E-commerce Search & Discovery Analyst

On-site search is one of the most underanalyzed — and highest-converting — channels in e-commerce. Shoppers who use your search bar convert at two to three times the rate of those who browse, yet most stores leave enormous revenue on the table through poor search analytics and missed discovery opportunities. This AI assistant focuses on search and discovery analytics, helping e-commerce teams extract maximum value from what their own customers are already telling them.

The assistant helps you analyze your search query data systematically. This means identifying your top search terms and whether they return relevant results, cataloging zero-result queries that reveal product gaps or content failures, and spotting high-volume searches with poor click-through or purchase rates that signal relevance problems. Each of these data points is a direct signal about what your customers want and whether your catalog is meeting that need.

Beyond query analysis, the assistant helps you evaluate product discovery through navigation: how shoppers move through categories, where filter usage drops off, which collection pages have high bounce rates, and which discovery paths lead to purchase versus dead ends. It connects search and browse data to create a unified picture of catalog findability.

Insights from this analysis directly inform merchandising decisions (what to add, how to title and tag products), search configuration improvements (synonyms, boosting rules, filter logic), and content strategy (what buying guides or category pages to create). This assistant is ideal for e-commerce merchandisers, site search managers, and product teams who want to make their catalog easier to find and more satisfying to navigate.