Competitors

• Store search results for the app's keywords, the store's similar-apps list, and category charts.
• Public listing data for each candidate: rating, review count, last update, publisher, and installs where the store shows them.
• Competitors the project tracks by hand. They take seats in the set first; tracked competitors beyond its seats are still tracked, but are not used in the audit benchmark.
• Rivals found when the same app was audited on the other store.

A 0 to 100 score of the set itself, which holds up to 10 competitors. On Google Play, and in several App Store categories including games, a language model rates each candidate as a direct rival (same core use and audience), an adjacent app, a same-genre app or unrelated, and direct rivals rank first. Apps whose names borrow a better-known rival's are dropped; in games, only when they are small beside the audited app. The publisher's own other apps are dropped too, except in games, where one of its titles is kept when it is rated a direct rival; for an App Store game filed under a non-game primary genre, only when its title or subtitle also names a subject of the audited app's title or subtitle. Stale and low-rated apps are mostly dropped; large established rivals and competitors tracked by hand are kept.

• Count: how many seats are filled.
• Quality: whether each competitor's installs, rating count and rating are known and strong.
• Breadth: the size of the set, discounted when it is a tiny share of the category.

• No recommendation rows of its own. The Keywords, User Intent and AI Discoverability engines compare against the set it picks.
• Notes on the set, such as too few competitors found, or rivals that average a very high rating, which means the app is benchmarked against strong listings.

• Competitors' private data: console figures, revenue or conversion.
• Rivals that none of these sources surface. Add them to the project by hand.
• Discovery for a draft listing before launch. The set is the one the user names.

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