High-Quality Labels Power Accurate Search for 385 Million Online Shoppers
The Client
A leading global e-commerce retailer with over 385 million monthly online visitors offers millions of products across dozens of categories. Maintaining a massive online catalog while ensuring exceptional search experiences is central to their business model. When they set out to improve site search relevance and accommodate their rapidly expanding product selection, they sought a dedicated labeling partner capable of producing high-quality datasets to train and improve their search algorithms.
Key Challenges
Inconsistent Labeling Impacting Search Accuracy: Poorly labeled products led to irrelevant search results, frustrating users and contributing to site abandonment, directly affecting conversion rates and revenue.
Time-Intensive and Specialized Labeling Needs: Each SKU required complex classification, description validation, image verification, and attribute tagging, but existing processes were slow and lacked consistency.
Struggles with Scaling Efficiently: Seasonal surges and a continuously expanding catalog overwhelmed previous solutions, causing backlogs that delayed product listings and slowed time-to-market.
The Solution
To improve accuracy, efficiency, and scalability, Hugo implemented a structured approach combining domain expertise, quality control, and adaptive resourcing.
Domain-Specialized Annotation Teams
Hugo built a team of annotators with retail and merchandising expertise across diverse product categories, from electronics to fashion to eliminate inconsistencies and improve search accuracy.
Specialized sub-teams focused on specific product types, developing deep domain knowledge to ensure precise labeling.
Additionally, Hugo implemented comprehensive training programs aligned with the Client’s taxonomy, brand guidelines, and quality standards. This ensured products were accurately categorized, improving search relevance and reducing customer frustration.
Multi-Dimensional Quality Assurance Framework
To enhance labeling accuracy and streamline SKU processing, Hugo implemented a structured quality control system.
The process included a three-tier review—initial annotation, peer review, and final verification—to ensure consistency.
Automated checks flagged inconsistencies, while blind sampling tests validated accuracy across annotators.
Weekly calibration sessions refined classifications, and real-time integration with the Client’s feedback system allowed immediate adjustments.
These measures accelerated product processing while maintaining high-quality data, ultimately reducing errors that impacted search accuracy and conversion rates.
Adaptive Scaling Methodology
Hugo developed a flexible resourcing model that could scale operations without causing backlogs. A core team of annotators ensured continuity, while a trained reserve team expanded capacity when needed.
An advanced allocation system prioritized time-sensitive products, ensuring critical listings went live without delay. Predictive capacity planning, based on historical trends and upcoming launches, helped anticipate demand spikes and prevent slowdowns. This approach reduced listing delays, improved time-to-market, and ensured consistent data quality even during peak periods.
The Results
Expanded Catalog Coverage: Processed 2.5 million items, increasing retail item coverage from 91% to 98% for better searchability.
Improved Search & Discovery: Enhanced labeling accuracy cut product search time by 23%, boosting customer satisfaction and conversions.
Lower Search Abandonment: Reduced bounce rates by 8%, retaining millions of customers.
Enriched Product Taxonomy: Added 500+ new category tags, refining classification and navigation.
Scalable, High-Quality Labeling: Maintained 97.8% accuracy even during peak periods, ensuring seamless scaling.
Lasting Impact
Hugo’s expert-driven approach delivered tangible results—improving customer experience, boosting conversions, and driving revenue growth. "Quality is important to them. They feel like a true partner, not just a vendor," said Aaron T., Product Manager.
