Marketing research comes down to four recurring questions: which topics and segments to go after, how buyers talk about the problem you solve, how you compare with competitors, and which sources shape opinion in your category. The inputs behind those answers have barely changed over the past decade. Keyword tools count searches that already exist. Surveys only answer what you thought to ask. Win-loss interviews and analyst reports age the day they're written. And your own funnel data describes the buyers who already found you.
AI Search has added a new source. Before buyers type a brand name into Google or fill out a form, many open ChatGPT or Perplexity and ask, in full, messy sentences, the way they'd ask a knowledgeable friend.
"How do startups usually handle expenses before they hire a finance person?"
"What do people mean when they say a tool is hard to implement?"
These are the kind of long, specific, unfiltered questions people ask while they're still figuring out what they even want. And the engine answers by pulling from specific sources, so you can see not just what they asked but who the model trusted to answer.
For a marketer, that's both a gift and a headache. People tend to be more candid with an AI than they are with a form, and they ask earlier, so you get an unusually honest look at what your market is chewing on before it hardens into a shortlist. It's also a firehose: millions of these prompts, phrased a hundred different ways, scattered across engines, with no volume column and no tidy keyword to group them by. The signal is rich. Making sense of enough of it to decide something is the hard part.
Two kinds of signal in the data
There are really two things to read here, and they answer different questions:
- The prompts tell you what your market wants: the questions themselves, at volume, before anyone has narrowed down.
- The citations tell you how your market is described and who's shaping that description: when an engine explains your category or names the players in it, the sources it leans on are the ones setting the terms, and you can see which they are.
One is demand, the other is perception, and many "AI for research" tools out there give you neither, because pointing a chatbot at a survey or a competitor's homepage is just the old inputs sped up. Profound reads 1.9+ billion real prompts and the citations that answer them, and it ships Agents built to run specific research jobs against that data. Each one gathers, compares, and hands back something you can act on.
The rest of this guide walks through four of the questions marketers ask research most often, and the Profound agents that answer each.
"What should we go after?"
Deciding where to spend effort usually means reading your own traffic and branded volume, which describe the demand you've already captured and stay quiet about the demand forming upstream among buyers who haven't found you yet. You’re essentially judging a room's temperature while standing next to the radiator.
Live buyer questions give you the whole room. Point the AEO + SEO Research Report at a topic, and you see the questions being asked around it, where interest is rising, and where you're absent while a competitor gets cited.
A topic is rarely one query, though. It fans out into a cloud of sub-questions, and the Query Fanout Estimator agent predicts how a model breaks a single prompt into the many searches it runs underneath, so you're sizing the whole cloud rather than the one head term you already track. Put real numbers on it with the Pull SEO + AEO Keyword Volume agent, which reports search demand and answer-engine demand side by side, and you can weigh a bet on where the market is heading instead of where it's been. A topic with climbing prompt volume and no owned page in the answers is an opening you'd never see in a keyword tool, because the tool only counts the searches that already exist.
"How does our market think and talk?"
Who your market is and how it frames the problem usually comes down to inference. You look at who converted and reason backward. That teaches you about the people who already fit. It teaches you little about the ones who describe the problem in words you don't use, because they never matched your pages and never entered your funnel to be counted.
Prompt and community data show you the language directly. You see how different buyers phrase the same need, which words cluster around your category, and which questions come up early in a decision versus late. That gap between your words and theirs is where demand leaks out: you call it "spend management," a buyer asks how to "stop the team expensing nonsense," and your page never meets the question.
Two Profound agents work this seam. The Reddit Insights Generator reads the top-cited discussions on a topic and reports how the category is talked about, where no vendor is steering the conversation, which is usually where the honest phrasing lives, complaints and all.
The Reddit Post Sentiment Analyzer takes a set of threads and your brand name and classifies the sentiment across them, with the themes driving it, so "people are unhappy" becomes a specific, sourced list of what they're unhappy about. Messaging built from that starts with the words your market already uses, not the ones you wish it used.
"How do we stack up against the competition?"
Competitive positioning is often pieced together from point-in-time exercises, whether it’s a win-loss round, an analyst call, or a teardown that's accurate the day it's written. Each one is a snapshot, and it starts aging the moment it's done, while your positioning keeps moving. You might find out you've drifted late, often when a deal you should have won goes the other way.
Profound reads the same competitive picture from live prompt and citation data, so the comparison refreshes automatically instead of waiting for the next round. You see the words the engines attach to you and the words they attach to each competitor, and the distance between how the market describes you and how you'd describe yourself, which is the distance your answer engine optimization exists to shorten.
Two failure modes turn up here, and they need different fixes. Sometimes an engine has a plain fact wrong, which is findable and correctable at the source. Sometimes every fact is right, but the synthesized picture misses your real positioning, because the web never made a consistent case for it, and that one is slower and harder to move.
