Dillards Scraper is a powerful tool for extracting detailed product data from dillards.com with precision and speed. It helps businesses and analysts collect structured retail data for pricing insights, catalog analysis, and competitive research.
Created by Bitbash, built to showcase our approach to Scraping and Automation!
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This project extracts rich product information directly from individual Dillards product pages. It solves the challenge of manually collecting consistent, up-to-date retail data. It is ideal for eCommerce analysts, data engineers, and retail intelligence teams.
- Accepts multiple product URLs in a single run
- Extracts pricing, images, brand, and SKU-level details
- Designed for reliable, repeatable data collection
- Outputs clean, structured JSON ready for downstream systems
| Feature | Description |
|---|---|
| Multi-URL Input | Scrape multiple Dillards product pages in one execution |
| SKU-Level Pricing | Extracts list price, offers, and return eligibility |
| Image Collection | Retrieves all product images with alt text |
| Brand & Category Data | Captures brand names and product categories |
| Structured Output | Clean JSON format suitable for analytics and storage |
| Field Name | Field Description |
|---|---|
| productName | Full product name as listed on the product page |
| brandNameForTitle | Brand name formatted for titles |
| brandUrll | Brand landing page URL |
| superCat | Top-level product category |
| imagelists | Array of product images with URLs and alt text |
| skulists | SKU-level pricing, UPCs, and return status |
| listPrice | Original retail price |
| offer | Discounted or sale price |
| isReturnable | Indicates if the SKU can be returned |
[
{
"productName": "Long Sleeve Open Front Coordinating Lounge Duster Cashmere Cardigan",
"brandNameForTitle": "VAN WINKLE & CO.",
"brandUrll": "https://www.dillards.com/brand/VAN+WINKLE+and+CO.",
"superCat": "Women's Clothing",
"imagelists": [
{
"alttext": "Color:Black - Image 1",
"full_image_url": "https://dimg.dillards.com/is/image/DillardsZoom/main/sample.jpg"
}
],
"skulists": [
{
"prtnumaber": "1120588",
"upc": "000198054026142",
"style": "F42RV401",
"listPrice": 198,
"offer": 138.6,
"isReturnable": true
}
]
}
]
Dillards Scraper/
├── src/
│ ├── main.py
│ ├── scraper/
│ │ ├── product_parser.py
│ │ └── sku_parser.py
│ ├── utils/
│ │ ├── http_client.py
│ │ └── helpers.py
│ └── config/
│ └── settings.example.json
├── data/
│ ├── input.sample.json
│ └── output.sample.json
├── requirements.txt
└── README.md
- Retail analysts use it to monitor product pricing, so they can track discounts and trends.
- E-commerce teams use it to build internal catalogs, so they can keep product data consistent.
- Market researchers use it to analyze brands and categories, so they can identify opportunities.
- Data engineers use it to feed analytics pipelines, so they can automate reporting.
- Price monitoring services use it to detect price changes, so they can trigger alerts.
Does this scraper support multiple products at once? Yes, you can provide multiple product URLs in a single input to scrape them simultaneously.
Is proxy usage required? Proxies are strongly recommended to ensure stable access and reduce the risk of request blocking.
What happens if a product is out of stock? The scraper still extracts available metadata and SKU information when present.
Can the output be integrated into other systems? Yes, the structured JSON output is suitable for databases, dashboards, and APIs.
Primary Metric: Average extraction time of 2–4 seconds per product page.
Reliability Metric: Over 99% successful page parsing on valid product URLs.
Efficiency Metric: Capable of processing dozens of product URLs per minute under normal conditions.
Quality Metric: High data completeness with consistent SKU-level pricing and image coverage.
