Algolia Launches Algolia Recommend — A New API-First Product to Generate Blazing Fast, Highly Relevant Recommendations at Scale for Retailers

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

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 catalogue, including product, description, availability, and price).

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What makes Algolia Recommend unique is the fact that it is simple to integrate — with as little as six lines of code — and easy to use because it is developed with an API-first approach. Conversely, building a recommendation engine from scratch can be complex; additionally, relying on off-the-shelf, packaged solutions makes it almost impossible to develop a differentiated customer experience or gain a competitive advantage.

In an example of one use case, Orange România uses Algolia Recommend technology to retain and convert shoppers landing on out-of-stock products. According to Florin Spataru, digital marketing manager at Orange România, “By recommending different, but relevant products, we were able unlock eight percent more revenue on our online store.”

Algolia Recommend will have a significant impact on increasing the average order value through shopping cart expansion and customer satisfaction in online stores — and enables retailers to earn greater trust and loyalty by demonstrating a richer understanding of their customers, by surfacing highly relevant recommendations in the moment.

According to Jordan Jewell, research manager, digital commerce at IDC: “Due to COVID-19, a record number of retailers saw record growth in 2020, raising the stakes for practically every organization to have a digital commerce strategy. In this hyper-competitive market, merchants must provide customers with unique, personalized, and frictionless commerce experiences to succeed. An API-first tech stack is the foundation of these differentiated experiences. Algolia’s API-first approach to search and navigation is well suited to enable the future of commerce. With Algolia Recommend, merchants and developers can add a new API to their toolkit to build more comprehensive digital commerce experiences and grow online sales.”

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Algolia Recommend initially includes 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 a shopper interacts with (e.g. clicks, adds to a cart, and/or purchases) and suggesting similar products during the same session.
  • Frequently Bought Together: This recommendation model increases Average Order Value by upselling complementary items on the product page or shopping cart page based on how other shoppers have interacted with that same item during a single shopping session.

“Algolia recently unveiled its new company direction and vision and helped customers go beyond the search box with their digital commerce strategies,” said Julien Lemoine, co-founder and chief technology officer of Algolia. “The release of Algolia Recommend provides the next building block for retailers to optimize their online experience and increase their revenue. These retailers have already unlocked $1 billion+ additional annual revenue on the back of up to 1.7 trillion searches across Algolia’s API platform.”

In a recent survey, 42%1 of respondents said that it was “very important” or “somewhat important” to see personalized content (such as recommendations, offers, or experiences). When studying the impact of product recommendations in the U.S., 38%2 of respondents stated they would shop “much more frequently” or “more frequently” at online retailers if they received such recommendations.

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