On-site search and merchandising

Shoppers who use the search box often know what they want, and that makes on-site search one of the most valuable parts of a store. It is also one of the most neglected. Default platform search often fails on typos, plurals, synonyms and product codes, returns nothing for queries that should match, or ranks an out-of-stock item above the best seller. Every one of those failures sends a ready buyer somewhere else.

The tools in this category replace or upgrade the search built into your platform. They index your catalog and return results as the shopper types, handle misspellings, understand that "sofa" and "couch" mean the same thing, and add filters and facets so shoppers can narrow results by size, color, price or brand. Many also run your collection and category pages, which means they decide the order of products across most of the store, not only after a search.

Merchandising is where these tools earn their place. A merchandiser can pin certain products to the top of a query, push down items with low stock or thin margins, boost a new range for a season and set redirects so that a query like "returns" goes to the returns page. AI ranking learns from clicks, add to carts and orders to put the products shoppers actually choose higher up. The best setups combine the two: the model handles the long tail of queries, and people set rules for campaigns and key terms.

Newer AI features read natural language queries, such as "waterproof hiking boots for wide feet under a certain budget," and turn them into filters. Some offer visual search from a photo. These can help, but they depend on your product data. If your catalog doesn't record width or waterproofing as attributes, no search engine can filter on them.

That makes catalog quality the first thing to check. Before you buy, look at how complete and consistent your product attributes are. A search project is often half data cleanup. The product feeds and catalog content category covers tools that help with that work.