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Retail

Enhancing Retail Efficiency with Automated Product Matching

An omnichannel US retailer turned to Dataseeders' AI-driven product matching engine to unify a sprawling catalog reaching 96% matching accuracy across 100,000+ SKUs.

Client: Omnichannel Retail Company
96%
Product Matching Accuracy
83%
Reduction in Duplicate Listings
150+
Hours Saved per Month

Client Overview

Client is USA based eminent omnichannel retail company having a strong presence in both local stores and online marketplaces. Their catalog covers consumer goods, accessories, sports equipment, and home essentials. They sell products on their own website, third-party platforms, and vendor portals.

Client Requirements

The client approached Techdataseeders with a need to:

  • Match identical and near-identical products listed across various marketplaces and internal catalogs.
  • Combine product listings to evade duplication and confusion.
  • Improve search accuracy and product recommendation on their platform.
  • Gain a competitive edge through faster and more precise product comparisons.
  • Improve vendor onboarding and catalog management with nominal manual involvement.

Challenges

Despite being a known brand, the client was struggling with:

  • Duplicate product entries which are making difficult for consumers to compare or find the right product.
  • Uneven product naming conventions and missing metadata, which hindered automated catalog syncing.
  • Time consuming manual matching results in human error and inadequacy.
  • Inaccurate competitor mapping, limiting pricing intelligence and assortment decisions.
  • Poor product discoverability, especially for similar or substitute items.

Techdataseeders Solutions

We implemented a tailored Product Matching Solution built with AI and NLP-based models to resolve their pain points:

Automated Matching Engine

Detected exact and approximate matches using product titles, attributes (brand, size, SKU), pricing, and visuals.

Machine Learning Models

Skilled over the client catalog to improve matching precision over time using similarity scoring, fuzzy logic, and attribute mining.

Centralized Product Repository

Formed a single source of fact for product data across all channels.

Visual Recognition Module

Integrated image matching algorithms to compare product images and detect duplicates even with different branding.

API Integration

Connected with their ERP, PIM, and eCommerce CMS to ensure continuous product synchronization and real-time updates.

Benefits to the Client

The process transformation was both tactical and operational which provided measurable benefits to their business.

  • More than 96% product matching accuracy acquired across over 100,000 SKUs.
  • Reduced duplicate listings by 83% which results in improved catalog quality and search results.
  • Improved product recommendations led to a 21% increase in upsell and cross-sell conversions.
  • Enhanced competitive benchmarking assisted them respond faster to market trends.
  • Saved 150+ hours/month by abolishing manual product comparison tasks.
  • Boosted customer satisfaction with easy product search and cleaner navigation.

Conclusion

With our automated product matching capabilities, our retail client transmuted their uneven catalog into a cohesive, high performing product ecology. The result was a significant boost in operational proficiency, product visibility, and sales performance, all driven by intelligent data alignment.

Product MatchingRetailAI & NLPCatalog Management