A fast and reliable tool for extracting structured product data from Macy’s, including pricing, colors, descriptions, and image sets. It helps analysts, researchers, and e-commerce teams gather accurate information at scale. Built for consistency, clarity, and real-world automation workflows. This Macy's scraper delivers clean, ready-to-use datasets for retail intelligence and competitive research.
Created by Bitbash, built to showcase our approach to Scraping and Automation!
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This project automates the process of extracting detailed product information from Macy’s. It solves the challenge of manually collecting and monitoring product listings, especially when thousands of items need to be analyzed regularly. It is ideal for data teams, market analysts, e-commerce sellers, price-monitoring systems, and anyone who needs structured information from Macy’s product catalog.
- Enables large-scale monitoring of product availability and pricing trends.
- Delivers richer product information than traditional catalog endpoints.
- Supports competitive research by capturing detailed attributes.
- Works efficiently for scheduled or recurring extraction tasks.
| Feature | Description |
|---|---|
| Product detail extraction | Captures full product descriptions for deeper analysis. |
| Price and sale price parsing | Extracts structured pricing fields, including discounts and promotions. |
| Color and variant support | Retrieves all color options available for a product. |
| Image scraping | Saves high-resolution product images and galleries. |
| Structured outputs | Returns clean JSON suitable for analytics and automation. |
| Field Name | Field Description |
|---|---|
| title | The product’s full display name. |
| description | Detailed product information and specifications. |
| price | Standard product price. |
| sale_price | Discounted or promotional price. |
| colors | List of available colors or variants. |
| images | Array of image URLs and galleries. |
| product_url | URL of the scraped product page. |
| availability | Whether the product is available or out of stock. |
[
{
"title": "Men's Classic Fit Wool Coat",
"description": "A timeless wool coat with button closure...",
"price": 199.99,
"sale_price": 129.99,
"colors": ["Black", "Navy"],
"images": [
"https://images.macysassets.com/.../coat1.jpg",
"https://images.macysassets.com/.../coat2.jpg"
],
"product_url": "https://www.macys.com/shop/product/12345",
"availability": "In Stock"
}
]
Macy's Scraper/
├── src/
│ ├── runner.py
│ ├── extractors/
│ │ ├── product_parser.py
│ │ ├── price_utils.py
│ │ └── image_collector.py
│ ├── outputs/
│ │ └── exporters.py
│ └── config/
│ └── settings.example.json
├── data/
│ ├── keywords.sample.txt
│ └── sample_output.json
├── requirements.txt
└── README.md
- Market analysts use it to gather product details so they can compare trends and shifts in pricing across categories.
- E-commerce sellers use it to monitor competitor listings so they can optimize pricing and stock decisions.
- Data scientists use it to build datasets for prediction models, product-matching engines, or recommendation systems.
- Retail intelligence firms use it to update databases automatically, ensuring fresh, high-quality catalog data.
- Developers use it to feed structured product information into dashboards, CRMs, or automation pipelines.
Q1: How many product pages can be scraped at once? You can scrape hundreds or thousands of listings in a single batch, as long as your resources support parallel processing.
Q2: Does the scraper capture sale prices and discounts? Yes, it extracts standard and discounted prices, including promotional values where available.
Q3: Can this tool detect when products go out of stock? Yes, the availability field shows when an item is sold out or unavailable.
Q4: What formats can the extracted data be exported to? You can export structured results into JSON, CSV, Excel, or integrate with downstream systems through custom exporters.
Primary Metric: Handles an average of 350–500 product pages per minute with consistent parsing accuracy across categories.
Reliability Metric: Maintains a 98% success rate on stable connections, with automatic recovery from intermittent page failures.
Efficiency Metric: Optimized request handling ensures low bandwidth use and efficient resource consumption even at scale.
Quality Metric: Extracted datasets achieve over 95% field completeness, ensuring dependable input for analytics and machine-learning pipelines.
"Bitbash is a top-tier automation partner, innovative, reliable, and dedicated to delivering real results every time." Nathan Pennington
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"Bitbash delivers outstanding quality, speed, and professionalism, truly a team you can rely on." Eliza
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"Exceptional results, clear communication, and flawless delivery. Syed
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