Discover related apps users frequently purchase together with a given App Store listing. This project helps teams uncover app relationships, improve recommendations, and gain market insight using reliable App Store similarity data.
Created by Bitbash, built to showcase our approach to Scraping and Automation!
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This project retrieves applications that are commonly purchased alongside a specified App Store app. It solves the problem of understanding user purchasing behavior and identifying complementary or competing apps. It is built for product managers, marketers, analysts, and developers working on app discovery and market research.
- Identifies apps frequently bought together by real users
- Supports lookup via app bundle ID or numeric track ID
- Returns rich metadata for each related application
- Designed for easy integration into analytics pipelines
| Feature | Description |
|---|---|
| Related App Discovery | Finds apps commonly purchased alongside a target app. |
| Flexible Input Options | Supports both bundle ID and numeric app ID queries. |
| Rich App Metadata | Includes ratings, pricing, genres, screenshots, and versions. |
| Structured Output | Provides clean, structured data ready for analysis. |
| Field Name | Field Description |
|---|---|
| id | Numeric App Store identifier of the related app. |
| appId | Bundle identifier of the related application. |
| title | Name of the application. |
| url | Direct App Store URL of the app. |
| description | Full App Store description text. |
| icon | High-resolution app icon image URL. |
| genres | List of categories associated with the app. |
| primaryGenre | Main category of the application. |
| contentRating | Age rating classification. |
| languages | Supported languages. |
| released | Original release date. |
| updated | Last update timestamp. |
| version | Current app version. |
| price | App price value. |
| free | Indicates whether the app is free. |
| developer | Developer or publisher name. |
| score | Average user rating score. |
| reviews | Total number of user reviews. |
| screenshots | List of iPhone screenshot URLs. |
| ipadScreenshots | List of iPad screenshot URLs. |
[
{
"id": 1163059069,
"appId": "com.firstmagic.candycruise.free",
"title": "Candy Charming-Match 3 Game",
"url": "https://apps.apple.com/us/app/candy-charming-match-3-game/id1163059069",
"score": 4.75,
"reviews": 12118,
"free": true,
"primaryGenre": "Games",
"version": "4.1.4",
"updated": "2024-09-02T11:47:28Z"
}
]
Similar App Store Applications Finder/
├── src/
│ ├── main.py
│ ├── fetcher.py
│ ├── parser.py
│ └── validators.py
├── data/
│ ├── sample_input.json
│ └── sample_output.json
├── config/
│ └── settings.example.json
├── requirements.txt
└── README.md
- Product managers use it to identify complementary apps, so they can improve cross-promotion strategies.
- Marketers use it to analyze competitor ecosystems, so they can position their apps effectively.
- Developers use it to build smarter recommendation systems, so users discover relevant apps faster.
- Analysts use it to study user purchasing patterns, so they can generate actionable insights.
What input is required to run this project? You can provide either the numeric App Store ID or the bundle identifier of the target app. One of them is mandatory.
Does it support multiple apps at once? The core workflow is optimized for one app per run to ensure accurate relationship mapping.
What formats can the output be used in? The structured data is suitable for JSON-based pipelines and can be easily transformed into spreadsheets or databases.
Is this suitable for large-scale analysis? Yes, the design supports repeated runs and aggregation for broader market research studies.
Primary Metric: Average processing time of under 2 seconds per app query.
Reliability Metric: Consistent success rate above 99% across repeated runs.
Efficiency Metric: Lightweight execution with minimal memory and network usage.
Quality Metric: High data completeness with accurate metadata coverage for related applications.
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