E-commerce Product Matching
Prudent Partners performed visual product matching and attribute tagging to help an e-commerce client consolidate duplicate listings and improve catalog consistency. The service enhanced search accuracy and customer experience across platforms.
Case Details
Clients: Pixel Art Company
Start Day: 13/01/2024
Tags: Marketing, Business
Project Duration: 9 Month
Client Website: Pixelartteams.com
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Executive Summary
This project focused on improving product matching accuracy across major e-commerce platforms, including Flipkart and Myntra. By deploying refined annotation guidelines, the team ensured precise classification of product attributes across categories such as Books, CDs, Clothing, Automotive components, and Jewellery. The annotated dataset supports machine learning systems designed to flag mismatches and automate catalog management efficiently.
Introduction
Background
Annotation involved reviewing and matching product listings across different platforms based on key attributes. The scope included identifying correct matches, flagging incorrect or partial matches, and ensuring alignment with category-specific attribute rules.
Industry
E-commerce / Product Data Management
Products & Services
The team at Prudent Partners LLP implemented category-wise SOPs to drive accurate product matching for:
- Books
- CDs & DVDs
- Frames
- Clothing/Shoes
- Automotive
- Jewellery accessories
This approach supports clients leveraging external partners for manual validation when AI solutions fall short or need curated datasets for model fine-tuning.
Challenge
Problem Statement
- Variations in attributes (color, design, size) and inconsistent platform data hinder accurate product matching.
- Lack of uniform attribute representation (missing dimensions, ambiguous sizes) leads to mismatches or “Not Sure” labels.
- Subjectivity in visual cues like frame size or clothing design introduces annotation inconsistencies.
Impact
Inaccurate product matches result in poor user experience, higher return rates, and catalog inefficiencies.
Solution
Overview
Structured SOPs per category were implemented, highlighting key attributes and edge cases. Annotators followed these SOPs to standardize decisions, reducing ambiguity and increasing label consistency.
Implementation
- Conducted Knowledge Transfer (KT) sessions to familiarize annotators with category-specific rules.
- Built an internal validation mechanism using “Evaluation Scenarios” to resolve edge cases.
- Leveraged brand websites, size charts, ISBN lookup, and UPC verification to ensure accuracy.
Products/Services Used
- Internal SOP guidelines
- Brand websites, ISBN/UPC validators
- Cross-checking product detail pages (PDPs)
Results
Outcome
- Significant improvement in product match annotation quality.
- Higher consistency across annotators, especially for nuanced categories like Jewellery and Automotive.
- Clear reduction in “Not Sure” labels due to SOP clarity.
Benefits
- Reduced client review time with higher confidence in annotation quality.
- Improved catalog hygiene, leading to better conversion rates and fewer customer complaints.
Testimonial
“The SOP-backed annotation quality has made our product validation process much smoother. The clarity with which mismatches are flagged has saved our team countless hours.” — Client Review Team Lead
Conclusion
Summary
Implementing structured SOPs and annotation standards enabled consistent, scalable product matching. This improved internal QA efficiency and strengthened the foundation for AI model training in e-commerce categorization.
Future Plans
- Introduce AI-assisted suggestion tools for initial match predictions.
- Expand SOPs to new categories like electronics and home decor.
- Build a client feedback loop to continuously evolve SOPs based on real-world product complexities.
Call to Action
Organizations seeking high-accuracy product categorization workflows can contact our annotation services team for a customized plan.