Enjoyable shopping made easy with Google’s Recommendations AI 

Ecommerce should be more than just buying things online. It should be a great experience.  Things have changed quickly in the past couple of years in terms of how people shop and how companies sell their products.

Even before the pandemic, there was enough pressure on retailers to close brick and mortar locations and focus on convenience and diversity. COVID-19 took the world far past the point of no return in this regard. As we become increasingly reliant on online shopping for a substantial part of our needs, it also becomes clear that retailers are compelled to provide that pleasurable experience. Whether you’re shopping for toilet paper (who would’ve thought that would be a thing before 2020?) or for a new bike, the internet is an ocean of almost unlimited possibilities. It is easy to become disoriented, especially for the later adopters of online retail. What should be a seamless, convenient process can breed frustration and a reserved behavior. 

Personalization is one of the best ways to tackle this issue and it stands at the foundation of the great experience ecommerce should be.  It’s also a surefire way to create and maintain customer loyalty, something all online retailers strive for.

So how do you personalize a customer’s shopping experience, particularly when you have an inventory that could go into the hundreds of thousands, or even millions of items?

How do you give one of many individual customers the ideal items list suited for their specific needs and how do you scale this? Enter Google’s Recommendations AI.

Scaling Customization

Product recommendations are one of the most effective ways to achieve a high level of personalization and create an enjoyable online shopping experience. But delivering recommendations can be a difficult mission for several good reasons.

Customer behavior is a crucial element to consider when setting up a memorable ecommerce experience. However, one size does not fit all and using the same approach for cold start buyers and loyal clientele is probably not the best idea.

According to a Harvard Business Review study, 73% of online transactions have an omnichannel component. That means the understanding of the omnichannel context is pivotal for providing recommendations at scale.

Another impediment is a lack of meta-data structure, which makes it almost impossible to customize each visit. Google’s Recommendations AI was created to tackle all these issues and enable retailers to provide their customers with personalized product recommendations, at scale, across all channels.

And it does so by leveraging many years of research and development spent solving these exact problems and many more. In a nutshell, Recommendations AI brings the power behind YouTube, Google Search and Google Shopping to any retailer’s website. Its machine learning model is constantly adjusting to provide not just item recommendations, but the enjoyable experience that keeps customers coming back each time.

By analyzing each customer’s browsing history on the retailer’s page instantly, the deep learning model enables a quick and easy return to products of previous interest. It uses this data to generate insights across millions of items. Harnessing this kind of potential would be impossible using conventional manual approaches.

In practice

Let’s take Robert, a young professional with a passion for hiking. It’s about time Robert changed his hiking boots, so he visits the website of an outdoor equipment retailer popular in the community, powered by Recommendations AI. After browsing for a while, he zeroes in on a brand-new model, which he adds to the cart. The machine learning model has insights on all the items in the retailer’s inventory, including the newer additions. By having the information about other items bought frequently with these boots, it can recommend Robert a pair of socks, highly praised on hiking forums, as a good combination with the new boots. He also sees a pair of shorts and a wind jacket, which he marks as favorites for now.

Several days later, a satisfied Robert visits the outdoor gear retailer again, this time to a landing page customized with other hiking apparel and items suggested to him by the machine learning model, based on the experience of other shoppers with similar browsing history and carts. This is just one quick, virtual example of how loyalty is created and sustained, and it can be easily replicated with Google’s Recommendations AI.

Retailers don’t need to go through the trouble and expenses of creating their own machine learning model. As Google Cloud Premier Partner, Zitec can help you plug your data into Recommendations AI and be up and running in no time. Our Google Cloud experts will assist you on your journey to larger shopping carts, higher conversion rates and increased revenue.

We’re happy to answer any questions about how your online store can meet and exceed your goals with Recommendations AI, so feel free to get in touch.

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