Netcore Unbxd Autosuggest is an AI-powered typeahead and query suggestion system that guides shoppers from the search bar to the right products faster. It predicts what users are trying to search for, corrects misspellings, and recommends popular queries, categories, and products in real time to increase engagement and conversion.

Netcore Unbxd Autosuggest uses shopper behavior signals such as search volume, click-throughs, and conversions to prioritize high-performing suggestions. It corrects spelling errors, surfaces meaningful alternatives, and highlights top and trending queries so shoppers pick viable search paths instead of triggering zero-result pages.

Netcore Unbxd Autosuggest can show keyword suggestions, in-field category or attribute-based suggestions, top and trending queries, and fast-selling products directly in the dropdown. Retailers can also surface content, such as guides or landing pages, when that helps answer the shopper’s intent better than a simple product list.

Merchandisers use a visual workbench to customize Autosuggest without engineering support. They can promote or block specific suggestions, define rules for campaigns, adjust how different suggestion types appear, and align typeahead experiences with business goals such as margin, inventory, or strategic categories.

Autosuggest ranking is driven by performance metrics such as hits, click-through rate, order count, and order value. The system continuously learns from real shopper interactions and automatically reorders suggestions so high-intent, high-conversion queries and products appear more prominently at the top.

Netcore Unbxd provides dynamic templates with ready-made layouts that speed up implementation and keep experiences consistent. Teams can choose from pre-designed patterns or use the template builder to create custom layouts that combine queries, categories, and product tiles in a way that fits their brand and UX guidelines.

Retailers get detailed Autosuggest analytics, including conversion funnel reports that show how often suggestions are viewed, clicked, and converted. Algorithm-level reports highlight which Autosuggest strategies perform best, making it easier to double down on what works and refine or retire underperforming configurations.