Recommendations and Merchandising

Create intelligent product recommendation banners and dynamically sort product lists using AI-powered algorithms that learn from customer behavior and transaction data. AB Tasty's Recommendations & Merchandising solution enables you to deploy personalized recommendations across websites, email campaigns (including Brevo and Adobe Campaign), and CMS platforms like PrestaShop, Shopify, and Salesforce Commerce Cloud while merchandising product catalogs to optimize visibility and sales performance.

Why recommendations and merchandising matter

Modern e-commerce requires dynamic product discovery that adapts to individual customer journeys and business objectives. Static product listings and generic recommendations can't compete with intelligent systems that understand customer intent and product relationships. Whether showing associated products in shopping carts, personalizing email campaigns, or optimizing category page rankings, effective recommendations and merchandising directly impact conversion rates and customer satisfaction.

How AB Tasty leverages recommendations and merchandising

The platform combines multiple recommendation algorithms including similar items, associated products, and sorted items based on sales, views, recency, or ratings. You can create recommendation widgets triggered by specific customer actions like viewing items, making purchases, or adding products to cart. The system integrates through JavaScript tag, API endpoints, and direct CMS connections, with built-in A/B testing capabilities to optimize banner placement, algorithm selection, and design elements.

Recommendations & Merchandising Use cases

Recommendations & Merchandising allows you to cover a wide range of business use cases such as:

  • Best sellers

  • New arrivals to showcase recently added products.

  • Trending products based on recent sales.

  • Similar items to suggest alternatives on PDP.

  • Bought together to propose complementary items in the cart.

  • Cross-sell and upsell to maximize basket value.

  • Low-stock nudges to create urgency.

  • Price-drop highlights to attract price-sensitive users.

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