We’re excited to announce the launch of our Algolia Recommend Spring Release 2022. Algolia Recommend is an AI-based recommendations engine integrated with our Search and Discovery Platform that connects users with the most relevant, actionable recommendations.

From your home page, to category pages — from product pages through checkout — at every touchpoint and using any device, Algolia Recommend helps maximize your user engagement.

Read on to learn about what’s new in this release and to hear how other businesses have benefited from Algolia’s Search & Discovery Platform.

What’s new in Algolia Recommend?

Within Algolia Recommend, from a single dashboard, merchandisers, digital content managers, or digital business leaders can choose the model that is right for them, deploy it, and then track the results. Algolia Recommend Spring Release 2022 includes the following new capabilities:

• Popular Trends – An innovative new set of AI models that detects emerging trends based on users’ behavioral data as they interact with various brands, categories of products and content, as well as topics of interest, all of which provides merchandisers and digital content leaders the ability to engage instantly with visitors. This increases click through rates, reduces bounce rates, and helps visitors overcome a sense of ‘fear of missing out’ by surfacing what items or topics are trending.

 

• Business Rules – Low code/no code functionality for making continuous improvement with AI and activating unique business strategies –without the need for developer intervention. This provides greater flexibility and control for category merchandisers, online retail strategists, and content specialists to generate powerful new recommendations, connected to their business goals, while gaining significant operational efficiency and flexibility.

 

• Hybrid Recommend Engine – This is a combination of collaborative filtering algorithms and content-based filtering algorithms that together increase the relevancy and accuracy of recommendations. This approach overcomes the ‘cold start’ problem since recommendations can be presented immediately to users once the content-based data is indexed. Availability of behavioral information either at this initial stage or later can further help fine-tune and enrich the quality of recommendations. This new capability will enable all online vendors to get up and running immediately, increase user engagement more quickly and improve order rates.

In addition to these new models and features, customers have already been seeing success with Algolia Recommend capabilities such as:

• Related Products – This recommendation model enables retailers to increase conversions and orders by analyzing items shoppers interact with (e.g. clicks, adds to cart, and/or purchases) during their sessions and suggesting similar products from this analysis.

 

• Frequently Bought Together – This recommendation model increases average order value (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.

Algolia Recommend use cases and customers

One of the most obvious use cases for Algolia Recommend is in an online retail or ecommerce setting, yet Recommend is beneficial in media, publications, and a wide variety of other settings. Whether you choose to integrate it into your homepage, product detail pages, or checkout experience, it can help users discover products that they might need and ensure that they have a delightful experience in the process.

Algolia customers like Gymshark, one of the largest sportswear retailers in the UK, have seen Recommend transform their business in the form of a 150% increase in order rate and a 32% increase in ‘Add to cart’ rate during Black Friday 2021.Other retailers like Noski Noski have utilized recommendations to ensure that the right product within their catalog of over 10,000 products finds the right users, at just the right time. Flaconi has increased their average order value (AOV) by 10% since implementing Recommend. Others like UK-based Co-op, publisher Android Authority, broadband internet provider Orange Romania, and others are utilizing Algolia’s platform to power their recommendations.

According to Claire Armstrong, Director of Digital Products, at Fender Musical Instruments Corporation:

“with Algolia Recommend, we are able to further promote a wide variety of our content, curriculum, and learning activities within ‘Fender Play’, the complete learning app for guitar bass and ukulele, all of which are supporting the next generation of players on their musical journey.” 

Algolia customers are benefitting from Recommend capabilities in a variety of different use cases and industries beyond these examples, as well. Algolia is ready to power your recommendation needs.

Get started with recommendations today

Everything that we’re announcing as part of the Algolia Recommend Spring Release 2022 is available now. Want to see how Algolia Recommend can be implemented in as little as 4 days and start making an immediate impact on your business? Get a personalized demo today. Ready to start building? Sign up for free and get started with Algolia today.

If you’re an existing Algolia customer, implementing Recommend is as simple as introducing 6-lines of code to create a new carousel – learn more in our documentation. Reach out to your Customer Success Manager or contact us if you want to learn more. 

Thanks for reading, spreading the word, and trying out Recommend!