What Semantic Search Actually Does: A Guide for Ecommerce Teams

Semantic search in ecommerce utilizes an AI model specifically trained on a retailer's catalog to interpret purchase intent, rather than merely matching text, thereby addressing the limitations of traditional keyword search systems like BM25. This approach allows for a more nuanced understanding of queries, such as "something cozy for winter," by mapping their meaning to relevant products even when no direct word matches exist. Marqo, an AI company, develops a singular, comprehensive model that incorporates product data, including images, titles, descriptions, and behavioral signals, overcoming the cold start problem for new products and resulting in significant conversion rate improvements and revenue increases, with some clients reporting up to $130 million in additional revenue. The model trains quickly, typically within two weeks, and includes a guarantee of at least a 3% revenue uplift, offering a penalty-free exit if this is not achieved.

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