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GeoIp2\Model\City Object
(
    [raw:protected] => Array
        (
            [city] => Array
                (
                    [geoname_id] => 4509177
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                            [ja] => コロンバス
                            [pt-BR] => Columbus
                            [ru] => Колумбус
                            [zh-CN] => 哥伦布
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                            [fr] => États Unis
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                            [pt-BR] => EUA
                            [ru] => США
                            [zh-CN] => 美国
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                                    [ru] => Огайо
                                    [zh-CN] => 俄亥俄州
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            [traits] => Array
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    [continent:protected] => GeoIp2\Record\Continent Object
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                    [names] => Array
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                            [de] => Nordamerika
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                            [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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                    [iso_code] => US
                    [names] => Array
                        (
                            [de] => USA
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                            [es] => Estados Unidos
                            [fr] => États Unis
                            [ja] => アメリカ
                            [pt-BR] => EUA
                            [ru] => США
                            [zh-CN] => 美国
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            [locales:GeoIp2\Record\AbstractPlaceRecord:private] => Array
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            [validAttributes:protected] => Array
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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
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    [representedCountry:protected] => GeoIp2\Record\RepresentedCountry Object
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            [record:GeoIp2\Record\AbstractRecord:private] => Array
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            [locales:GeoIp2\Record\AbstractPlaceRecord:private] => Array
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    [traits:protected] => GeoIp2\Record\Traits Object
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                    [network] => 216.73.216.0/22
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            [validAttributes:protected] => Array
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                    [2] => connectionType
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                    [8] => isHostingProvider
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                    [14] => isTorExitNode
                    [15] => mobileCountryCode
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                    [17] => network
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                    [19] => staticIpScore
                    [20] => userCount
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        )

    [city:protected] => GeoIp2\Record\City Object
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            [record:GeoIp2\Record\AbstractRecord:private] => Array
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                    [geoname_id] => 4509177
                    [names] => Array
                        (
                            [de] => Columbus
                            [en] => Columbus
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                            [fr] => Columbus
                            [ja] => コロンバス
                            [pt-BR] => Columbus
                            [ru] => Колумбус
                            [zh-CN] => 哥伦布
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    [location:protected] => GeoIp2\Record\Location Object
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                    [latitude] => 39.9625
                    [longitude] => -83.0061
                    [metro_code] => 535
                    [time_zone] => America/New_York
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            [validAttributes:protected] => Array
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                    [1] => accuracyRadius
                    [2] => latitude
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                    [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
                (
                    [0] => code
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        )

    [subdivisions:protected] => Array
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            [0] => GeoIp2\Record\Subdivision Object
                (
                    [record:GeoIp2\Record\AbstractRecord:private] => Array
                        (
                            [geoname_id] => 5165418
                            [iso_code] => OH
                            [names] => Array
                                (
                                    [de] => Ohio
                                    [en] => Ohio
                                    [es] => Ohio
                                    [fr] => Ohio
                                    [ja] => オハイオ州
                                    [pt-BR] => Ohio
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)
 country : United States
 city : Columbus
US
Array
(
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    [asn] => AS16509
    [continent] => North America
    [continent_code] => NA
    [country] => United States
    [country_code] => US
)
Navratri Mega Sale Price Tracking

Introduction

In the fast-growing Indian e-commerce ecosystem, maintaining a clean and accurate product catalog is essential for customer trust and operational efficiency. Our client, a large multi-category online marketplace, was struggling with catalog inconsistencies, repetitive product listings, and mismatched SKUs across platforms. These issues impacted search visibility, conversions, and seller performance. To address these challenges, Actowiz Solutions implemented a scalable data intelligence framework centered around Catalog & Duplicate Listing Detection.

With millions of products updated daily, the client needed an automated solution that could identify duplicates, standardize listings, and ensure accurate product matching across marketplaces. By leveraging advanced scraping logic, machine learning-based matching, and real-time monitoring, Actowiz Solutions helped the brand gain full control over its catalog ecosystem. This case study highlights how our data-driven approach improved accuracy, reduced redundancy, and enhanced the overall shopping experience.

About the Client

Navratri Mega Sale Price Tracking

The client is a leading Indian e-commerce brand operating in fashion, home essentials, electronics, and lifestyle categories. Their platform serves millions of users across tier-1 and tier-2 cities, with a strong focus on value-driven customers and high seller participation. The business relies heavily on third-party sellers, which leads to frequent catalog duplication and inconsistent product data across marketplaces like Meesho and Snapdeal.

To scale efficiently, the client required continuous visibility into external listings and competitive catalogs. Actowiz Solutions supported this requirement using Meesho & Snapdeal Catalog Scraper, enabling the client to extract structured product data, monitor seller listings, and maintain catalog hygiene. The goal was to ensure accurate product representation, eliminate duplicate entries, and improve product discoverability while supporting rapid marketplace expansion.

