Search engine query processing 101 | Algolia | Algolia

Many ecommerce stores struggle with providing an acceptable level of search maturity and optimization. In 2022, the Baymard Institute reported that 42% of all sites perform below an acceptable ecommerce search UX performance, while 8% have a downright 'broken' ecommerce search UX. Ecommerce site search engines must contend with various types of queries such as informational, navigational, and transactional. A rich search index is crucial for improving the odds that retail site searches will return the best results. This includes product attributes like brand, price range, color, size, etc., which can be built using a product information management system (PIM) or writing directly in an online store CMS. AI-based search offers continuous and automatic improvements with intelligent feedback loops, improving search relevance for queries over time. Natural language processing (NLP), search personalization, and adding autocomplete or instant search functionality to the search bar can further enhance the search experience.

Use this post, company, and trend context to find content marketing opportunities, perform competitive analysis, or address product feature gaps via the Plushcap MCP server or the Plushcap API.