What Is AI Research for Marketing? How It Works, With an Example

Marketing research is the work of finding out who your customers are, what your competitors say and charge, which topics people care about and what has changed in your market. Done by hand, it means many searches, dozens of open tabs and notes copied into a document.

AI research for marketing hands the reading part to software. You type one question, such as how competitors price a product in a given country. The AI plans the question, runs several web searches, opens and reads the pages it finds, compares what they say and writes a report. A good research tool shows a numbered source for each claim, so you can open the page and check it yourself.

It is different from asking a chatbot a quick question. A normal chat answer comes from what the AI model already knows, sometimes with one web search. A research run is a longer job that goes back to the web in rounds until it has evidence for each part of the answer.

The most useful research jobs for marketers are the ones that need many sources at once:

• Competitor research: what rival brands offer, how they describe themselves and what their pricing pages show.
• Audience research: the questions, worries and words your customers use in reviews, forums and articles.
• Market overviews: the main players, trends and rules in a new country or category before you launch there.
• Content research: what is already written on a topic, so a blog post or video covers something new.
• Offer and messaging checks: which benefits rivals lead with, so your ad copy can say something different.
• Ad research: which ads a brand is running now. This needs an ad library search rather than general web pages.

AI can also help with the next step: turning the findings into a positioning statement, a content calendar or ad copy. Keep research and writing as separate steps so you check the facts before they reach an ad.

Most AI research tools follow the same pattern, and Infinall AI shows each stage on screen:

1. Plan: the AI breaks your question into parts and writes an outline for the report.
2. Search: it runs web searches for each part.
3. Read: it opens the pages that look relevant and reads them.
4. Verify: it cross checks key claims, flags contradictions and decides what to look for in the next round.
5. Synthesise: it groups what it found by the outline.
6. Write: it writes the report with numbered sources.

Steps 2 to 4 repeat in rounds. More rounds mean more pages read and a better checked report, but the run takes longer and costs more. That is the main trade-off when you pick a depth level in any research tool.

Say you sell a skincare serum online in India and want to know how rivals position similar products. A vague question like "tell me about skincare" gives a vague report. A specific one works better: "How do online skincare brands in India price and describe vitamin C serums for first-time buyers? Focus on brand websites and reviews from the last 12 months. List the main claims, price ranges and common customer complaints."

That question sets a topic, a region, a timeframe and the shape of the answer. The report that comes back should list the brands it found, the claims each makes, the prices quoted on their pages and the complaints that repeat across reviews, each with a source number.

Then do the human part. Open the sources behind any price or claim you plan to use, because pages change and an AI can misread a page. Note where every rival says the same thing, since that is where your message can stand out. Finally, ask for a short list of ad angles based only on the checked findings.

In Infinall Chat, click the plus button (Add files) and switch Research on. Pick a level, type your question and press Send message. The research card shows the six steps and counters for searches, pages read, useful sources and coverage, which is how much of the planned outline has evidence. The report arrives with numbered sources and checked claims.

The run keeps going on the server if you close the tab, and you get a notification when the report is ready. You can stop it at any time with Stop generating or Esc. If you want the report as a file, ask Infinall AI to put it in a document: it opens in a side panel that you can edit and export to Word or PDF.

For a quick answer rather than a full report, use Web search in chat instead. It checks the live web for one answer and cites its sources. To see the ads a competitor is running right now on Meta, Google or LinkedIn, use Ad Spy, which is free and costs no credits today.

Research uses credits, and the app does not show a price before you start. The credits taken depend on the engine used and how long the run is, and a deeper level reads more and uses more credits. Auto and Model Council cannot be used with Research, so turning it on moves the picker to a single engine.

Everyone can use Quick. Deep and Max are Pro only. The Free plan gives 1,000 credits every month with no card. Pro costs $10 for the first month, then $30 a month or $299 a year (in India Rs 999 for the first month, then Rs 2,899 a month or Rs 33,999 a year) and gives 3,000 credits a month. One credit is one US cent.

AI research is a fast first draft, not the final word. It can only read what is on the open web, so it misses paid reports, private data and anything behind a login. It cannot run a survey or talk to your customers, and nothing replaces a few real customer calls. It can also misread a page or lean on a weak source, which is why the numbered sources matter: check every number before you publish it or put it in an ad.

For your own numbers, such as ad results or post reach, use your analytics rather than web research. For live ad examples, an ad library search is more direct than a report about ads.