Finding similar apps
Three ways to reach the apps that sit next to the one you are reading, and what each neighbourhood actually means.
Download Markdown ↓There is no single "similar apps" button. There are three different neighbourhoods, and they mean different things. Pick the one that matches the question you are asking.
| You want | Go to |
|---|---|
| Apps competing on one specific search | Open that keyword's ranking on the ASO tab |
| Apps on the same shelf | The category crumb, or the Categories page |
| Apps at a similar commercial level | The public chart around its rank |
Apps in ranking: the neighbours on one search
In the keyword table on the ASO tab, click a ranking preview. Apps in ranking lists the apps that appeared for that search, in position order, with the app you are reading highlighted.
This is the tightest possible neighbourhood: the exact set of apps a person saw when they typed that phrase, on the day it was collected. Any app in the list that has its own case file is a link.
When a public response contains more results than the stored observation, the heading explains that the visible result set is incomplete.
The shelf: same category
Two routes into a category:
- The category crumb above a case file, or the category chip in a chart row, opens the public chart for that shelf. That gives you the shelf ranked by position.
- Apps → Categories lists every shelf in the catalogue snapshot with how many apps sit on it. Picking one opens the catalogue filtered to that category, ordered by how many people rated each app.
Category is the store's own classification, so it is broad. It answers "what else is filed here", not "what else competes with this".
The chart: same commercial level
From a chart row, clicking the rank opens that app's rank history; going back to the board shows you the apps immediately above and below it on the same chart, on the same day. For grossing charts in particular, that is a useful peer set: apps holding a similar position on the same shelf, in the same storefront.
Tracking a competitor
When a rival is worth following rather than just reading, put it on one of your boards. Use Add to board on its case file, or Track an app on a board. PeekPanda then tracks everything about it: its paid ads, organic posts, keywords and chart position, collected from the day you add it. Open the board to see what changed.
For agents
The API and MCP profile expose a stored similar-listings read, get_similar_competitors, at GET /v1/competitors/{platform}/{storeId}/similar (in MCP, search_apps with similar). It returns the competitor list's paginated envelope, strongest evidence first, and every app says why it is there:
matchReason | What it means |
|---|---|
shared_searches | It ranks in the top 100 for at least two US App Store searches this app ranks for, and at least one of them does not name this app. |
similar_listing | Its name, in the same category, holds a job from this app's searches or current name, such as "recipe keeper". |
similar_description | It is one of the 1,000 most-rated apps in the same category, and its description names at least two of this app's jobs word for word, such as "meal planner". |
same_category | Fallback only: same category, and its name shares a word with this app. |
sharedSearches is how many US searches both apps rank for; it is null for same_category. Jobs are two-word phrases taken from the app's current App Store name and from the searches it ranks for; a search that copies another app's title ("Samsung Food: Meal Planner") adds that app's job, never its brand. An app that shares searches and does the same jobs ranks above one that only shares searches or only shares words. Evidence counts more when the other app ranks higher, and among equally similar apps the one with more ratings comes first. Searches that only name this app never make a match on their own.
Send fallback=none to get evidence-backed apps only, or leave the default fallback=category to fill the rest of the list with same-category apps. It is a stored read: it never triggers collection.
Related
AI agent or LLM? Read this page as markdown