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GeoIp2\Model\City Object
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                            [es] => Columbus
                            [fr] => Columbus
                            [ja] => コロンバス
                            [pt-BR] => Columbus
                            [ru] => Колумбус
                            [zh-CN] => 哥伦布
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                            [fr] => Amérique du Nord
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                            [pt-BR] => América do Norte
                            [ru] => Северная Америка
                            [zh-CN] => 北美洲
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            [postal] => Array
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                    [code] => 43215
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                            [en] => United States
                            [es] => Estados Unidos
                            [fr] => États Unis
                            [ja] => アメリカ
                            [pt-BR] => EUA
                            [ru] => США
                            [zh-CN] => 美国
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                                    [es] => Ohio
                                    [fr] => Ohio
                                    [ja] => オハイオ州
                                    [pt-BR] => Ohio
                                    [ru] => Огайо
                                    [zh-CN] => 俄亥俄州
                                )

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            [traits] => Array
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                    [ip_address] => 216.73.216.110
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    [continent:protected] => GeoIp2\Record\Continent Object
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                    [code] => NA
                    [geoname_id] => 6255149
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                            [de] => Nordamerika
                            [en] => North America
                            [es] => Norteamérica
                            [fr] => Amérique du Nord
                            [ja] => 北アメリカ
                            [pt-BR] => América do Norte
                            [ru] => Северная Америка
                            [zh-CN] => 北美洲
                        )

                )

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    [country:protected] => GeoIp2\Record\Country Object
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                    [geoname_id] => 6252001
                    [iso_code] => US
                    [names] => Array
                        (
                            [de] => USA
                            [en] => United States
                            [es] => Estados Unidos
                            [fr] => États Unis
                            [ja] => アメリカ
                            [pt-BR] => EUA
                            [ru] => США
                            [zh-CN] => 美国
                        )

                )

            [locales:GeoIp2\Record\AbstractPlaceRecord:private] => Array
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                    [0] => en
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            [validAttributes:protected] => Array
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                    [0] => confidence
                    [1] => geonameId
                    [2] => isInEuropeanUnion
                    [3] => isoCode
                    [4] => names
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        )

    [locales:protected] => Array
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            [0] => en
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    [maxmind:protected] => GeoIp2\Record\MaxMind Object
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            [record:GeoIp2\Record\AbstractRecord:private] => Array
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                )

            [validAttributes:protected] => Array
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                    [0] => queriesRemaining
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        )

    [registeredCountry:protected] => GeoIp2\Record\Country Object
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            [record:GeoIp2\Record\AbstractRecord:private] => Array
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                    [geoname_id] => 6252001
                    [iso_code] => US
                    [names] => Array
                        (
                            [de] => USA
                            [en] => United States
                            [es] => Estados Unidos
                            [fr] => États Unis
                            [ja] => アメリカ
                            [pt-BR] => EUA
                            [ru] => США
                            [zh-CN] => 美国
                        )

                )

            [locales:GeoIp2\Record\AbstractPlaceRecord:private] => Array
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            [validAttributes:protected] => Array
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                    [0] => confidence
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    [representedCountry:protected] => GeoIp2\Record\RepresentedCountry Object
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                    [2] => isInEuropeanUnion
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                    [4] => names
                    [5] => type
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        )

    [traits:protected] => GeoIp2\Record\Traits Object
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            [record:GeoIp2\Record\AbstractRecord:private] => Array
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                    [ip_address] => 216.73.216.110
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                    [network] => 216.73.216.0/22
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            [validAttributes:protected] => Array
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                    [0] => autonomousSystemNumber
                    [1] => autonomousSystemOrganization
                    [2] => connectionType
                    [3] => domain
                    [4] => ipAddress
                    [5] => isAnonymous
                    [6] => isAnonymousProxy
                    [7] => isAnonymousVpn
                    [8] => isHostingProvider
                    [9] => isLegitimateProxy
                    [10] => isp
                    [11] => isPublicProxy
                    [12] => isResidentialProxy
                    [13] => isSatelliteProvider
                    [14] => isTorExitNode
                    [15] => mobileCountryCode
                    [16] => mobileNetworkCode
                    [17] => network
                    [18] => organization
                    [19] => staticIpScore
                    [20] => userCount
                    [21] => userType
                )

