The Insights API now supports authenticated user tokens and offers enhanced revenue analytics for Adobe Commerce (Magento) customers. This update, powered by the new PHP API client (v4), introduces the ability to track event subtypes and revenue data, including Magento-specific discounts. Additionally, this release enhances PII protection and improves cookie consent handling. Click here to learn more.

Enhanced API client automation and documentation are now available in beta. This update addresses the challenge of manually synchronizing our numerous API clients, ensuring consistency and reliability across new feature releases. The automated approach includes an updated API client framework with a comprehensive test suite and enriched documentation featuring detailed code snippets for every method in all supported languages. This enhancement significantly improves the developer experience, allowing for more efficient integration and utilization of Algolia's customizable search and discovery platform. Click here to learn more.

AI Personalization in the Merchandising Studio in private beta addresses a significant gap in the ranking configuration tools. Previously, configuring and visualizing the impact of AI personalization was not possible within the "Layers of Ranking" widget. With this update, AI Personalization is now integrated into the widget, inviting users who haven't activated it to configure the feature. This feature allows the selection of user tokens to visualize the impact on any query or category page, offering greater control and explainability in a no-code, unified interface. This feature is accessible to all customers in the AI Personalization private beta and will be included in the general availability launch of the feature. Watch demo here.

Flutter Helper, a library for integrating seamless search experiences into Flutter applications, is now generally available. This transition marks the commitment to providing a stable, powerful tool that developers can rely on for building production-ready applications using our official Dart API client. Now covered by our support policy, Algolia for Flutter simplifies search logic and integrates features like autocomplete, faceting, sorting, and highlighting into Flutter apps. This ensures users find exactly what they’re looking for, enhancing the overall search experience and maximizing the potential of your applications. Learn more and get started with Flutter Helper here. 

Improvements have been made to the mechanisms for extending and customizing the Algolia cartridge on SFCC, particularly focusing on the product data model. Previously, customizations often intermingled with core code, complicating upgrades and support. The new approach cleanly separates Algolia core code from customization-specific code, providing a dedicated space for extensions and customizations. This not only simplifies upgrades and maintenance but also enhances the flexibility and compatibility of our integration for enterprise customers. Comprehensive documentation and examples further aid in the customization process, ensuring smoother and more efficient operations. These enhancements aim to reduce maintenance overhead, and support ongoing customer benefits from new product improvements. Click here to learn more.

The Algolia Recommend setup process for Salesforce Commerce Cloud's native Storefront Reference Architecture (SFRA) has been simplified. Historically, setting up Recommend required extensive coding and conceptual adjustments, which complicated the integration process. The new low-code solution allows integrators to easily incorporate pre-made templates into SFRA content slots with minimal coding required. This enhancement not only streamlines the integration process but also provides comprehensive guidance for using various Recommend models. This update makes it easier and faster to adopt Recommend on SFCC SFRA storefronts. Click here to learn more.

Algolia Recommend is now supported natively on BigCommerce. Previously, merchants using our native BigCommerce integration needed a custom setup to utilize the Recommend feature, making it inaccessible for those without technical resources. With this update, Recommend can be easily added to any page by dragging and dropping our widget using the BigCommerce Page Builder. The widget is ready to use out of the box for users with trained models, and merchants can also customize their experience with a simple mechanism to edit the widget’s CSS. This enhancement streamlines the integration process, empowering merchants to effortlessly leverage Algolia Recommend and enhance their e-commerce platforms.

Optimizations for Shopify Markets have been implemented to improve indexing efficiency for merchants offering localized products globally. Previously, the number of indexing jobs required for merchants with multiple markets and extensive product data could lead to process failures. Indexing by markets, currencies, and languages instead of markets, countries, and languages has significantly reduced the number of indexing jobs. This update enhances the efficiency and reliability of the indexing process and supports seamless integrations.

Shopify Webpixels are now integrated for tracking conversion events. Previously, there was no standardized way for merchants to easily send add-to-cart and checkout completion insights to Algolia, often requiring complex workarounds. With Shopify’s Web Pixels feature, merchants can now effortlessly track these events by adding our code snippets to their store. This streamlined process enhances the accuracy and reliability of event tracking, enabling merchants to leverage valuable insights for powering Algolia features such as NeuralSearch, Recommend, Personalization, DRR, and Query Categorization. This update significantly improves integration efficiency and enhances the overall e-commerce platform experience. Click here to learn more.

Recommend has a new setting in the Frequently Bought Together (FBT) model! Previously, the FBT model inferred connections between products based on collaborative filtering, recommending items even if they hadn't been purchased together before. While sophisticated, this approach may not suit all use cases. To address this, we are introducing the "Strict" variant in public beta, which only recommends items that have been strictly bought together. This new option allows users to choose between the existing "Relaxed" model, which increases catalog discovery through inferred relationships, and the new "Strict" model, ensuring recommendations are based solely on past purchases. This update provides greater clarity and flexibility, enhancing the precision of product recommendations for different business needs. This feature is now available to all customers.