Kick Game Scraper is a focused data extraction tool that collects footwear product information and pricing from the Kick Game online store. It helps teams monitor products, analyze pricing trends, and build reliable datasets for e-commerce insights using structured output.
Created by Bitbash, built to showcase our approach to Scraping and Automation!
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Kick Game Scraper extracts detailed product and pricing data from the Kick Game footwear catalog. It solves the problem of manually tracking fast-changing product information in competitive sneaker and footwear markets. This project is built for developers, analysts, and e-commerce teams who need clean, reusable data for analysis and automation.
- Collects structured product listings from an online footwear store
- Normalizes pricing and availability data for analysis
- Supports repeated runs for ongoing price monitoring
- Outputs data ready for apps, reports, or spreadsheets
| Feature | Description |
|---|---|
| Product Catalog Extraction | Retrieves complete footwear product listings with key metadata. |
| Pricing Tracking | Captures current prices to support comparison and monitoring. |
| Structured Output | Delivers clean, machine-readable data formats. |
| Scalable Crawling | Handles large catalogs without manual intervention. |
| Data Reusability | Output fits analytics tools, dashboards, and workflows. |
| Field Name | Field Description |
|---|---|
| product_id | Unique identifier for the footwear product. |
| name | Official product name as listed in the store. |
| brand | Brand or manufacturer of the footwear item. |
| category | Product category or collection. |
| price | Current retail price of the product. |
| currency | Currency associated with the price. |
| availability | Stock or availability status. |
| product_url | Direct link to the product page. |
| image_url | Primary image of the product. |
[
{
"product_id": "KG-10231",
"name": "Air Jordan 1 Retro High",
"brand": "Nike",
"category": "Sneakers",
"price": 189.99,
"currency": "GBP",
"availability": "In Stock",
"product_url": "https://www.kickgame.co.uk/products/air-jordan-1-retro-high",
"image_url": "https://cdn.kickgame.co.uk/images/air-jordan-1.jpg"
}
]
Kick Game Scraper/
├── src/
│ ├── main.py
│ ├── scraper/
│ │ ├── product_parser.py
│ │ └── price_extractor.py
│ ├── utils/
│ │ └── helpers.py
│ └── config/
│ └── settings.example.json
├── data/
│ ├── sample_input.json
│ └── sample_output.json
├── requirements.txt
└── README.md
- E-commerce analysts use it to track footwear pricing, so they can identify market trends and opportunities.
- Retail teams use it to monitor competitor products, helping them adjust pricing strategies faster.
- Developers use it to feed product data into apps, enabling real-time catalog updates.
- Market researchers use it to build datasets for footwear demand and brand analysis.
Is this scraper limited to footwear only? The scraper is designed specifically for footwear products listed in the Kick Game catalog, ensuring accurate and relevant data fields.
Can the output be used in spreadsheets or dashboards? Yes, the structured output is suitable for spreadsheets, BI tools, and custom analytics pipelines.
How often can data be extracted? The scraper supports repeated runs, making it suitable for daily or periodic price and product monitoring.
Does it handle large product catalogs reliably? It is built to scale across large inventories while maintaining consistent data structure.
Primary Metric: Average extraction speed of approximately 250–350 products per minute, depending on catalog size.
Reliability Metric: Consistent success rate above 98% across repeated runs on stable product pages.
Efficiency Metric: Optimized requests minimize redundant data fetching, reducing overall processing time.
Quality Metric: Over 99% field completeness for core product and pricing attributes in test datasets.
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