Did you know that Amazon’s personalized recommendations account for 35 percent of sales?
But Amazon isn’t the only one: modern e-commerce stores offer a kind of personalized experience. In fact, the one-on-one strategy of delivering personalized content and recommendations is what drives smart commerce today.
If your ecommerce store is still not using AI-powered product recommendation engines, you may be losing sales.
Read on to learn how machine learning can help you speed up the buying process and how to get started today.
What are AI-powered product recommendations?
Visit Amazon and you will see a list of highly rated recommendations or “also bought” recommendations. Amazon uses machine learning to provide you with products that you might like based on your browsing activity and customer data.
But they’re not the only ones. Take Netflix or Spotify, for example – they use similar tactics to make the customer experience more rewarding. (Let’s blame machine learning for keeping you watching Netflix or listening to Taylor Swift on an endless loop.)
To create a customized shopping experience, brands need to use machine learning and artificial intelligence to determine which products their customers want. This enables the customer to shop without having to spend endless time browsing.
Referrals are also usually hyper-targeted, meaning that the target audience can be a single person or a small group of like-minded customers.
You can try doing this manually, but when you’re dealing with a huge catalog of products, your recommendations can quickly become inaccurate and irrelevant. And when you have over a thousand SKUs in your system, it’s inconvenient and almost impossible to keep up with.