Netcore Unbxd Personalization is an AI-powered engine that tailors search results, product listings, and recommendations to each shopper. It uses more than 200 behavioral, catalog, query, and contextual signals in real time to understand intent and surface products each shopper is most likely to buy across search, browse, and recommendation widgets.

The personalization engine tracks in-session behavior such as queries, product views, category browsing, time on page, scroll depth, filter usage, add-to-cart actions, and cart contents. As the shopper continues through the session, the system dynamically reorders products and updates recommendations so every new click reflects the latest intent signals.

Netcore Unbxd combines multiple signal families, including clickstream data like impressions, clicks, carts, purchases, and revenue, catalog signals like price, margins, brands, categories, and ratings, query features like search type and popularity, and contextual data such as device, location, time of day, weather, and season. This broad signal set helps the engine balance shopper preferences with business goals like margin, inventory, and promotion priorities.

For new or anonymous visitors with little or no history, the platform uses segment-based and context-based models. It draws on patterns from similar shopper cohorts, geo-level preferences, device-level behavior, and time-based context to make smart default choices. As the shopper browses and clicks, the engine quickly shifts from cohort-level patterns to true 1:1 personalization within the same session.

Personalization can be applied across the full journey, including search result pages, category and listing pages, product detail pages, cart and checkout, and post-purchase experiences. It also powers personalized widgets on the home page, in email campaigns, and across other digital touchpoints so shoppers see consistent, intent-aware experiences wherever they interact with the brand.

Business and merchandising teams use a visual console to configure personalization rules, define segments, and plug in business logic such as diversity, newness, or starving-product uplift. A testing framework with A/B experiments and multi-arm bandit models automatically favors winning models and strategies based on conversion or engagement metrics while still giving teams full visibility and control.

Retailers typically see higher conversion rates, average order value, and revenue from sessions that touch search or recommendations. When basic or rules-only setups are replaced with Netcore Unbxd’s real-time 1:1 personalization across search, browse, and recommendations, brands often see double-digit lifts in key ecommerce metrics.