Product Recommendations are considered a “must-have” for online retailers. With them done right, merchants can tailor the shopping experiences around the needs of each individual customer, leaving them wanting for more.
Not only that they are a powerful marketing tool for online merchants to increase conversions, boost revenue, and stimulate shopper engagement, but they also create a better shopping experience, reducing the time and frustration to find something great.
Product recommendations are especially valuable to organizations that have a very large, diverse product catalog, and they continuously seek to improve in this field, because recommendations provide a direct way to increase the impact of digital merchandising efforts while reducing the manual work required to uncover meaningful product affinities.
Exclusively available to Magento Commerce merchants as an extension on the Magento Marketplace, this capability is powered by Adobe Sensei, the real powerhouse behind it. With its ability to grab real-time behavioral context from all channels, Adobe Sensei analyses data such as recent sales, customer attributes, browsing behavior, or situational context.
Once it processes all the information, Sensei creates product relationships. Based on these affinities, it builds a recommendation structure that is able to serve results for different types of recommendations. From the user’s perspective, things could not be more simple. Once the modules have been installed and configured, the business user will have to go to the Magento admin panel, create the recommendations, assign specific values like internal name, storefront label, pages to deploy to, recommendation type, number of products in the unit, page placement, and once they are all set, deploy them directly on the storefront.
Magento also comes to help, and that is why it provides a set of recommendation types users can use across various storefront pages. Shopper-based, item-based, contextual popularity-based, and more, are all strengthened by the continuous analysis of shopper behavior by Adobe Sensei. Seamless integration with Page Builder also makes it effortless to drag & drop existing recommendations onto pages being authored within Page Builder.
While retailers use them extensively, product recommendations can be challenging to deploy with the desired flexibility, due to delayed data processing, limitations on complex data sets, and some solutions’ inability to push recommendations across all customer touchpoints.
If you have any technical questions about product recommendations and what is the best way to implement them, please contact us or drop us an email at email@example.com. We’re here to help! Oak3 is a Solution Partner for Magento, so don’t hesitate to get in touch!