3. Get hyper-personalized with product recommendations

(AI & machine learning allow you to have a hyper-personalized product recommendations | Source: Amazon)

AI can crunch customer behavior on any website, using algorithms to make an accurate prediction in regards what products our customers will like. Then, it makes a recommendation that your customer is more likely to act on.

Amazon already does this. It uses your browsing history and your purchase history to recommend you more products that you’ll like. Not only is this good for Amazon; it also benefits you the customer. Instead of being greeted by tons of products you have zero interest in, you’re able to quickly sift through things there’s a high chance you will be interested in.

It’s this kind of hyper-personalization that customers want in 2018 and beyond. Away from the eCommerce sector, Netflix is another example of how AI-driven recommendations work. Using a customer’s past behavior and preferences, Netflix will recommend more shows it thinks they’ll enjoy. When you consider how many shows there are on Netflix, this use of AI saves the consumer so much time.

This kind of tailored recommendation represents a huge leap forward from the times when eCommerce retailers could only recommend the same bestsellers to everyone. This meant that conversions were down because we weren’t able to tailor our recommendations to specific customers.

Actionable takeaway

To improve your own store’s recommendations, display a list of suggested products that are based on a customer’s past browsing history. Add a “frequently bought together” feature, as well as a “related to items you’ve viewed” feature.

You can also personalize the user experience by displaying items that are related to past purchases.