AI-Driven & Rule-Based Product Recommendations

Consumer recommendation logic—customers also bought, trending now, you might like— doesn't translate to B2B procurement. Buyers want what's on their approved vendor list, at their negotiated price, what they can actually order—not popular products.

Virto's AI Product Recommendations are built around that reality: recommendations are filtered by contract pricing, approved assortments, and account-level catalog access before they surface—so buyers only see what they can actually purchase, at the price their agreement reflects. Cross-sell and upsell logic operates within the procurement relationship, not against it.

The module supports both AI-driven and rule-based approaches, suggesting similar, complementary, recently browsed, or frequently bought together items—with full control over how many recommendations appear and where. Built on Virto's API-first, model-agnostic architecture, the recommendations engine connects to external AI models and custom signals to go beyond what generic algorithms provide.

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. 

• Contract-aware filtering by contract pricing, approved vendor lists, and account-level catalog access—buyers only see what they can purchase at their actual price.

• Extensible recommendation logic connects to external AI models and custom signals via Virto's API-first, model-agnostic architecture.

• Higher average order value: Smart AI product recommendations encourage users to add more items to the cart and complete the purchase with a higher order value. 

• 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.

• Increased revenue per order without sales overhead: Recommendations fit within procurement constraints and surface at the right moment—reorders, complementary items, seasonal restocking—without requiring sales intervention.

• Fleet management procurement: A buyer from a fleet management company reorders maintenance parts monthly. The AI surfaces recommended accessories and compatible consumables from their approved vendor list, at their contracted price.
• B2B distributor cross-sell: A wholesale buyer reordering a core product is shown complementary items that other buyers in the same segment regularly purchase together—filtered to the buyer's approved catalog and pricing tier. 
• Seasonal restocking: A retailer buyer who consistently increases order volumes in Q4 receives early recommendations for seasonal inventory based on prior year purchase patterns—surfaced at the right time, at contracted prices.

All three scenarios execute automatically—no sales intervention, manual merchandising, separate configuration per account required—freeing sales and operations teams to focus on higher-value work while driving increased basket size and average order value.

Build lasting loyalty by anticipating what your B2B customers want next.

Virto Commerce's AI ecommerce platform for B2B 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.