Did you know Amazon’s personalised recommendations account for 35 percent of their revenues?
But Amazon isn’t the only one: modern e-commerce stores offer some form of a personalised experience. In fact, the one-to-one strategy of delivering personalised content and recommendations is driving today’s intelligent commerce.
If your e-commerce store still isn’t using AI-powered product recommendation engines, you may be losing out on 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’ll see a list of highly-rated recommendations or “also bought” recommendations. Amazon is using machine learning to provide you with products you might like based on your browsing activity and customer details.
But they’re not the only ones. Take Netflix or Spotify, for instance—they use similar tactics to make the customer experience more rewarding. (Let’s blame machine learning for the reason you’re binge watching Netflix or listening to Taylor Swift on an endless loop.)
To create a customised shopping experience, brands need to use machine learning and artificial intelligence to determine what products their customers want. This allows the customer to shop without having to spend time browsing endlessly.
Recommendations also tend to be hyper-targeted, which means that the audience can be a single individual or a small cluster of like-minded customers.
You can try to do this manually, but if you’re dealing with a massive product catalogue, you may find that your recommendations can quickly become imprecise and irrelevant. And if you have over a thousand SKUs in your system, it will be impractical and almost impossible to keep up.