When and How to Outsource Market Research: A Buying Guide

A market research project can be as simple as a survey your team runs in-house, or as advanced as a bespoke market research commissioned from an external provider.

The decision to keep in-house or outsource depends on the scope of the study and the support your team needs. Some projects fit your existing research capabilities, while others require outside expertise or additional resources.

In this chapter, we look at how to:

Let’s start with the research your team may already be equipped to run in-house.

In-house research works best for repeatable studies such as message tests, concept tests, customer surveys, or moderated interviews. It’s a good fit when your team already knows how to design and run that type of study, can recruit the right participants, and has time to analyze the results properly.

In-house research is usually a good option when:

Once a study falls outside what your team can confidently run in-house, an external research partner may be a better fit.

Commissioning a study is a better fit when you need to reach buyers outside your own network, get candid feedback on sensitive topics like pricing or positioning, or coordinate research across multiple markets or audience segments.

High-stakes decisions are where outsourcing market research can be especially helpful. Bringing in expert support can help ensure that your studies lead to reliable, decision-driving outcomes. Generally speaking, you want to commission a study when the decision requires audiences, expertise, or research methods your team can’t easily support in-house.

For example:

Outside expertise can improve four key parts of the study:

You don’t need to outsource every part of the study. You might only need help recruiting participants, or you might commission an outsourced team to design, run, and analyze the entire project.

When you outsource a market research study, you can work with a traditional research agency or use an AI-native research platform.

Traditional agencies are researcher-led and typically involve hands-on support throughout the project, while AI-native platforms use automation to support the research workflow, from study execution to analysis.

What you choose depends on:

Let’s look at how those factors differ between traditional agencies and AI-native research platforms.

Traditional research agencies are a good fit for studies that need a custom research design and involvement from experienced researchers. You provide the brief, and the agency manages the methodology, recruitment, fieldwork, analysis, and final report.

For example, if you’re repositioning a brand across the US, UK, and Germany, an agency can recruit the same buyer profiles in each market, adapt the discussion guide for local audiences, run the interviews in different languages, and compare the findings across countries. Agencies are also useful for research that depends on experienced human moderation, such as interviews, focus groups, or ethnographic studies.

But this level of hands-on support comes with a few drawbacks:

If those repeated handoffs, timelines, and costs don’t fit the way your team works, you may want to look at tools that automate the research process.

If opting not to outsource, researchers can use AI to plan market research studies, draft questions, moderate interviews, analyze responses, and prepare findings.

AI research tools can also give teams more flexibility and improve operational efficiency. You can launch another study, adjust questions, or repeat the same research without starting a new agency engagement each time.

To get the most from that flexibility, teams still need researcher oversight throughout the process:

Whether you’re comparing a traditional research agency or an AI-native platform, assess both against the same core criteria. Look at how they design the research, recruit participants, maintain data security, deliver research results, and price the work.

Methodology rigor comes down to whether the provider can design the study around the question you need to answer. The method, sample, question order, moderation approach, and analysis all need to work together.

With a traditional agency, look at the agency’s track record and previous work. Ask about their experience with the research method, audience, and market you’re studying. Also check whether the agency handles the research itself or outsources parts of the fieldwork or analysis.

With an AI-native platform, look at how much control researchers have over the study design and how the AI handles questions, follow-ups, and analysis.

Maze’s AI moderator, for example, lets researchers define learning goals, choose between structured and freeform discussions, control discussion depth, and set instructions for how follow-up questions are handled. Maze also evaluates AI-moderated conversations against 25 quality metrics designed to check research quality and consistency.

For market research studies, also check whether the platform supports the question formats and stimuli your methodology requires. Maze’s AI for market research capabilities include visual stimuli for testing images and video. For preference studies, you can use ranking to compare a smaller set of options, or MaxDiff to identify which claims, benefits, or messages matter most across a larger list.

There are several ways to recruit research participants. You can invite your own customers or prospects, recruit through a research panel, or use a provider that sources participants on your behalf. What matters is that you know where respondents come from and whether they match the audience you intend to study.

With a traditional agency, ask how participants are recruited and screened. Find out whether the agency uses its own network, external panels, specialist recruiters, or a combination. For niche B2B audiences, check how it verifies criteria such as job role, seniority, organization size, and buying experience.

With an AI-native platform, look at the targeting and screening controls available to your team. Maze’s panel, for example, uses third-party recruitment providers and lets teams target B2B and B2C participants across 150+ countries. For unmoderated studies, teams can use 400+ targeting filters, including job title, seniority, industry, company size, demographics, technology use, and other criteria. Custom screener questions can then qualify participants against requirements that standard filters don’t cover.

