Delivering Personalized Recommendations at Scale with Google's Recommendations AI

Table of Contents

1. Introduction
2. The Importance of Personalized Recommendations
3. Challenges in Delivering Personalized Recommendations
4. Google's Recommendations AI
5. How to Use Recommendations AI
6. Case Studies: BigCommerce and Qubit
7. The Future of AI in Retail
8. Conclusion

Delivering Highly Personalized Product Recommendations with Recommendations AI

In today's digital age, customer shopping behavior has changed dramatically. With fewer in-store visits, retailers have had to focus on shoring up their digital storefronts and finding new ways to engage with their customers. One of the most effective ways to do this is by delivering highly personalized product recommendations. However, delivering recommendations at Scale can be complex and time-consuming. That's where Google's Recommendations AI comes in.

The Importance of Personalized Recommendations

Delivering a superior customer experience has become a key differentiator for retailers. Personalized recommendations have emerged as one of the strongest potential drivers of revenue lift. However, delivering recommendations at scale can be challenging. Customers frequently change their habits and jump between contexts as they mull their shopping decisions. Helping them discover new or poorly cataloged items in a low-latency environment can be especially challenging.

Challenges in Delivering Personalized Recommendations

Delivering personalized recommendations at scale requires understanding a customer, especially if You don't have a lot of data about them. On the surface, it means deciding which item to recommend for a given customer in a given Context. However, customers frequently change their habits and jump between contexts as they mull their shopping decisions. Helping them discover new or poorly cataloged items in a low-latency environment can be especially challenging.

Google's Recommendations AI

Google's Recommendations AI uses the latest machine learning architectures to dynamically adapt to real-time user behavior, changes, and variables like a storefront and pricing. It's a fully managed service, so there's no need to pre-process your data, maintain pipelines, optimize models, or provision infrastructure to deliver at scale. Recommendations AI uses two parts of data to train personalization models: a catalog of items to recommend that includes item descriptions, images, categories, and price, and customer and event data, which is a history of what users have clicked on or bought.

How to Use Recommendations AI

Using Recommendations AI is a simple three-step process. First, put all your data to work by codelessly ingesting it into the Recommendations AI by using Google tools you already use. Next, select your business goals through an intuitive UI with just a few clicks and set your models to train. Finally, serve recommendations anywhere in the customer Journey, across web, mobile, or email campaigns. Recommendations AI can be delivered at sub-hundred latency to customers anywhere in the world, irrespective of their language or location.

Case Studies: BigCommerce and Qubit

BigCommerce and Qubit are two companies that have had great success with rolling out recommendations to their customers using Recommendations AI. BigCommerce was able to increase revenue by 269% among the cohort of shoppers who engaged with the related product recommendations. Qubit was able to increase revenue per visitor by 5% by using Google Recommendations AI in the basket.

The Future of AI in Retail

AI is going to be at the heart of the biggest shift we've seen in e-commerce since the beginning of e-commerce. Deep learning AI is going to disrupt retail like we've Never seen it before. The next two years are going to be critical for retailers to adopt AI-powered solutions like Recommendations AI to stay ahead of the competition.

Conclusion

Delivering highly personalized product recommendations is critical for retailers to stay ahead of the competition. Google's Recommendations AI is a powerful tool that can help retailers deliver personalized recommendations at scale. By using Recommendations AI, retailers can provide a superior customer experience and drive revenue lift.