Shoppers who use site search are telling you exactly what they want. When search returns nothing, the wrong product or a page of sold-out items, they leave. Collection pages have the same problem in a quieter form. Sort orders bury your best sellers, filters do not match how people shop and new arrivals never get a turn at the top.
For brands with $10 million to $350 million in annual revenue, the catalog has usually outgrown the default settings. Search and merchandising work fixes relevance first, then gives merchandisers the controls to manage ranking without a developer.
What we work on
- Relevance. Searchable attributes, ranking rules, synonyms and typo tolerance set up for your catalog and the words customers actually use.
- Zero-result searches. A regular review of what shoppers search for and fail to find, fixed through synonyms, redirects or better product data.
- Filters and facets. Filters built from the attributes that matter in each category, such as fit, size, compatibility or material.
- Merchandising rules. Pinning, boosting and burying products on search and collection pages, with scheduled rules for campaigns.
- B2B search. SKU and part number lookup, customer-specific catalogs and quick ordering for trade buyers.
Native search or a dedicated tool
Native platform search has improved, and for a modest catalog it is often enough. Large catalogs, B2B buyers and headless storefronts usually need more control. We implement Algolia for brands that need fast, configurable search and merchandising across storefronts, and we work with other search tools where they are already in place.
Product data comes first
Search can only rank what the data describes. A surprising share of relevance problems turn out to be missing attributes, inconsistent naming or variants set up as separate products. We fix the data issues that block search alongside the configuration, and where the catalog is large, AI product data enrichment can fill gaps at scale.
Search and merchandising is part of our conversion rate optimization service. Ranking and layout changes can be validated through A/B testing programs, and personalization builds on the same foundation through AI merchandising and personalization.