Improving Product Categorization for an E-Commerce Giant

The Challenge

A prominent e-commerce platform experiencing rapid growth was struggling with one of the most foundational aspects of their customer experience—accurate product categorization.

• Over 1 million SKUs were live on the platform, but the categorization was riddled with inconsistencies.

• Misclassified products led to irrelevant search results, broken filters, and frustrated customers who couldn't find what they were looking for.

• Return rates surged, and vendors complained about reduced visibility for correctly tagged listings.

• Their internal teams were overwhelmed. Manual tagging was not only slow, but unsustainable at scale, and existing automation lacked precision.

They needed a partner who could bring accuracy, scalability, and domain understanding—fast.

Our Solution: Human-Centric Intelligence at Scale

LabelCo.Ai deployed a robust Human-in-the-Loop product annotation system tailored to the platform’s complexity. The approach combined domain-trained annotators, deep taxonomy logic, and rigorous quality control.

1. Custom Taxonomy Development

We first redesigned their category hierarchy, creating a granular and intuitive taxonomy based on:

• Market benchmarks from leading global marketplaces

• Historical customer behavior and search term analytics

• Retail-specific best practices (e.g., gendered categories, nested filters)

2. End-to-End Product Tagging

A team of over 100 trained annotators manually labeled:

• Titles, descriptions, and visual cues from images

• Technical specs like size, material, usage, target audience, and style Each SKU was mapped to the most accurate category, with all relevant attributes tagged.

3. Attribute-Level Annotation

For each product, we extracted:

• Color, size, and material

• Gender (men, women, unisex, kids)

• Seasonal relevance (e.g., winter jackets, monsoon gear)

• Style and usage context (casual, formal, activewear)

4. Quality Assurance Built-In

To ensure precision and reliability:

• A multi-tier review system was implemented

• Rule-based validations flagged anomalies in real-time

• 5% of annotations were sampled daily by QA leads for manual cross-checks

The Impact

Within 45 days, LabelCo.Ai delivered:

• Complete tagging of over 1 million SKUs

• 28% increase in successful product search and filter accuracy

• Return rate reduced by 19% on misclassified products

• Improved AI training data, enabling their automation tools to better self-learn

• Vendor satisfaction scores improved due to fair visibility and tagging parity

The project didn't just solve a categorization problem—it redefined their digital shelf, improved discoverability, and made shopping seamless for millions of users.