SKU Data Scraping & Product Details Extraction

About the Client

Location: Chicago, USA

Industry: E-commerce & Product Distribution

Objective: To extract detailed product data for 200 SKUs (initial batch) — including images, descriptions, specifications, and identifiers (UPC/MPN) — from multiple manufacturer and retailer websites.

The client's ultimate goal was to build a comprehensive master catalog for internal listing and marketplace uploads. Upon successful delivery, the project would scale to 2,000+ SKUs across multiple brands and categories.

Project Overview

The client required structured product data suitable for uploading to marketplaces like Amazon, Walmart, Shopify, and eBay. Each SKU needed verified and enriched fields including:

Primary Fields (High Priority)

Product Name
Full Description
High-Resolution Image URLs

Secondary Fields

MPN / UPC / EAN
Weight & Dimensions
Color / Material
Technical Specifications
Category / Subcategory
Price (where public)

Output Format: Excel or CSV

Accuracy Target: ≥ 98% verified completeness

The Challenge

Extracting detailed product data across hundreds of SKUs seems simple, but it presents multiple technical and operational challenges:

Inconsistent Product Pages: Each manufacturer's layout and tag structure vary, requiring site-specific scrapers.
Image Handling: Some websites store multiple image versions or use lazy loading, which complicates direct URL extraction.
Incomplete or Hidden Data: Many product pages hide MPN, weight, or specifications behind tabs or JavaScript.
Data Quality: Descriptions may include HTML tags, repeated text, or embedded special characters that must be cleaned.
Volume & Scalability: While the initial batch was 200 SKUs, the long-term plan involved 2,000+ SKUs, demanding scalable infrastructure.

Goals & Deliverables

Actowiz Solutions was tasked to:

Scrape product data for 200 SKUs with no missing entries.
Deliver the following key fields per SKU:

Description (text + HTML cleaned)
Primary Image URL(s)
MPN/UPC/EAN
Weight, Color, Specifications

Output the dataset in Excel and CSV.
Maintain a data quality report outlining:

Total items processed
Fields completed per item
Missing or inferred values

Technology Stack

Function	Tools / Frameworks
Core Scraper	Python (Scrapy + Requests + Playwright)
HTML Parsing	BeautifulSoup4, lxml
JavaScript Handling	Playwright (headless Chromium)
Image Extraction	Regex patterning & attribute parsing
Data Cleaning	Pandas + Regular Expressions
Validation	UPC & MPN regex filters
Output	Excel / CSV via Pandas
Logging	Python logging + custom retry handler

Scraping Workflow

[ Product URLs / SKU List ]

↓

[ Scrapy Spider → Playwright Renderer (for dynamic sites) ]

↓

[ Extraction Layer ]

→ Product Title

→ Description (clean HTML)

→ Image URLs

→ Specs (Weight, Color, etc.)

↓

[ Validation & Deduplication ]

↓

[ Data Cleaning & Normalization ]

↓

[ Export → Excel / CSV + Quality Report ]

Implementation Highlights

1. Dynamic Rendering with Playwright

Many retailer websites used lazy-loaded content. Actowiz Solutions used Playwright headless browser to render the DOM fully before parsing text and image elements.

2. Smart Description Cleaning

Descriptions were cleaned with regex rules to remove extra line breaks, tags, and irrelevant scripts, while maintaining bullet points and formatting.

3. Multi-Image Extraction

For each SKU, all <img> tags inside the product gallery section were scraped and converted into full URLs.

4. MPN/UPC Validation

Patterns like:

(\b\d{8,14}\b)

were used to detect valid numeric UPCs. Alphanumeric MPNs were standardized to uppercase.

5. Specification Parsing

Product detail tables were mapped into key-value pairs. Example:

Attribute	Extracted
Weight	1.5 kg
Dimensions	25x18x9 cm
Color	Blue
Material	Aluminum

6. Deduplication & Quality Assurance

If multiple sources listed the same SKU, the scraper prioritized official brand/manufacturer data for consistency.