        )

    [city:protected] => GeoIp2\Record\City Object
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            [record:GeoIp2\Record\AbstractRecord:private] => Array
                (
                    [geoname_id] => 4509177
                    [names] => Array
                        (
                            [de] => Columbus
                            [en] => Columbus
                            [es] => Columbus
                            [fr] => Columbus
                            [ja] => コロンバス
                            [pt-BR] => Columbus
                            [ru] => Колумбус
                            [zh-CN] => 哥伦布
                        )

                )

            [locales:GeoIp2\Record\AbstractPlaceRecord:private] => Array
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            [validAttributes:protected] => Array
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        )

    [location:protected] => GeoIp2\Record\Location Object
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            [record:GeoIp2\Record\AbstractRecord:private] => Array
                (
                    [accuracy_radius] => 20
                    [latitude] => 39.9625
                    [longitude] => -83.0061
                    [metro_code] => 535
                    [time_zone] => America/New_York
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            [validAttributes:protected] => Array
                (
                    [0] => averageIncome
                    [1] => accuracyRadius
                    [2] => latitude
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                    [4] => metroCode
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                    [7] => postalConfidence
                    [8] => timeZone
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        )

    [postal:protected] => GeoIp2\Record\Postal Object
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            [record:GeoIp2\Record\AbstractRecord:private] => Array
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                    [code] => 43215
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            [validAttributes:protected] => Array
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        )

    [subdivisions:protected] => Array
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                    [record:GeoIp2\Record\AbstractRecord:private] => Array
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                            [iso_code] => OH
                            [names] => Array
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                                    [de] => Ohio
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                                    [es] => Ohio
                                    [fr] => Ohio
                                    [ja] => オハイオ州
                                    [pt-BR] => Ohio
                                    [ru] => Огайо
                                    [zh-CN] => 俄亥俄州
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                    [locales:GeoIp2\Record\AbstractPlaceRecord:private] => Array
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                    [validAttributes:protected] => Array
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                            [0] => confidence
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)

 country : United States
 city : Columbus
US
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

Learn how Actowiz Solutions scraped SKU-level product data including images, descriptions, and specifications to build accurate, ready-to-list eCommerce datasets.

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Navratri Mega Sale Price Tracking

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

Navratri Mega Sale Price Tracking

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

Navratri Mega Sale Price Tracking

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"

Navratri Mega Sale Price Tracking

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.

From Raw Data to Real-Time Decisions

All in One Pipeline

Scrape → Structure → Analyze → Visualize
Explore Solutions Get a Custom Demo

Look Back Analyze historical data to discover patterns, anomalies, and shifts in customer behavior.

Find Insights Use AI to connect data points and uncover market changes. Meanwhile.

Move Forward Predict demand, price shifts, and future opportunities across geographies.

Ecommerce
Q-Commerce
Travel & OTA
Food Delivery
Real Estate
Social Media
Trusted by Global Leaders – Secured by International Standards

Industry:

Coffee / Beverage / D2C

Result

2x Faster

Smarter product targeting

★★★★★

“Actowiz Solutions has been instrumental in optimizing our data scraping processes. Their services have provided us with valuable insights into our customer preferences, helping us stay ahead of the competition.”

Operations Manager, Beanly Coffee

✓ Competitive insights from multiple platforms

Industry:

Real Estate

Result

2x Faster

Real-time RERA insights for 20+ states

★★★★★

“Actowiz Solutions provided exceptional RERA Website Data Scraping Solution Service across PAN India, ensuring we received accurate and up-to-date real estate data for our analysis.”

Data Analyst, Aditya Birla Group

✓ Boosted data acquisition speed by 3×

Industry:

Organic Grocery / FMCG

Result

Improved

competitive benchmarking

★★★★★

“With Actowiz Solutions' data scraping, we’ve gained a clear edge in tracking product availability and pricing across various platforms. Their service has been a key to improving our market intelligence.”

Product Manager, 24Mantra Organic

✓ Real-time SKU-level tracking

Industry:

Quick Commerce

Result

2x Faster

Inventory Decisions

★★★★★

“Actowiz Solutions has greatly helped us monitor product availability from top three Quick Commerce brands. Their real-time data and accurate insights have streamlined our inventory management and decision-making process. Highly recommended!”

