MoMoDi vs traditional market research: when you still need a research firm

If a conclusion only needs to come from public information, use AI research; if it must come from real people’s reactions, hire a research firm. The line is clear: AI does desk research fast and broadly, but for first-hand data it has no source, cannot do the job, and should not pretend to.

For most projects the right answer is not either/or. Finish the desk research with AI first, then spend the budget you saved on the few questions that genuinely need first-hand data.

What each one is

MoMoDi is Moments’ AI research product. Its decision partner, Momo, breaks a vague “look into this market for me” into sub-questions that can each be answered, researches them one by one — searching, comparing and recording sources — and returns a structured conclusion with the source behind every claim. The same path pointed at a different subject — “how do AI platforms answer questions about my brand?” — produces a GEO diagnosis; the free GEO audit starts from the same idea.

Traditional market research is a mature, decades-old discipline: desk research, surveys, in-depth interviews, focus groups, usability testing, mystery shopping, sampling and statistical inference. Its core assets are access to real respondents and a method that makes samples representative. Pricing depends on the project and sample size and varies widely.

Side by side

Dimension	MoMoDi (AI research)	Traditional market research
Data source	Publicly searchable information	First-hand respondents plus public information
Questions it answers	What exists in the market, what competitors say, the public record	What users really think, whether they will buy, what they will pay
Time to start	Minutes	Usually weeks (recruiting, fieldwork, analysis)
Breadth	Wide — dozens of sub-questions in parallel at low cost	Limited by sample size and budget
Traceability	Every conclusion carries a source	Original questionnaires and interview records
Sample representativeness	No statistical sample concept	Core of the method; supports inference
Main risks	Public information can be biased; model hallucination	Sample bias, leading questions, respondents not telling the truth
Decision size it suits	Day-to-day decisions, direction-setting, early scans	Large commitments that must be justified to a board or regulator
Repeatable	Yes — re-run the same questions any time	Repeating a round means another project

When to use AI research

• Early scans, when you do not yet know what to ask. Entering an unfamiliar category, evaluating a new market, sizing up competitors — the answers are scattered across public sources.
• Research you repeat regularly. Competitor moves, industry policy, category price bands. A traditional project is re-quoted each round; re-running AI research costs close to nothing.
• When the subject is AI itself. How AI search platforms describe your brand, whether they recommend you, whether they get facts wrong — that can only be tested on AI, and no questionnaire replaces a measured mention rate.
• When the budget cannot start a traditional project. Settle what public information can settle, and narrow the open questions.

When you need a research firm

• The conclusion must come from real users — whether a price can rise, whether new packaging will cost sales.
• You need statistical representativeness. Any claim phrased “X% of users” needs a sampling method.
• The decision is large enough to be audited — investment, M&A, major media spend, listing documents.
• It involves unpublished behavior — how shoppers move in a store, where eyes land on a screen.
• Regulation prescribes the method.

When neither fits

• The question is not defined. Write the decision as one sentence that can be judged right or wrong.
• The answer is already decided and you only want support for it — the most expensive mistake.
• The data is too sensitive to send to a third-party AI or an outside firm. Settle the data boundary first.
• You are predicting something without precedent. A small real-world test beats any research.

Which should you choose?

1. Write the decision point as one sentence. “Should we enter this category in Q1 next year?” leads to a different design from “research this category”.
2. Run the part public information can answer through AI first.
3. Narrow the rest to three to five questions that must be asked of people, then get research-firm quotes. A narrower scope usually lowers the cost noticeably.
4. Review before the conclusion enters a decision. Tracing each sourced claim is the cheapest quality check.

If your goal is to learn how AI platforms describe your brand, start with the free GEO audit, then decide on investment using GEO agency or do it yourself.