How to research a launch market with ChatGPT before you pay an agency
You can score five candidate countries yourself in an afternoon. What you need is a set of criteria you chose, one written brief, and a table you can defend in front of a partner. Parts of the output will be wrong, and finding those parts is the actual work. Do that first pass yourself, then decide what an agency would still add.
Write the criteria before you open the chat
The criteria decide the answer, so you write them and nobody else does. A founder scoring the next launch markets can get there with four: acceptability of the category in that country, the price point the market supports, customer acquisition cost, and regulatory friction. Nutraverde's launch-market scoring in my prompt library runs on exactly those four.
Write each one as a question with a unit attached. Acceptability means the category is already sold in that country, through a channel you can name. Regulatory friction is whether your product needs a license under food law or a full registration, and how many months that takes. Price point is the shelf band the market already pays for the nearest comparable product.
You know it is done when a partner can read the four lines and predict which country will win. If they cannot, one criterion is doing nothing and one is missing. Check that none of the four is a proxy for another. Acquisition cost and acceptability drift into each other fast, and then two columns say the same thing twice.
What can ChatGPT actually do for a launch market scan?
It reads public sources and returns a scored table with a reason and a link in every row. That is the whole of it, and for a first pass it is enough. Trade data, importer listings, regulator pages, retailer assortments and category reports are all public, and the work of pulling them into one shape is the work you were going to pay for.
What it does not hold is your category knowledge. It has no view on whether your formulation survives a hot supply chain or whether your founder network opens a door in one market and not the next. That stays with you, which is why the criteria came first.
Name the mode you want. Deep research, available on a ChatGPT Plus plan with a monthly allowance of runs, goes wider and cites as it goes, and you can restrict it to sites you trust. A plain chat with web search on is fine for a quick read of three countries. Verify the mode and its limits on the day you run it, because both move.
Score the countries in one table and read the spread
Ask for one table, countries as rows, your four criteria as columns, each cell carrying a score out of five, a one-line reason, and a source link. Then ask for a second table listing every assumption the scoring rests on. The second table is the one you read first.
A scan like this is done when the scores spread. If four countries all come back at 3 and 4 out of 5 on everything, the prompt was too vague and the scoring collapsed to an average of public optimism. Force the spread by asking which country is worst on each criterion and why, one at a time. Weak markets are easier to describe than strong ones, and the reasons are more specific.
Keep the sheet. The next prompt in my library turns a verified set of findings into a launch brief, a competitor matrix and a retailer note for Mamaearth in Poland. That only works if the scan is in a table you can reuse rather than a wall of prose you have to re-read.
How do I know the market scan is wrong?
Pick three claims and check them against the primary source yourself. Take the regulatory line for your top-ranked country and open the regulator's own page. Take the price band and check it against a live retailer listing in that country. Take the acquisition cost and compare it with what you already pay in your home market. Expect at least one of the three to be off.
Then look for the claim with no link at all. An unsourced cell is the one most likely to hold a reasonable-sounding average that nobody ever published. Mark it and either source it or drop the row's score until you do.
The last check is the one only you can run. Override any score your own experience contradicts, and write one line next to it saying why. Regulatory friction is where this bites first. A country may score well because the license route on the regulator's page looks short. If you know exporters in your category go through a different route, that score is wrong and you change it. The judgment to evaluate the output rather than accept it is the muscle an afternoon like this builds.
Can I skip the agency on the first pass?
Do I still need an agency for the second pass?
Sometimes, and now you can brief one properly. After a first pass you know which two countries are live and which three questions remain open. You are buying fieldwork and local contacts against a named question, not a general market report you could have assembled yourself.
What if no public data exists for my category there?
Score it as unknown rather than average, and note what would resolve it. Thin data is itself a finding, because a category with no visible trade or retail footprint in a country is usually early or blocked. Both answers change your ranking, and neither needs a number invented to fill the cell.
How long should the first pass take?
An afternoon for the table, and a second sitting for the checks. Most of the time goes into writing the criteria and verifying three claims, not the research run. Founders who rush the criteria redo the scan a week later with new columns. A scan built this way is what founders leave with at A.D.A.P.T. AI Coaching.