Providing a highly personalized shopping experience for online shoppers, especially with the rapid shift to digital in the retail sector, is crucial to building customer loyalty. Product recommendations in particular are an effective way to personalize the customer experience by helping customers discover products that suit their tastes and preferences.

Google has spent years delivering high-quality recommendations on its major products, such as YouTube and Google Search. By leveraging this rich experience, Recommendations AI offers organizations a way to deliver highly personalized product recommendations to their customers at scale.

Upgrade your referral solutions

Instead of manually editing rules or managing cumbersome recommendation models in-house, you can upgrade your personalization strategy by modifying or complementing your existing solution with Recommendations AI.

Giving more importance to each customer rather than a product, Recommendations AI can piece together the history of a customer's shopping journey and offer them personalized product recommendations. Deep learning models use item and user metadata to generate insights across millions of items at scale, repeating these insights continuously in a way that is impossible to catch up with manually generated rules in real time.

Recommendations AI also offers a simplified model management experience in a scalable managed service with an intuitive user interface. This means your team no longer has to spend months writing thousands of lines of code to train custom recommendation models and struggle to keep up with the latest technology.

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Important updates for Recommendations AI

Now you can start using Recommendations AI with just a few clicks on the console. After you create a Google Cloud project, you can integrate and reload your catalog and user activity data with tools you already use, such as Merchant Center, Google Tag Manager, Google Analytics 360, Cloud Storage, and BigQuery.

Once the data import process is complete, you can select the model type, set your optimization goal, and start training your model. Initial model training and adjustment takes only two to five days, after which you can start offering recommendations to your customers. To make sure your setup works the way you want, you can preview the model's recommendations before offering them to customers.

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Our models can scale to support massive catalogs of tens of millions of items, ensuring that your customers have the opportunity to explore the full breadth of your catalog. Recommendations AI can also correct biases about products that are extremely popular or on sale, and better handle items with seasonality or sparse data. Our model training infrastructure allows us to retrain your models on a daily basis to gain insights from changing catalogs, user behavior, or shopping trends and incorporate them into the recommendations presented.

How do customers use Recommendations AI

Many retailers from around the world have gained tremendous value from Recommendations AI.

Sephora, a multinational retail company in beauty and personal care products with thousands of stores around the world, uses product recommendations to personalize the e-commerce experience of its customers.

“We wanted to offer our customers the same highly personalized shopping experience they experience on our digital platforms, physical stores,” says Jaclyn Luft, Sephora Director of Site Personalization and Testing. “We've started working with Google Cloud to explore how we can leverage innovative machine learning technology to provide enhanced personalization to our online customers through product recommendations.”

Luft continues: “Since we started implementing Recommendations AI, we have achieved impressive results, with a 50% increase in TO on our product pages and an increase of approximately 2% in the overall conversion rate on our homepage over our previous machine learning-driven recommendations.” “We're now evaluating how we can continue to test, iterate, and extend Recommendations AI to empower recommendations in other areas of our ecosystem, such as payment flow and emails.”

Home to many of Australia's iconic clothing and lifestyle brands, Hanes Australasia is another customer empowering personalization with Recommendations AI.

“Recommendations AI offers an extremely good data application and shows how Google Cloud can turn data into real commercial value,” says Peter Luu, Director of Analytics at Hanes Australasia Online. “When we A/B tested recommendations from Recommendations AI against our previous manual system, we found a double-digit increase in revenue per session.”

Luu also added: “The product is extremely easy to use-Google Cloud provided expertise, functionality, and performance, so we don't need to be machine learning experts to get the most out of it.”

Digitec Galaxus, Switzerland's largest online retailer offering its customers a wide range of products from electronics to clothing, uses Recommendations AI to help its customers find the products they are looking for.

“Providing our customers with a great online shopping experience is our top priority at Digitec Galaxus,” says Christian Sager, Personalization Product Owner at Digitec Galaxus. “With Recommendations AI, we are able to offer our customers personalized product recommendations on a large scale on our website.”

“Over the past few months, we've noticed a strong increase in the use of recommendations overall, and Recommendations AI delivered an additional increase in TO of up to 40% compared to the previous period,” Sager said. Recommendations AI has adapted well to changes, enabling us to keep up with our customers and their preferences.”

You can contact us to discover all the benefits of Recommendation AI and learn how to start using it step by step.