Challenges & Objectives

Challenges
  • Duplicate Listings Proliferation: Multiple sellers uploaded identical products with varying titles, images, and attributes, confusing customers.
  • Catalog Inconsistency: Product specifications differed across platforms, impacting trust and returns.
  • Manual Monitoring Limitations: Internal teams could not manually compare thousands of listings daily.
  • Competitive Blind Spots: Lack of cross-platform visibility reduced pricing and assortment intelligence.
Objectives
  • Automated Comparison: Implement Meesho & Snapdeal Listing Comparison Scraper to detect identical products across platforms.
  • Catalog Accuracy: Standardize titles, descriptions, and attributes for improved SEO and UX.
  • Operational Efficiency: Reduce manual workload with automated detection workflows.
  • Scalable Intelligence: Enable real-time monitoring as product volumes grew.

Our Strategic Approach

Intelligent Data Collection Framework

Actowiz Solutions designed a robust data pipeline focused on Scraping duplicate listings on Meesho using advanced crawlers. We extracted titles, images, prices, seller IDs, and attributes at scale while maintaining data accuracy and compliance. The system normalized data fields to prepare them for effective comparison.

Smart Matching & Classification

We applied AI-assisted matching algorithms to identify near-duplicate listings, even when product titles or images varied. Fuzzy matching, image hash comparison, and attribute-level scoring ensured high accuracy. This approach allowed the client to proactively manage catalog quality and prevent duplicate uploads before they impacted performance.

Technical Roadblocks

Platform-Level Anti-Scraping

Marketplaces frequently updated layouts and bot-detection mechanisms. Our team adapted scraping logic dynamically to ensure uninterrupted Snapdeal duplicate product scraping.

Data Volume & Velocity

Handling millions of SKUs required scalable infrastructure. We optimized crawl frequency and distributed processing to ensure real-time insights without data loss.

Product Variability

Different sellers used inconsistent naming conventions and imagery. We solved this by implementing attribute-weighted similarity models, improving duplicate detection accuracy across categories.

Our Solutions

Actowiz Solutions delivered a centralized, automated solution powered by Duplicate Product Listing Detection API. This API continuously scanned Meesho and Snapdeal catalogs, identified duplicate SKUs, and flagged inconsistencies in titles, images, and specifications. The solution integrated seamlessly with the client’s internal systems, enabling real-time alerts and actionable dashboards.

Our API-based architecture allowed flexible scaling as product volumes increased. The client could filter duplicates by category, seller, or similarity score, enabling targeted catalog cleanups. With automated workflows replacing manual checks, the brand significantly improved catalog hygiene, reduced operational overhead, and enhanced customer experience.

Results & Key Metrics

Key Outcomes
  • 38% reduction in duplicate listings within 3 months
  • 27% improvement in catalog accuracy scores
  • 22% increase in product discoverability
  • 30% drop in customer complaints related to incorrect listings
Data Intelligence Impact

Using Meesho & Snapdeal product matching data extraction and advanced Product Matching, the client gained a unified view of cross-platform listings. This enabled smarter pricing strategies, better seller governance, and improved marketplace credibility. The automated detection system ensured long-term scalability and sustained catalog quality.

Client Feedback

“Actowiz Solutions transformed how we manage our catalog. Their duplicate detection framework helped us clean millions of listings efficiently and improved our marketplace credibility.”

— Head of Marketplace Operations, Leading E-commerce Brand

Why Partner with Actowiz Solutions?

  • Proven Expertise: Years of experience in Web scraping Ecommerce Data at scale
  • Advanced Technology: AI-driven matching and real-time APIs
  • Customization: Tailored solutions for complex marketplace ecosystems
  • Scalable Infrastructure: Handles millions of SKUs seamlessly
  • Dedicated Support: Continuous monitoring and optimization

Actowiz Solutions empowers e-commerce brands with reliable, actionable data intelligence that drives measurable growth.

Conclusion

This case study demonstrates how Actowiz Solutions helped a leading e-commerce brand regain control over its catalog using Web scraping API, Custom Datasets, and instant data scraper solutions. By automating duplicate detection and product matching, the client achieved higher accuracy, better customer trust, and operational efficiency.

Ready to clean and optimize your e-commerce catalog? Partner with Actowiz Solutions today.

FAQs

1. What is catalog and duplicate listing detection?

It is the process of identifying identical or highly similar product listings across marketplaces to maintain catalog accuracy and prevent redundancy.

2. How does Actowiz detect duplicate listings?

We use a combination of web scraping, AI-based similarity scoring, image comparison, and attribute matching.

3. Can this solution scale for large marketplaces?

Yes, our infrastructure is designed to handle millions of SKUs with real-time updates.

4. Is the data extraction compliant?

We follow ethical scraping practices and customize solutions based on client compliance requirements.

5. Which industries benefit most from this solution?

E-commerce marketplaces, retail aggregators, brands, and sellers managing multi-platform catalogs benefit the most.

From Raw Data to Real-Time Decisions

All in One Pipeline

Scrape Structure Analyze Visualize

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.

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

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

Actowiz Insights Hub

Actionable Blogs, Real Case Studies, and Visual Data Stories -All in One Place

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