Two products that are semantically similar may have dissimilar performance. Why does product A sell more often than product B? By comparing the KPIs of semantically similar products, merchandisers can dig into the data to gain insight into varying performance and make informed decisions. Perhaps product B is consistently shown below the fold, or product B is priced too high.
Type-ahead suggestions are traditionally based on matching partial or complete query terms to popular searches, categories, and products. Use Semantic Vector Search to locate high performing queries and products that are nearby in the shared vector space. This powers a higher-converting experience that goes beyond understanding intent.
Semantic Vector Search can be combined with traditional filtering to find semantically similar products with specific features, such as a specific price range or high popularity and low return rate. Use this to power better recommendations and increase engagement.