Guide · AI answers for apps
Where ChatGPT’s app recommendations come from
Nearly half the time ChatGPT recommends an app, the page behind the pick is a store listing. Know which pages it reads in your category and you know exactly where to win. Here’s the data on what ChatGPT cites, how it shifts by category, and how to find the pages that decide your app’s fate.
The data behind the playbook
The largest public look at this so far comes from AppTweak, an app store optimisation company. It analysed more than 125,000 ChatGPT responses to 9,489 app prompts and over 9,000 game prompts, in the US, in May 2026. [1] The prompts were non-branded, the way people ask when they don’t yet know which app they want: “best app to track my running”, not “is Strava good”. Store citations were counted from the public listing pages on apps.apple.com and play.google.com. [1]
Before using the numbers, it helps to know their limits:
- ChatGPT only. Gemini, Claude and Perplexity search the web their own way, and the study didn’t measure them.
- One country, one month. AppTweak itself says this is a May 2026 snapshot and that the way ChatGPT picks sources shifts over time. [1]
- A vendor’s study. AppTweak sells an AI visibility tool for apps, so read it as good data with a point of view.
It is still the clearest picture available, and the rest of this guide works through what it found. If you only want the to-do list, our guide to getting your app recommended by ChatGPT has it.
Your listing is the page ChatGPT reads most
Three takeaways for anyone shipping an app.
Your listing is the page that gets read most. Together, the two stores were 47.5% of citations, and the App Store alone was the single most-cited source. [1] A listing puts the app’s name, what it does, who it’s for and its ratings on one public page, which is exactly what a model needs to name an app with some confidence. (You may see 38% and 9.5% in another AppTweak article; those are the same figures rounded. [2])
The App Store outweighs Google Play about four to one for apps. If you ship on both, your iOS listing is doing more of the work in ChatGPT’s answers, at least in the US. Games are less lopsided: the App Store was 42.6% of citations for games and Google Play 16.0%. [1]
About a quarter of picks cite nothing. In those 22.6% of cases ChatGPT recommended from what the model already knew. [1] You can’t edit that memory directly, but it isn’t random: it reflects how often, and how consistently, your app has been described around the web.
The bottom of the list is up for grabs
Whether a recommendation comes with a source depends on where it sits in the answer. [1]
The first few apps are backed by pages ChatGPT just read, and they are hard to push out. The last few are often the model filling the list from memory, and AppTweak calls those places more open to challengers. [1] For a small app that is the realistic path: get into the list at all, with a page behind you, then move up as more good pages mention you.
Know what your category runs on
The average hides big differences between categories. [1]
| Category | Store listings | Other web pages | No source |
|---|---|---|---|
| Weather | 65.8% | 18.2% | 9.8% |
| Commerce and shopping | 50.5% | 28.6% | 17.5% |
| Medical | 45.5% | 30.4% | 18.6% |
| Education | 43.2% | 22.6% | 29.1% |
| Productivity and tools | 39.7% | 23.7% | 24.7% |
| House, home and real estate | 34.9% | 31.3% | 25.5% |
| Finance | 31.9% | 44.3% | 18.4% |
| Food and drinks | 29.3% | 45.7% | 22.3% |
In a simple, crowded category like weather, the listing is most of the story. In finance and food and drink, other web pages were cited more than the stores, so review sites, round-ups and your own website matter as much as the listing. Education had the most answers with no source at all, 29.1%. Games lean more on community and reference sites than apps do: AppTweak counted about five times as many Reddit citations, and five times as many Wikipedia citations, for games as for apps. [1]
Become the page AI quotes
AppTweak names three things that make a page likely to be cited: [1]
- Easy to lift from. Clear headings, lists, tables, FAQs and paragraphs that make sense on their own.
- On a trusted site. The same claim carries more weight on a high-trust domain than on a page nobody links to.
- Consistent with everything else. The listing, your website, reviews and community posts describe the app the same way.
The last one is where small teams most often slip. If the listing says “habit tracker”, the website says “your productivity companion” and Reddit calls it “that streak app”, a model gets three weak signals instead of one strong one. Pick the words once and use them everywhere.
Four moves, in order
- Turn your listing into the answer. It is the biggest share and fully in your hands. Open the description with what the app is, who it’s for and what it helps them do, then stick to three to five use cases. [2] Our guides to your App Store listing and your Google Play listing go field by field.
- Give AI one page on your site worth citing. Where brand-owned or round-up pages lead in a category, AppTweak’s advice is a clear product page and an FAQ on your own domain. [1] Make sure AI search crawlers can reach it: see llms.txt, AI crawlers and your app’s website.
- Show up where your category looks. In categories like finance, the round-ups and review sites carry as much weight as the stores. Our guide to the “best apps” articles and Reddit threads AI cites covers how to get in without spamming anyone.
- Measure, change one thing, measure again. AppTweak suggests setting a baseline before you change anything and checking again after each change. [1] Answers vary from one run to the next, so look at the trend over a few weeks, not one result.
Find the pages that decide your app’s fate
AppTweak suggests three checks, and you can do all of them by hand: [1]
- When your app is named, which pages are cited? That tells you which pages are already working for you.
- When competitors are named and you aren’t, which pages are cited? Those are the pages you need to be on, or to beat.
- Across your category, what is the mix of listings, other web pages and no source? That tells you where to spend your time.
To do it by hand, write down ten or so questions a customer would ask before downloading an app like yours, without your app’s name in them. Ask each one in ChatGPT, and for every app it names, note whether the source is a store listing, the app’s own site, a review or round-up page, or nothing. Run the same questions again a week later. Our guide on how to check if ChatGPT, Gemini and Claude recommend your app has a step-by-step method.
Questions people ask
Does this apply to Gemini, Claude or Perplexity?
The study measured ChatGPT only. [1] Other assistants search the web in their own way, so their mix may differ. Check each AI your customers use rather than assuming.
Does a higher store ranking get my app recommended?
The study looked at which pages are cited, not at store rankings, so it doesn’t answer that. What it does show is that the listing page itself is read and cited, so what your listing says matters on top of where it ranks.
My app is iOS only. Should I care about Google Play?
Not for your own listing. But competitors’ Play listings can still be cited in answers for your category, so it is worth knowing which ones are.
Sources
- Pierre-Antoine Roy, Which sources do AI search engines cite when recommending apps, AppTweak, 30 September 2026. Data: more than 125,000 ChatGPT responses to 9,489 app prompts and 9,000+ game prompts, US, May 2026.
- Pierre-Antoine Roy, How to optimize your app store listing for AI search engines, AppTweak, 16 August 2026.
Sources checked .