You can also review individual participant responses and report participants whose answers are rushed, copied, generated by bots, or inconsistent with the targeting criteria.

Some AI-native research providers also offer synthetic users. They are AI-generated representations of users rather than people who have taken part in the study. They can help teams explore early hypotheses or test a research setup, but their responses shouldn’t be treated as direct feedback from real buyers.

Market research may involve interview recordings, transcripts, survey responses, and participant information. So before you share that data with a provider, look at:

With a traditional agency, review its data processing agreement, cybersecurity practices, subcontractors, access controls, and retention policies. If the agency uses external transcription, analysis, or AI tools, ask which services receive your research data.

With an AI-native platform, also check whether your data is used to train AI models, which third-party models process it, and how long those providers retain it.

Maze, for example, doesn’t use customer data to train AI models. Its AI features use third-party providers including OpenAI, Anthropic through Amazon Bedrock, Google Gemini, and Deepgram. Enterprise teams can also disable all AI features or control access to individual AI capabilities from the admin settings.

Maze is also SOC 2 Type II certified and GDPR compliant, with its subprocessors and data-processing terms documented separately. You can visit the Maze Trust Center to review its security controls, compliance documentation, subprocessors, and data-processing practices.

Turnaround time tells you how long it will take to go from a ready-to-launch study to findings your team can use. This is important when you need to make time-sensitive decisions, such as a product launch, pricing change, campaign, or repositioning project.

With a traditional agency, ask how long each stage will take. Recruitment availability, moderator schedules, analysis, and reporting can extend the timeline, especially for multi-market or highly customized studies.

With an AI-native platform, check which stages of the research run automatically and which still depend on participant recruitment or researcher review.

Maze’s AI study builder is an AI-powered research assistant that turns a research goal into a structured study. It recommends the methodology, drafts questions, adds relevant study blocks, and configures key settings. Researchers can then review and refine the study before launch.

For market researchers using Maze this can reduce the time spent setting up a study. Maze also generates UX reports as responses come in and updates them as new data is collected. For unmoderated studies, AI-powered thematic summaries and open-response analysis become available once the study collects at least 10 open-text responses.

Pricing should show what it will cost to run the research you have planned, including any costs beyond the initial quote or subscription.

Check how the provider charges for:

With a traditional agency, check what the project fee includes. Recruitment, participant incentives, moderation, translation, analysis, reporting, project management, and additional markets may be charged separately.

With an AI-native platform, look at subscription limits and usage-based charges. Check the cost of seats, studies, responses, interview hours, AI credits, participant recruitment, incentives, onboarding, and additional usage.

Use your expected research volume to compare providers. If you plan to run 20 interviews every month, for example, work out what those interviews will cost once AI usage, recruitment, and participant incentives are included.

A bespoke market research study is designed around a specific business question, audience, and decision. It may focus on one research area or combine several methods. Common examples include win-loss research, category and buyer perception studies, and pricing research.

A win-loss study evaluates buyer decisions to show what repeatedly influences deal outcomes. Findings can reveal whether product fit, pricing, positioning, competitor differences, or the sales process shaped the result. The study can also include anonymized buyer quotes and comparisons across won, lost, and no-decision deals.

A bespoke perception study can answer two related questions. Category perception research looks at how buyers define the market, which brands they group together, and what they expect from companies in that category. Brand perception research looks at how those same buyers see your brand, including the qualities they associate with it and how they compare it with competitors.

Pricing research helps you understand what buyers are willing to pay and how demand changes at different price points. A bespoke study might use Gabor-Granger to test purchase intent at specific prices, Van Westendorp to identify an acceptable price range, or conjoint analysis to measure which product features buyers value enough to pay more for.

A market research brief gives the research team the context they need to design the study. Start with the business problem, what you need to learn, and the constraints the study needs to work within.

It should include:

You don’t need to describe every research decision in the brief. Once the scope is clear, you can decide which parts need outside support and which your team can run in-house.

Not every market research project needs to be outsourced. If your team has the research skills and capacity to run the study, Maze gives you the tools to manage more of the work in-house.

Start with the Maze AI study builder to turn your research goal into a structured study with questions, task blocks, and follow-ups. Maze also supports AI for market research capabilities, including visual stimuli for testing images and video, ranking for comparing preferences, and MaxDiff for identifying which claims, benefits, or messages participants value most.

You can invite participants through in-product prompts or recruit them through the Maze panel, then run surveys, interview studies, and AI-moderated interviews in one platform.

Maze AI can help summarize responses, identify themes, and turn the findings into reports your team can review and share—giving you one workflow for planning, running, and analyzing market research in-house.