Artificial Intelligence-powered product recommendations and a more scientific approach to data has seen IKEA lift average order value (AOV) by 2% worldwide. Here Albert Bertlisson, head of engineering at Edge at IKEA Retail (Ingka Group) explains how the company did it. "At IKEA we have multiple places in our customer journey in various channels where different kinds of personalisation can deliver a superior customer experience," he says. "After a while in the broader'recommendations' team there was a decision to split the team to have one sub-team focused on product recommendations. The pandemic altered customer behaviour and needs as well. At that inflection point we decided to change our way of working and dive head-first into a more scientific approach to handle the operational complexities of delivering high quality product recommendations at scale. We deemed this necessary to improve our level of personalization and to have a holistic understanding of our customers."

With the continuing shift to digital, especially in the retail industry, ensuring a highly personalized shopping experience for online customers is crucial for establishing customer loyalty. In particular, product recommendations are an effective way to personalize the customer experience as they help customers discover products that match their tastes and preferences. Google has spent years delivering high-quality recommendations across our flagship products like YouTube and Google Search. Recommendations AI draws on that rich experience to give organizations a way to deliver highly personalized product recommendations to their customers at scale. Today, we are pleased to announce that Recommendations AI is now publicly available to all customers in beta.

With the continuing shift to digital, especially in the retail industry, ensuring a highly personalized shopping experience for online customers is crucial for establishing customer loyalty. In particular, product recommendations are an effective way to personalize the customer experience as they help customers discover products that match their tastes and preferences. Google has spent years delivering high-quality recommendations across our flagship products like YouTube and Google Search. Recommendations AI draws on that rich experience to give organizations a way to deliver highly personalized product recommendations to their customers at scale. Today, we are pleased to announce that Recommendations AI is now publicly available to all customers in beta.

During its Cloud Next 2019 conference, Google unveiled Recommendations AI, a fully managed service designed to help retail-oriented businesses deliver personalized product recommendations to their customers. Beginning today, following a lengthy preview period with early adopters that include Sephora, Boozt, and Digitec Galaxus, Recommendations AI is available in beta to eligible Google Cloud customers. Google says Recommendations AI was informed by work across properties like Google Ads, Google Search, and YouTube. Using machine learning to dynamically adapt to customer behavior and changes in variables like assortment, pricing, and special offers, it ostensibly boosts click-through rates and conversions on web, mobile, and email while increasing the revenue driven by recommendations and total revenue per visit. Thanks to "context hungry" deep learning models developed in partnership with Google Brain and Google Research, it's able to draw insights across tens of millions of items and constantly iterate on those insights in a real-time way.

Although the revealing of Google's Recommendation AI has already been done during the company's Cloud Next event in 2019, Google is now launching its beta version for its customers. A fully managed service -- Google's Recommendation AI -- targeting retail businesses, has been designed to help in delivering personalised recommendation of products to customers at scale. According to the blog post written by the product manager, Pallav Mehta, the move has been taken in sync with the ongoing shift of retail companies towards data-driven strategies and the increasing customer demand. To keep up their relevance in this competitive scenario, the retail companies now require to provide an ultimate personalised experience to customers. And one such way of enhancing the experience is by recommending them products matching their interest, preferences and need.

Many enterprises see the value in applying AI and machine learning to their business challenges, but not all have the necessary resources to do it. Where should your organization begin if you don't already have a team of data scientists, or if your team is fully committed to other tasks? Businesses need a quick and easy way to bring AI to their organizations. From the beginning, our goal has been to make AI accessible to as many businesses as possible. For example, last year we introduced Cloud AutoML to help businesses with limited ML expertise start building their own high-quality custom models.