Using AI for Market Research and Customer Insights

Ask an AI assistant how big the market for mobile dog grooming is in your region and it will give you a confident number, a growth rate, and three trends. Some of it may be right. You have no way of telling which part. Ask the same assistant to read two hundred of your own customer enquiries and tell you what people were worried about when they wrote, and it will do in ten minutes what would have taken you a weekend. The second job is the one worth doing.

Market research for a small business usually means answering a few plain questions. Who buys from us and why? What do they compare us to? What would make them pay more, or leave? What is changing among our competitors? AI helps with some of these far more than others.

Good at: reading a lot of text and sorting it. Give a model a pile of reviews, emails or call notes and it will group them by theme, pull out representative quotes, and spot the phrases that keep recurring. This is tedious work for a person and the model is fast and consistent at it.

Good at: structure. Drafting interview questions, building a comparison grid, suggesting the categories a survey should cover, turning messy notes into a readable summary.

Unreliable at: facts about the world. Market sizes, competitor prices, statistics, anything that changed recently. A model without live search is working from what it read in training, which may be old or wrong, and it will not tell you when it is guessing. With search it is better, but only if you open the sources it cites.

Useless at: knowing your customers. A model has never met them. Everything it says about “customers like yours” is an average of what it has read. Your customers are not average, which is presumably why they chose you.

Most small businesses are sitting on more customer research than they realise. It is just scattered, unread, and written in full sentences, which used to make it hard to analyse. That last part is exactly what AI changes.

The useful sources, roughly in order of value:

Enquiries and intake forms. What people wrote when they first got in touch, before you had a chance to frame anything. This is your customers describing the problem in their own words.

Sales call notes or transcripts. The questions people asked, the objections they raised, the competitors they mentioned. If you record calls, transcripts are the richest source you have.

Lost deals and cancellations. Why people said no or left. Usually the least-read and most useful pile.

Support emails and tickets. Where the product or service falls short of expectations.

Reviews, yours and your competitors'. Public, free, and written by people who felt strongly enough to write. Competitor reviews in particular show you what their customers wish was different.

A repeatable method that works with any capable AI assistant and a spreadsheet.

The output that usually proves most useful is a list of the exact phrases customers use for their problem. Those phrases belong in your website copy, your ads and your answers to common questions, which is also how you get found when buyers ask an AI assistant. That side of it is in how to rank in ChatGPT.

For research about the outside world, the rule is: you gather the material, AI reads it. That keeps the facts grounded in something you can see.

Pricing and packaging. Save each competitor's pricing page, then ask AI to put them into one comparison table: pricing model, what each tier includes, what costs extra. Check the table against the pages before relying on it.

Reviews. Run the same theme exercise from the previous section on competitors' reviews. The recurring complaints are an outline of the positioning available to you.

Job posts and changelogs. What a competitor is hiring for says a lot about where it is heading. A run of sales hires means a push upmarket; a run of support hires may mean a problem.

What AI assistants say about your category. Ask ChatGPT, Perplexity, Claude or Google's AI the questions your buyers would ask, such as the best option for a business like theirs, and note who gets recommended and why. Buyers increasingly start there. Doing it once by hand is a useful exercise; doing it every week needs a tracker, which is what PostWyse's AI visibility tracker and competitor watch do inside Wysera. The background is in what answer engine optimisation is.

Reading what customers wrote tells you what they said when they had a reason to write. It does not tell you what they were thinking when they chose a competitor, or what they would have paid. For that you have to ask.

A handful of short conversations with recent customers and recent lost prospects is still the most valuable research a small business can do. AI makes each part of it cheaper:

Before: draft open questions that do not lead the answer, such as what were you doing before you found us, rather than would you like feature X.

During: transcription, so you can listen instead of writing.

After: the same theme analysis across all the transcripts at once, with quotes you can check.

Different research tasks need different amounts of checking. A rough guide:

The pattern: the closer the task is to reading text you supplied, the more you can trust it. The closer it is to recalling facts about the world, the more you should treat the answer as a suggestion of where to look. The same habit of spot-checking applies to any AI output, as covered in auditing AI agent output.

AI-assisted research is good enough for most of the decisions a small business makes every month: what to write on a page, which objection to address, which service to promote. It is not good enough for everything.

The decision is big and hard to reverse. Opening in a new city, a large price change, a new product line. If being wrong costs a year, spend money on research designed for the question.

Someone else will scrutinise the numbers. Investors, a bank, a board. A market-size figure from an AI chat is not evidence. A figure from a named, published source might be.

You need to measure, not just understand. How many people would buy at a given price, what share of a market prefers one option. That needs a properly designed survey with a real sample, which AI can help you write but cannot replace.

You have no customer data yet. If you are pre-launch, there is nothing to analyse. Talk to twenty potential buyers first. Then use AI on the transcripts. For deciding who to talk to in the first place, the go-to-market strategy framework starts with picking one customer.