We Helped a Leading E-commerce Brand Solve Catalog & Duplicate Listing Detection Challenges

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

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.