personalization-engine

To generate personalized product recommendations, the system analyzes shopper browsing history and purchase patterns using collaborative filtering and content-based signals to tailor suggestions across PDP, homepage, and cart contexts.

Yes, you can deploy personalized product recommendations across Shopify, WooCommerce, BigCommerce, or custom headless storefronts using either an app-based ML solution or a custom algorithm.

Cold-start scenarios for product recommendations are handled by applying content-based signals and default logic to serve relevant items to new shoppers before sufficient browsing history accumulates.

You exclude out-of-stock and recently purchased items from recommendations by configuring the custom algorithm or ML solution logic to automatically filter these products based on inventory and user purchase data.

You measure CTR and revenue impact by integrating the provided API, which tracks shopper interactions and purchase events tied to the personalized product recommendations served across storefront placements.

The best way to A/B test personalized carousels is to deploy the recommendation engine across specific homepage and PDP placements, then compare CTR and revenue metrics via the integrated API.