Aarav Shah, Senior Data Analyst, Mensa Brands

✓ 28% product availability accuracy

✓ Reduced OOS by 34% in 3 weeks

Industry:

Quick Commerce

Result

3x Faster

improvement in operational efficiency

★★★★★

“Actowiz Solutions' data scraping services have helped streamline our processes and improve our operational efficiency. Their expertise has provided us with actionable data to enhance our market positioning.”

Business Development Lead,Organic Tattva

✓ Weekly competitor pricing feeds

Industry:

Beverage / D2C

Result

Faster

Trend Detection

★★★★★

“The data scraping services offered by Actowiz Solutions have been crucial in refining our strategies. They have significantly improved our ability to analyze and respond to market trends quickly.”

Marketing Director, Sleepyowl Coffee

Boosted marketing responsiveness

Industry:

Quick Commerce

Result

Enhanced

stock tracking across SKUs

★★★★★

“Actowiz Solutions provided accurate Product Availability and Ranking Data Collection from 3 Quick Commerce Applications, improving our product visibility and stock management.”

Growth Analyst, TheBakersDozen.in

✓ Improved rank visibility of top products

Trusted by Industry Leaders Worldwide

Real results from real businesses using Actowiz Solutions

★★★★★
'Great value for the money. The expertise you get vs. what you pay makes this a no brainer"
Thomas Gallao
Thomas Galido
Co-Founder / Head of Product at Upright Data Inc.
Product Image
2 min
★★★★★
“I strongly recommend Actowiz Solutions for their outstanding web scraping services. Their team delivered impeccable results with a nice price, ensuring data on time.”
Thomas Gallao
Iulen Ibanez
CEO / Datacy.es
Product Image
1 min
★★★★★
“Actowiz Solutions offered exceptional support with transparency and guidance throughout. Anna and Saga made the process easy for a non-technical user like me. Great service, fair pricing highly recommended!”
Thomas Gallao
Febbin Chacko
-Fin, Small Business Owner
Product Image
1 min

See Actowiz in Action – Real-Time Scraping Dashboard + Success Insights

Blinkit (Delhi NCR)

In Stock
₹524

Amazon USA

Price Drop + 12 min
in 6 hrs across Lel.6

Appzon AirPdos Pro

Price
Drop −12 thr

Zepto (Mumbai)

Improved inventory
visibility & planning

Monitor Prices, Availability & Trends -Live Across Regions

Actowiz's real-time scraping dashboard helps you monitor stock levels, delivery times, and price drops across Blinkit, Amazon: Zepto & more.

✔ Scraped Data: Price Insights Top-selling SKUs

Request Demo Access icon

Our Data Drives Impact - Real Client Stories

Blinkit | India (Retail Partner)

"Actowiz's helped us reduce out of stock incidents by 23% within 6 weeks"

✔ Scraped Data, SKU availability, delivery time

US Electronics Seller (Amazon - Walmart)

With hourly price monitoring, we aligned promotions with competitors, drove 17%

✔ Scraped Data, SKU availability, delivery time

Zepto Q Commerce Brand

"Actowiz's helped us reduce out of stock incidents by 23% within 6 weeks"

✔ Scraped Data, SKU availability, delivery time

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Score big this Navratri 2025! Discover the top 5 brands offering the biggest clothing discounts and grab stylish festive outfits at unbeatable prices.

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Top 10 Most Ordered Grocery Items During Navratri 2025

Discover the top 10 most ordered grocery items during Navratri 2025. Explore popular festive essentials for fasting, cooking, and celebrations.

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UK Food Aggregator Pricing Scraping Reveals Competitive Pricing Trends Across Deliveroo, Just Eat, and Uber Eats

This research report uses UK Food Aggregator Pricing Scraping to reveal competitive pricing trends across Deliveroo, Just Eat, and Uber Eats
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KEETA Menu Data Extraction Reveals High-Demand Dishes and Peak Hours Across Saudi Arabia

This research report uses KEETA Menu Data Extraction to reveal high-demand dishes and peak ordering hours across Saudi Arabia.
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Price Matching & Availability Analysis for Lidl in the UK Retail Market

Discover key insights in the UK retail market with our Research Report – Price Matching & Availability Analysis for Lidl, tracking pricing trends and stock availability.
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