Sample Dataset (Illustrative)

SKU	Product Name	Description	MPN	UPC	Weight	Color	Image URL	City of Origin
SKU001	Stainless Steel Travel Mug 500ml	Insulated 500ml stainless mug with spill-proof lid.	TM-500SS	87432948172	0.5 kg	Silver	https://example.com/mug.jpg	Chicago
SKU002	Noise-Canceling Headphones	Wireless headphones with 20h battery life.	NC-H200	098432874321	0.9 kg	Black	https://example.com/headphones.jpg	Boston
SKU003	Yoga Mat Eco 6mm	Non-slip eco mat with carrying strap.	YM-ECO6	88213457492	1.2 kg	Blue	https://example.com/yogamat.jpg	Austin
SKU004	Smartwatch Sport 4.0	Waterproof smartwatch with heart rate monitor.	SW-4SPORT	987654112345	0.35 kg	Red	https://example.com/watch.jpg	Miami

Data Cleaning Example

Before Cleaning:

<p><b>Features:</b><br>High-quality material<br>Available in red and black<br><script>alert('promo');</script></p>

After Cleaning:

Features: • High-quality material • Available in red and black

Infographic Concept – "How Actowiz Scrapes SKU-Level Product Data"

Chart Example – Data Completeness by Field

Field	Completion %
Description	100%
Image	100%
MPN/UPC	94%
Weight	87%
Color	91%
Other Specs	85%

Quality Metrics

Metric	Result
Total SKUs Scraped	200
Average Fields per SKU	8.5
Verified Images	200 (100%)
Verified Descriptions	200 (100%)
MPN / UPC Captured	94%
Data Accuracy	98.6%
Turnaround Time	4 business days

Challenges Solved

Multiple Layouts, One Parser: Unified schema mapping allowed extraction from dozens of page structures.
Image Parsing Consistency: Implemented fallback logic for alternate <meta property="og:image"> tags.
Data Gaps: Implemented rule-based inference (e.g., color from title text).
Scalability: Pipeline prepared for 10× more SKUs without performance issues.

Project Outcome

Delivered clean Excel dataset with all 200 products fully enriched.
Met 100% of priority field requirements.
Validated images and descriptions for eCommerce listing readiness.
Developed scraper capable of handling 2,000+ SKUs in future batches.

Client Benefits

Faster Catalog Creation: Eliminated manual entry time.
Accurate Product Data: Improved buyer confidence on listings.
Marketplace Compatibility: CSV ready for Amazon, Shopify, WooCommerce.
Scalable Framework: Easily expandable to thousands of SKUs.

Client Testimonial

“Actowiz Solutions provided a complete product dataset, clean and verified. The SKU scraping was accurate, and every image and specification matched perfectly. We plan to expand to 2,000+ items with their support.”

— Product Data Manager, Chicago-based E-commerce Distributor

Ethical & Compliance Practices

Collected publicly available product information only.
No bypassing of CAPTCHAs or restricted data sources.
Followed robots.txt and fair-use scraping policies.
Data used solely for internal product catalog creation.

Actowiz Solutions ensures full compliance with international data protection standards and ethical data sourcing norms.

Why Choose Actowiz Solutions

Experience with large-scale SKU & eCommerce scraping.
Expertise in image extraction, HTML cleaning, and spec mapping.
Scalable, reliable Python-based infrastructure.
End-to-end delivery with accuracy and documentation.

Future Enhancements

Add real-time price monitoring from multiple retailers.
Integrate AI-based product classification by category.
Enable API-based delivery to sync SKU data automatically.
Expand to competitor benchmarking and stock tracking.

Conclusion

This case study demonstrates how Actowiz Solutions helped an eCommerce client automate SKU-level data extraction from multiple product websites, covering descriptions, images, and technical specs with near-perfect accuracy.

By leveraging Python Scrapy + Playwright, the solution delivered verified, ready-to-use product data—reducing manual effort by over 90% and setting the stage for future large-scale catalog updates.

Whether for marketplaces, distributors, or analytics teams, Actowiz Solutions provides the tools and expertise to convert scattered web data into structured, actionable product intelligence.

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Array ( [as_domain] => amazon.com [as_name] => Amazon.com, Inc. [asn] => AS16509 [continent] => North America [continent_code] => NA [country] => United States [country_code] => US )

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Product SKU Data Scraping: Description, Image & Specifications Extraction

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About the Client

Project Overview

Primary Fields (High Priority)

Secondary Fields

The Challenge

Goals & Deliverables

Technology Stack

Scraping Workflow

Implementation Highlights

1. Dynamic Rendering with Playwright

2. Smart Description Cleaning

3. Multi-Image Extraction

4. MPN/UPC Validation

5. Specification Parsing

6. Deduplication & Quality Assurance

Sample Dataset (Illustrative)

Data Cleaning Example

Before Cleaning:

After Cleaning:

Infographic Concept – "How Actowiz Scrapes SKU-Level Product Data"

Chart Example – Data Completeness by Field

Quality Metrics

Challenges Solved

Project Outcome

Client Benefits

Client Testimonial

Ethical & Compliance Practices

Why Choose Actowiz Solutions

Future Enhancements

Conclusion

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