How Do “Who This Is For” and “Who This Is Not For” Pages Help Buyers and AI?

Why do fit statements matter more with AI?

• Prompts carry constraints. Semrush's study of ChatGPT clickstream data found prompts without search averaged 23 words in late 2024 and about 13.5 words in early 2026, still far longer than typical searches. Google says AI Mode searches average three times the length of traditional queries. Those extra words are usually context: "for a 12-person agency", "that works with Xero", "under £50 a month".
• Assistants match constraints to sources. A product page that never mentions team size or integrations gives the assistant nothing to match. A fit page that says "built for teams of 5 to 50 that use Xero" does.
• Caveats come from somewhere. Answers often add "but it may not suit larger teams". If you do not state your limits, the assistant takes them from reviews, forums or competitors' comparisons, which may exaggerate them.
• Shopping assistants are built for this. OpenAI's shopping research asks clarifying questions before recommending. In OpenAI's own evaluation it was more accurate than standard ChatGPT search on queries with several constraints.

How phrasing changes recommendations is covered in whether buyer phrasing changes AI recommendations more than the platform you use.

What should the page say?

1. Who it is for, as specific segments: size, industry, role, use case, budget range, region, technical setup.
2. Why it suits them: the features, pricing or service model that make it a fit, with a proof point for each.
3. Who it is not for, with the reason: a missing feature, a scale limit, a region you do not serve, a price point.
4. What to consider instead, in general terms or by naming alternatives fairly.
5. Prerequisites: what a customer needs to have, such as an admin, a data export, a particular system or a minimum volume.
6. A quick self-check: five or six yes-or-no questions a reader can answer in a minute.
7. Examples of customers who fit, with results where you can share them.

What does a good example look like?

Who Shiftmoor Rota is for

• Restaurants, cafés and shops with 10 to 200 hourly staff across up to 20 sites in the UK and Ireland.
• Managers who build weekly rotas and need staff to swap shifts from their phones.
• Businesses using Xero, Sage or BrightPay for payroll, since hours export directly.

Who it is not for

• Businesses with fewer than 5 staff: our free plan covers basic rotas, but most small teams find a spreadsheet enough.
• Organisations with more than 2,000 employees or complex union rules, which usually need an enterprise workforce management system.
• Companies outside the UK and Ireland, since holiday and pay rules are built for those two countries.
• Teams that need built-in payroll, which we do not offer.

Each statement is specific and checkable, and each "not for" has a reason. An assistant asked "what rota software suits a 30-person café in Leeds?" can match the first list. Asked "rota software with built-in payroll", it can see this product does not fit, and your page does not waste the buyer's time.

Where should fit statements live?

• A dedicated fit page for each main product or service, linked from the navigation or product pages.
• A short "Best for / Not for" box on product and pricing pages.
• Comparison pages, where "choose us if... choose them if..." is the natural format.
• Industry and use-case pages, each stating the fit for that segment.
• Sales and onboarding material, so the same wording appears everywhere.

Keep the statements consistent across all of them. An assistant that finds "for teams of 5 to 50" on one page and "for businesses of any size" on another cannot use either. How to write product descriptions that match buyer queries is covered in how to write clear product descriptions that AI can match to buyer queries.

Will saying who it is not for cost sales?

Usually the opposite:

• It filters out poor-fit leads, who take sales time, churn quickly and write unhappy reviews.
• It raises trust. A company that admits limits is more believable about its strengths.
• It shapes the caveats assistants use. Better that an answer says "not designed for enterprises over 2,000 staff", quoting you, than "users complain it doesn't scale", quoting a frustrated review.
• It helps you be recommended for the right questions. An assistant confident about your fit is more likely to name you when the question matches it.

The businesses that lose from fit statements are those whose product fits almost no one well. For them, the page is a useful warning.

How do you write "not for" without sounding negative?

• Frame it as fit, not failure: "built for teams under 200" rather than "can't handle large teams".
• Give the reason, so it reads as a design choice.
• Point people somewhere useful: a type of product, an enterprise tier, a partner.
• Name competitors only fairly and with facts, if at all.
• Update it when the product grows, so you do not turn away customers you now serve.

How do you check whether it works?

Ask assistants constraint-heavy questions that match your fit and some that do not. For example: "best rota software for a 40-person restaurant group using Xero" and "rota software with built-in payroll for 3,000 staff". Check whether you are recommended for the first, left out of the second, and described accurately in both. Track whether leads from AI answers fit better over time, through sales qualification data or win rates.

What might an illustrative case look like?

Consider an illustrative event ticketing platform that described itself as "ticketing for events of every size". Assistants rarely recommended it, and when they did, often for large festivals it could not handle. It published a fit page. The page said it was for independent venues and promoters selling up to 5,000 tickets per event, and not for stadium events or organisations needing assigned seating. It added a "best for / not for" box to its pricing page. Within three months, assistants recommended it for small venue questions and described its limits accurately. Its sales team reported fewer demos with organisations it could not serve.

What do people ask about fit pages?

Should every product have a fit page?

Every main product or service that buyers compare. A short box on smaller product pages is often enough.

Can we say who it is not for if it is technically possible?

Say what it is designed for. If something is possible but awkward, say that honestly.

Will competitors use our "not for" list against us?

They might, but buyers and assistants will find the limits anyway. Stating them yourself keeps the framing fair.

How specific should we be about size?

As specific as your data allows. Ranges based on where current customers succeed are more useful than "small to medium".