In today’s hyper-competitive retail environment, consumers expect all of their digital experiences to be personalized and relevant, including the recommendations they receive from eCommerce companies. Machine learning has become the crux of personalizing the recommendation experience for visitors, assisting in making generalizations from observed data to better find the best products or offers to serve each specific shopper.

We have spent nearly a decade developing the most powerful recommendation engine in the market, built based on direct feedback from thousands of merchandisers and retailers across hundreds of global brands and verticals.

With Dynamic Yield, you can exercise full merchandising control over your content or product recommendations; adapt the recommendation layout according to context, fuse multiple strategies into a single recommendation widget, test and optimize any element – including the layout, design, recommendation strategy, location, widget headers, and more.

You can also predict customers’ next purchase through a self-training, deep learning recommendation strategy to automatically predict the next product a user may be interested in by exploiting the co-occurrence of products in a user’s browsing history.

This in-depth overview includes a comprehensive look at all of our existing recommendations capabilities, broken down across 13 categories.