product-recommendations-ai

AI-Based Recommendation Engines

While content marketers are interested in adaptive and predictive content, e-commerce marketers might be more interested in the product recommendations that optimize sales and maximize revenue.

Amazon is the master of this, and their team uses machine learning to personalize product recommendations based on digital footprint and previous purchase history—both from you and others.

Features like “You may also like this” and “Other people also bought” that were popularized by Amazon are becoming the norm in e-commerce these days.

The “Personalization in Shopping” report also notes,“Recommendations are directly linked to longer shopping visits. Shoppers that clicked a product recommendation spent an average of 12.9 minutes on-site vs. 2.9 minutes for those that didn’t click recommendations.”

Of course, when they stay longer on the site, they spend more too.

The report states that “visits where the shopper clicked a recommendation comprise just 7% of all visits, but 24% of orders and 26% of revenue.” Shoppers that click recommendations view 4.8 times more products and spend five times more per visit.

Furthermore, 52% of orders from recommendation-clickers include one of the recommended items. These figures seem to be a good argument for the use of recommendation engines in e-commerce.

Luckily, nearly all e-commerce site, even small ones, can now use AI-driven product recommendations that are optimized and adapted for each individual person. The suggestions can come in many forms, but generally, they adapt the products each specific person is offered on websites or in email.

There are many popular e-commerce platforms in widespread use including Shopify, OpenCart, WooCommerce, PrestaShop, and Magento. Wouldn’t it be nice to get a product recommendation engine for the system that drives your business?  

You can use companies like BlueShift, Dynamic Yield, Perzonalization, Reflektion, RetentionScience, and Zen to help increase sales by personalized product recommendations, including the display of similar products, trending (most popular) products, top picks for you, recently viewed, often bought together, and the like.

Up-selling and cross-selling is also a common feature here. 

Do you want to extend recommendations beyond products and e-commerce?

The same type of predictive technology can be used to recommend what content (such as CTAs, banner ads, lead magnets, and other downloadable resources) to display on your website for a particular visitor. Certona, Marketo, and Uberflip do this effectively, helping to promote your content library in a smarter way.

Recommendation engines have many more uses. For example, they can be integrated into the products themselves. Netflix is a great example, and their recommendations for movies you might want to watch are a contributing factor for their huge success.

You might be able to replicate some of that magic by using recommendation engines appropriately in your business.