Sears Scraper is a data extraction tool designed to collect structured product information from Sears product pages. It helps businesses and analysts turn complex product listings into clean, usable datasets for analysis, monitoring, and integration workflows.
This project focuses on extracting detailed retail product data at scale, enabling better visibility into pricing, availability, specifications, and catalog structure.
Created by Bitbash, built to showcase our approach to Scraping and Automation!
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Sears Scraper processes product URLs and returns comprehensive, structured product records. It solves the challenge of manually collecting and organizing large volumes of retail product information. This tool is suitable for analysts, developers, and businesses working with e-commerce data.
- Processes individual product URLs in a structured and repeatable way
- Normalizes pricing, availability, and specification data
- Captures hierarchical category and SEO metadata
- Outputs consistent product records for downstream systems
| Feature | Description |
|---|---|
| Product Metadata Capture | Extracts product name, SKU, brand, model number, UPC, and catalog identifiers. |
| Pricing Details | Collects regular price, sale price, savings, and promotional indicators. |
| Availability Tracking | Identifies stock status, showroom eligibility, and delivery options. |
| Specification Parsing | Structures detailed technical specifications and attributes. |
| Seller Information | Captures seller name, condition, and marketplace indicators. |
| Media Assets | Retrieves main images, galleries, and associated media URLs. |
| Category Hierarchies | Extracts multi-level product category and navigation data. |
| SEO Data | Collects SEO titles, descriptions, and canonical URLs. |
| Field Name | Field Description |
|---|---|
| url | Source product page URL |
| brandName | Brand or manufacturer name |
| modelNo | Manufacturer model number |
| sku / partNum | Product part or SKU identifier |
| price | Structured pricing information |
| salePrice | Current discounted price |
| regularPrice | Standard listed price |
| availability | Stock and fulfillment indicators |
| specifications | Structured technical attributes |
| images | Image and media URLs |
| rating | Average customer rating |
| reviewCount | Total number of reviews |
| categories | Hierarchical category placement |
| seller | Seller and condition details |
| seoData | SEO title and description fields |
[
{
"url": "https://www.sears.com/example-product",
"brandName": "GE Profile Series",
"modelNo": "PSB48YSNSS",
"sku": "04682793000",
"salePrice": "9836.99",
"regularPrice": "10929.99",
"availability": "Out of Stock",
"rating": null,
"reviewCount": null,
"categories": [
"Appliances",
"Refrigerators",
"Side-by-Side Refrigerators"
],
"seller": "Sears"
}
]
sears-scraper/
├── src/
│ ├── runner.py
│ ├── parsers/
│ │ ├── product_parser.py
│ │ └── pricing_parser.py
│ ├── utils/
│ │ ├── normalizer.py
│ │ └── validators.py
│ └── config/
│ └── settings.example.json
├── data/
│ ├── inputs.sample.txt
│ └── sample_output.json
├── requirements.txt
└── README.md
- Retail analysts use it to monitor product pricing changes, so they can track market trends.
- E-commerce teams use it to audit product catalogs, so they can maintain consistent listings.
- Data engineers use it to feed product data into analytics pipelines, so they can build dashboards.
- Market researchers use it to analyze product attributes, so they can compare competitors.
- Automation developers use it to generate structured product datasets, so they can integrate systems.
What type of product pages are supported? The tool is designed to process standard Sears product detail pages that include pricing, specifications, and media sections.
Does it handle missing or optional fields? Yes. Fields that are not present on a product page are returned as null or empty values to maintain schema consistency.
Can the output be used in databases or analytics tools? The structured output format is suitable for direct ingestion into databases, spreadsheets, or analytics platforms.
Is the data structure consistent across products? Yes. The scraper normalizes fields to ensure consistent keys even when product attributes vary.
Primary Metric: Processes product pages with consistent field extraction per URL.
Reliability Metric: Stable handling of optional and missing data fields across varied product categories.
Efficiency Metric: Optimized parsing minimizes unnecessary processing overhead per product.
Quality Metric: High data completeness for core product attributes such as pricing, specifications, and media.
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