Reading them apart is the builk the work, and three Profound agents do it:
- The Industry Analyst Report Simulation agent cross-references what you say against public G2 and Gartner Peer Insights data and sweeps every competitor in your category for a market-level read.
- The Customer Question Miner agent mines the real questions customers ask about a given topic. It pulls recent prompt responses tagged to the topic plus related query-fanout data, then synthesizes a report covering top questions, themes, intent breakdown, underserved questions, and recommended content angles.
- The Citation-to-Content Engine agent reads citation behavior around a topic to find the specific openings where a competitor has the answer and you don't.
"Where does influence come from here?"
Authority is measured by reach—the outlets with the biggest audiences, the loudest names. Reach and influence existed in lockstep for a long time, but in AI answers they've come apart. The source an engine leans on isn't always the one with the largest readership, and the list of who earns citations for your category doesn’t always match the one you'd build from a media database.
The Citation-Friendly PR Target Finder agent pulls the domains AI genuinely cites for your topics, checks each one's robots.txt to see whether it even allows the crawlers, and ranks them into a pitch list with the cite-friendly, high-citation outlets at the top.
Often the top of that list is a niche publication, a specific subreddit, or a review site that carries more weight in the answer than a national outlet does. That map informs decisions across PR, partnerships, and content, telling each where a placement or mention will actually move what the engines say about you. It's research, not outreach: it tells you where to aim, and the earned-media work in how to get cited in AI search takes it from there.
From research to action with Profound's AI Marketer
Every agent in this guide waits for someone to ask it a question. But what if you didn't have to ask? What if something noticed the Reddit threads turning sour, or the outlet the engines started citing, and came to you first?
That's AI Marketer (Aim), an orchestrator that keeps the research running on its own. It tracks prompt volumes, citations, sentiment, and competitor movement, and every week it brings your team a ranked list of what it found, such as:
- A topic where buyer questions are climbing but you have no content on
- A publication the engines cite frequently but has never covered you
- A complaint that keeps coming up in Reddit threads about your product
Most importantly, Aim doesn't stop at the finding. If you approve, it turns the insight into a project and builds the Agents that execute it, whether that means a brief and a draft for the missing page or a pitch list for the outlet. Nothing ships without your team's sign-off, and Aim learns from what you approve, edit, or decline, so each week's list fits your strategy and priorities better.
The four questions this guide started with (what to go after, how buyers talk, how you compare, and who influences the answer) don’t have to wait for the next planning cycle. They arrive proactively, with a plan to act on them ready for your approval, and execution one click away.
Learn more about what Profound can run across your marketing.
AI marketing agents for research FAQs
Is AI-powered marketing research the same as using ChatGPT to summarize a survey or scan a competitor's site?
No, and the difference is the source. Using a chatbot to speed up work on your existing inputs, a survey or a competitor's homepage, keeps you on the same lagging, sample-based data. Research on prompt and citation data draws on a different source entirely: the questions real buyers ask AI, at volume, and the citations attached to the answers. That's a live read on demand and perception that a survey or a site scan can't give you, however fast you summarize them.
What can prompt data tell me that keyword tools and surveys can't?
It catches intent while it's still forming, in the buyer's own words, before it narrows into a branded search or a category term. Keyword tools count demand that already exists and skew toward people who know what to look for; surveys answer only what you thought to ask, weeks later. Prompt data shows what your market is working out right now, including the questions from people who haven't found you yet, which is the demand a keyword tool structurally can't see.
How is the prompt data collected, and can I trust it for real decisions?
It comes from the questions real users ask answer engines, captured from the consumer experience at scale, rather than from an API estimate or a keyword tool's model. That's what makes it usable for research: you're reading what your market actually asks, in its own words, not a proxy for it. Profound analyzes 1.9+ billion of these real prompts and the citations that answer them, which is what makes the read stable enough to plan against instead of a pile of anecdotes.
Won't this overlap with the research my SEO or product marketing team already does?
It shares subject matter, but it sits above any one role, as the input layer they all draw from. The SEO loop treats topic research as one stage of getting pages cited; product marketing runs competitive and positioning research as part of its own cycle. This is research as a discipline in its own right, cross-functional, answering the four questions any marketer brings to a decision, then handing off to the teams that act on the answer.
Can an agent actually make the decision, or does it just gather the inputs?
It gathers and synthesizes; you decide. The agents pull the data, compare it, and hand back a prioritized, sourced answer you can defend in a planning meeting, but the model of this whole series is that a person makes the call and approves the work. The value is that the decision starts from evidence with the numbers attached, rather than from whatever happened to be easiest to measure.
How does research fit with the rest of the marketing work?
It's the front of the loop. Research tells you what to go after, who you're talking to, how you stack up, and where influence sits; the content, outreach, and campaign work turns those answers into action. When the research is sharper, the content briefs, the targeting, and the pitches built on it are sharper too, which is why it's worth running on the best data available rather than the most convenient.