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Unlock higher order values and deeper customer engagement with smart, AI-driven product recommendations.
Virto’s product recommendation module delivers intelligent, context-aware product suggestions using a combination of ElasticSearch and Machine Learning (ML). It supports both AI-driven and rule-based recommendations, offering flexibility for a wide range of B2B, B2C, and D2C ecommerce scenarios.
Enhance product catalog exploration by suggesting similar, complementary, recently browsed, or frequently bought together items. Easily control how many product recommendations appear on the page to optimize user experience.
The AI system is powered by the Elastic App Search and analyzes customer behaviour, browsing patterns, purchase history, contextual factors (current page content, time of day, season, customer role, customer industry, search queries), product characteristics (category, brand, price range, tech specs), real-time inventory, trending items, and business rules (promotion, exclusion, or prioritization of products) to provide unique suggestions.
Supports use cases like related products, collaborative filtering, and cross-sell models.
Uses semantic search to understand product context, not just keywords.
Allows for easy configurability and extensibility with custom logic.
Integrates with any frontend via GraphQL or xAPI.
Elevated shopping experience: Customers quickly find what they need through deeply personalized and context-aware suggestions, making their shopping experience smoother and more enjoyable.
Continuous improvement of suggestions: Product recommendations improve over time through ML algorithms and A/B testing capabilities for swift POCs.
Deep integration with product catalog: Ensures up-to-date product data and respect for B2B rules (contract-specific catalogs or pricing, role-based purchasing permissions).
Better inventory turnover: Dynamic ecommerce product recommendations introduce customers to new and less-visible products, helping circulate inventory and reduce slow-moving stock.
Build lasting loyalty by anticipating what your B2B customers want next.
Virto Commerce analyzes purchase history and preferences to deliver personalized product recommendations, enhancing the shopping experience while driving sales and increasing average order value. Learn more in our user guide.
AI-driven product recommendations are personalized suggestions offered to customers via Virto’s ecommerce platform, powered by ElasticSearch and Machine Learning. Recommendations include similar, complementary, recently browsed, or frequently bought together items.
Smart ecommerce product recommendation engine aligns suggestions to customer’s preferences, purchasing history, and buying habits, encouraging users to add more products to cart, completing the purchase with higher value.
Yes, Virto’s AI-driven ecommerce product recommendations personalize product suggestions based on customer behavior and B2B rules. Among considered factors are browsing history, purchase patterns, customer role, contract-specific pricing, and real-time inventory.
Yes, Virto’s AI-powered product recommendations stay relevant and improve with time. ML and A/B testing continuously refine suggestions to provide most relevant suggestions according to user preferences.
Yes, you can choose to integrate via xAPI or GraphQL and run a smooth integration to add AI suggestions to an existing storefront.