Product recommendations

Suggests products that this individual shopper has browsed or purchased frequently.

Suggests the most likely purchase by comparing the shopper’s purchase history with the purchasing behavior of other customers who’ve viewed this product.

Suggests complementary products based on what people who bought this product have ended up buying with it.

Suggests the product that shoppers who viewed this product most often went on to buy.

Suggests products which are similar to the product on the current page, based on the contents of product details, using Natural Language Processing.

Suggests products you’ve defined as related to the current product for merchandising purposes.

Suggests products that are trending with other shoppers right now.

Suggests the latest products, e.g. from across the store, or from the customer’s favorite category.

We give you full merchandising control to design the product recommendations or content recommendations strategy that is right for your business. Build your own strategy by mixing over 30 different recommendation types, merchandising and business filters. For example, you can control which brands appear side-by-side, a vital feature if you’re a retailer selling multiple labels.

71% of brands use basic cross-selling recommendations. To stand out from the competition, make your suggestions more personal and base them on customer behavior, like browsing history, geo-location, category affinity, and purchase history. Treat customers differently based on their stage in the purchase funnel (home page, product page, cart page) or customer lifecycle (first-time user vs. VIP customer). If cookies are enabled, you can serve product recommendations and content recommendations based on previous browsing behavior even before a shopper has signed up with an email address.

Provide a consistent cross-channel experience and maximize conversion potential by aligning the most relevant recommendations at all stages of the purchase funnel across email, website, mobile and app. You can recommend products and content anywhere – on navigational menus, the homepage, category pages, site overlays, banners, and inside bulk and triggered emails.

Our recommendations engine uses solid machine learning to track and analyze the behavior of each shopper. It looks at the shopper’s real-time contextual, browsing, purchasing and demographic data. The platform then filters recommendations using the rules you set to select the products and content each shopper or reader is most likely to be interested in at this moment in time.

Product recommendations are based on the most up-to-date data to ensure you don’t suggest products that are out of stock, that the customers just purchased or that have changed in price. You can provide multiple fall back data sources to ensure that customers are always presented with the best possible suggestion.

Set up A/B/n tests for any recommendation element – including the layout, design and location – to understand which recommendation type drives most revenue for your business. Automatically optimize individual pieces of content, or the entire CX, across channels using Machine Learning. Analyze your personalization campaigns in Google Analytics.

Include recommendations in cart, form and browse abandonment messages to increase click throughs. Learn more >

Add an element of urgency and speed up on-site conversions with live trends, showing how many people have viewed or bought the recommended item recently. Learn more >

Reduce purchase anxiety and reassure shoppers by including rating stars from other customers. They are pulled in real-time from your product rating provider, ensuring that only recent and positive scores are shown. Learn more >

Localize recommendations based on the shopper’s physical location at the moment of engagement. Learn more >

Reduce exit rates by suggesting products and content in exit-intent triggered pop-ups. Learn more >