Credit: iStock The beneficial impact of enabling product recommendations can’t be overestimated. A recent post noted that Marshall Wolf Automation saw a 20% increase in average order value after it added product recommendations to its storefront. This uplift is consistent across both B2C and B2B commerce sites. Effective use of product recommendations is fast becoming one of the most straightforward ways to increase the top line. To make this process simpler and to increase the number of places where product recommendations can be implemented in your online store, Adobe is announcing that Product Recommendations for Magento Commerce has been integrated with Magento’s content creation tool, Page Builder. It’s now possible to simply drop in a dynamic recommendations block within any page that has been built with Page Builder. “The integration supports even greater leverage of Recommendations and provides our merchants with the flexibility of implementing it in multiple places and in varying ways throughout the store,” notes Ryan Rozich, director of product management at Adobe. The extensibility to use recommendations in different areas of the store makes it possible to use diverse types of recommendations more effectively. For example, a recommendation on the front page might focus on items that have high stock levels or are going out of season soon. And a recommendation on the page after login could be based on the customer’s behavior and prior activities. Signoff pages might have a list of products that complement purchased items. This new capability also allows for unique recommendations on either specific product pages or for categories of products. SUBSCRIBE TO OUR NEWSLETTER From our editors straight to your inbox Get started by entering your email address below. Please enter a valid email address Subscribe Rozich believes that this new integration makes it possible to deliver more recommendations in the “right context to dramatically increase value to the customer or prospect.” The most important aspect of this update is the ability to use personalized recommendations as dynamic content within Page Builder without complex programming. Many sites use static recommendations, and these often miss the mark. Making intelligent, contextual recommendations is far more impactful. Adobe’s Sensei-powered Product Recommendations has this functionality and makes personalized recommendations much simpler. There are nine pre-built algorithms that can be leveraged to get up and going far more quickly. In addition, there is a feedback loop that can be used to optimize recommendations going forward and to understand how personalized recommendations impacted the buying experience. This solution also makes it easy to ripple through any changes in the catalog to provide updated recommendations. This process is automated to eliminate the need to manually update pages or recommendations as the inventory changes. That’s a huge time saver for the team. The combination of delivering personalized or contextualized product recommendations and simplifying the addition of these product recommendations to the storefront is a game-changer for commerce sites. The increased revenue derived from product recommendations has already been proven, and personalizing recommendations in the right context on the site should increase those gains substantially. Adobe is once again delivering product integrations that enable brands to improve results and deliver improved CX. Using Page Builder and Product Recommendations together is the newest proof point of that strategy. Related content brandpost Taking the Next Step to Impactful Personalization By Aaron Goldberg Oct 19, 2021 3 mins IT Leadership brandpost Taking B2B CX Up a Level By Aaron Goldberg Oct 12, 2021 3 mins IT Leadership brandpost Improving Developer Tool Sets: Shortening the Time to Innovation By Aaron Goldberg Oct 05, 2021 3 mins IT Leadership brandpost The X-factor of Customer Experience By Sunil Menon Sep 22, 2021 5 mins IT Leadership Podcasts Videos Resources Events SUBSCRIBE TO OUR NEWSLETTER From our editors straight to your inbox Get started by entering your email address below. Please enter a valid email address Subscribe