidence you need.

Maze Panel gives you access to millions of B2B and B2C participants across 150+ countries with 400+ filters like demographics, profession, industry, technology use, shopping behavior, and lifestyle habits. Select your criteria, set your sample size, and Maze recruits, screens, and delivers qualified participants automatically.

For your own audience, use Maze Reach to send studies via email to customers, prospects, or internal teams. You can upload your list, segment by criteria, and send personalized invitations with tracking and reminders built in.

Once you’ve recruited participants, the quality of your data depends on your study. For quantitative studies, check that responses are distributed across your target segments so you're not accidentally over-indexing on one group. Monitor response rates as they come in and watch for drop-off points that might signal confusing questions or survey fatigue. For qualitative interviews, keep sessions focused on your original objectives while staying flexible enough to explore unexpected themes that emerge.

This is where you look for themes in qualitative feedback, statistical significance in quantitative data, and segment-specific differences that reveal how different audiences respond.

Maze automatically generates reports as soon as participants start completing your study, like:

You can customize reports with custom slides, remove sections, reorder content, and hide or show specific responses. This makes it easy to add context from secondary research—like industry benchmarks, competitor analysis, or market sizing data—directly alongside your primary research findings. Your team can comment directly on reports to discuss findings and align on next steps.

Plus, you can share reports as live links (no Maze account needed), download PDFs for presentations, or embed directly into Notion, Confluence, Slack, or your roadmap tool.

At this stage, you should be able to answer questions like:

Quote the language participants used in your research to humanize your recommendations and make them stick with stakeholders. For example:

After launch, measure what happened. Did brand perception change? Did purchase intent translate to conversions? Did the campaign perform better than your baseline? Track your KPIs, compare them to your benchmarks, and figure out what worked and what didn't.

Here are a few KPIs worth tracking:

Market research is a cycle. Launch, measure, learn, and iterate. You should keep testing new hypotheses based on what you discovered and keep building on what you learn.

Each study gets you closer to understanding what really drives decisions in your industry—and that's how you stay ahead.

Maze fits into a market research strategy as the platform for finding and screening participants, conducting quantitative and qualitative primary research, and analyzing data collected during market research for both Marketing and Product teams. It’s strongest when you need rich, fast feedback on campaigns, concepts, and brand messaging, not when you’re running large, complex quantitative market studies or long-running tracking programs.

Because Maze also supports a wide variety of research methods—like prototype and usability testing—it can sit across both market research and user research, so the same tool that validates a concept can later be used to test how that concept performs in the product.

Maze aligns closely with how Marketing and Product teams already do market research, especially when they need to:

Under the hood, Maze supports these use cases with AI-powered qualitative research methods like AI-moderated or unmoderated interviews, open-ended questions with AI follow-ups, rating scales, and thematic analysis that turns raw feedback into patterns your team can act on. The same workflows and question types are used for UX studies in Maze, which makes it easier to link market-level insight to what you later test in prototypes or live flows.

Use Maze to explore perceptions, test positioning strategies, and uncover unmet needs through interview studies and surveys—then layer those insights with market sizing data, competitive benchmarks, or statistical analysis from other sources.

From there, Product and UX teams can push the best concepts straight into prototype tests and usability studies in Maze. And what you learn from those tests about language, friction, and mental models can flow back into campaign creative, brand messaging, and sales enablement.

Instead of running market research and user research as separate projects, Maze helps you build a single system of learning that supports decisions across the organization.

Let’s say you’re a SaaS company launching a new project management tool for small businesses. You’re keen to understand the market you’re about to enter, specifically the competitors that already exist and what customers think of them. You want to understand what’s currently out there so you can improve on it—winning over customers and becoming the go-to.

You've identified three potential positioning strategies, but you're not sure which will resonate most with your target market of small business owners.

Research objective: Determine what problems your target customers currently have with their existing project management solution.

The approach:

You recruit 200 small business owners (50-250 employees) through Maze Panel, screening for those who currently use or are considering project management software. You split them into three groups: one for teams using Competitor A, one for Competitor B, and a third for Competitor C.

For the quantitative layer, you ask each group to rate satisfaction with their existing solution, perceived value, price elasticity, and willingness to switch. Maze automatically generates comparison charts showing which concept scores highest across segments.

For the qualitative layer, you include open-ended questions: "What is your favorite thing about [competitor product]?" and "What do you wish it did better?" Maze's AI groups responses into themes, revealing that all customers wish their PM solution offered more finance features.

You also conduct 10 follow-up AI-moderated interviews with respondents who showed high switching intent. The AI probes deeper into decision criteria, current pain points, and what exactly would make them switch from their existing tool.

The outcome:

Within five days, you have a clear list of opportunities for developing a more advanced project management solution for small businesses. You know what customers like and dislike, what they’re willing to pay, and under what conditions they’d make a switch.

You combine these findings with secondary research on market size and competitor positioning, then present a unified report to leadership with clear direction on which strategy to pursue.

Market research and user research are still different disciplines, but they’re no longer owned by completely different teams. The traditional boundary between research and analytics is dissolving, with research professionals shifting toward strategic consulting rather than purely executing studies.

Research leadership at Fortune 500 companies now manages both Marketing and UX researchers under one function. Teams want one platform that handles concept validation, messaging tests, brand perception studies, and prototype testing.

This shift changes how you think about your research practice.

Market research and user research work best as one loop, and Maze fits into that merged model as a shared environment for modern insight work. It gives marketers, Product teams, and researchers one place to conduct research with real audiences, so market- and user-level questions feed the same decisions instead of competing for attention.