Case Study Detail

E-commerce Search Optimization

A major online retailer was struggling with poor search conversion rates. Users frequently searched for products but could not find what they were looking for, leading to high bounce rates and lost revenue. Their existing search relied on basic keyword matching with manual boost rules that had grown unwieldy over time.

Cognitivi conducted a comprehensive search relevance audit, analyzing query logs, click-through data, and zero-result rates. We identified key areas for improvement including synonym handling, query classification, and result ranking.

We implemented a Learning to Rank model trained on historical click and conversion data, integrated semantic search using vector embeddings for long-tail queries, and built a continuous evaluation pipeline using NDCG and MRR metrics. The result was a 35% increase in search-driven conversions and a 50% reduction in zero-result queries within the first quarter.