Algolia Recommend Extends the Capabilities of Algolia’s API Platform to Compose the Experiences of Tomorrow’s Applications

Algolia, the leading API Platform for Dynamic Experiences, today announced the general availability of Algolia Recommend, a high-performing, Artificial Intelligence (AI)-optimized API that accelerates the creation and implementation of product recommendations across digital touchpoints.

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”We’ve had several hundred companies use Algolia Recommend during our two-month beta period. Some companies, such as The Vegan Kind, went into production with Algolia Recommend within days,” said Julien Lemoine, CTO, Algolia. “The beta program has been a success, both in terms of the number of beta users and their successful use of the new API to easily ‘switch on’ the new recommendation capabilities very quickly within their own environments.”

For The Vegan Kind, a provider of subscription boxes of vegan goodies, it is all about helping customers make the right choices by providing them with great alternatives and suggestions as they browse. After deploying Algolia Recommend, The Vegan Kind was able to surface recommended products associated with the items visualized, increasing the opportunity for shopping cart expansion.

Algolia Recommend surfaces in milliseconds the most relevant recommendations, offers, or suggestions for a shopper using machine learning models that collect data from two sources: shopper behavior (the shoppers’ actions across a website or app, including previous purchases) and product data (all product attributes contained in the product catalog, including product, description, availability, and price).

Julien Lemoine added: “The Algolia Recommend API provides unique flexibility and more programmable control, which enables developers to filter, merchandise, rank, and contextualize recommendations to better fit their business goals. By adding this to our Algolia Search API, our customers now have access to a single, unified platform that leverages the same product catalog, merchandising logic, and analytics across search, navigation, and recommendations.”

Algolia Recommend’s API-first approach, front-end frameworks, and advanced documentation ensure that it is simple to integrate and highly flexible. HiCart, the creators of a user-friendly, Lebanese marketplace for their members to enjoy a seamless experience, was struggling to offer recommendations with their current implementation. With as little as six lines of code, HiCart was able to implement Algolia Recommend and go into production in four days. Now, when a shopper searches for a specific item, additional alternatives are surfaced as well, meaning the customer has more choices, a more satisfying experience, and less chance of abandoning their shopping cart.

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Algolia Recommend significantly increases the average order value (AOV) through shopping cart expansion and customer satisfaction in online stores. By using Algolia Recommend, businesses can more easily increase their average order value with a smart “related product” capability that improves their ability to merchandise a broader range of items. This, in turn, enables them to surface highly relevant recommendations in the moment, demonstrating a richer understanding of their customers and earning greater loyalty in the process.

Julien Lemoine continued: “We are seeing companies pleased with their initial implementation – they are already seeing how Algolia Recommend can improve their customers’ experience, making it more personal and relevant. Eventually, product managers will be able to fine-tune and iterate recommendations on the fly. This means they will be able to quickly offer recommendations associated with the items that their shoppers are seeking and adjust the product recommendations across every part of the shopping journey.”

Available immediately, Algolia Recommend comprises two of the more popular machine learning models that automatically deliver tailored recommendations:

• Related Products: This recommendation model enables retailers to increase conversions and orders by analyzing items shoppers interact with (e.g. clicks, adds to a cart, and/or purchases) during their sessions and suggesting similar products from this analysis.
• Frequently Bought Together: This recommendation model increases AOV by upselling complementary items on the product page or shopping cart page based on what other shoppers have purchased with that same item during a single shopping session.

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