eCommerce product recommendations, as we all know, have become an integral part of any ecommerce website. Initially preconfigured recommendations like related products were suggested to the customers to show similar things they might be interested in, while browsing. But now-a-days, most of the brands have understood that for increasing cart sizes, checkout numbers and boosting sales, the recommendations need to be personalized and tailored for an individual customer.
There are different factors that drive rich, relevant and personalized eCommerce recommendations: analyzing the relevance of the items that are needed to be recommended to the customers and ensuring that recommendations are timely amongst a few. Simply put, the crux is to build an eCommerce store that simplifies the customer’s search of products/content and narrows it, so the customer can focus only on those products which they are really interested in. As easy as it seems, to achieve this effortlessness, a robust AI and ML technology needs to be employed.
And one of the leading and advanced AI technologies is Sensei, Adobe’s AI & ML technology for its Magento performance optimization platform. Recapping from my previous blog, personalized recommendations, powered by Adobe Sensei, brings machine-learning power to this process for providing recommendations based on the customer’s past eCommerce buying behavior along with the behavior of other shoppers with similar characteristics.
In this blog, we will explore Magento product recommendations engine along with the types of eCommerce product recommendations that can be employed in an online store.
Product recommendations in Magento are deployed as a SaaS service. As you can see in the architecture diagram, the Magento product recommendation engine contains the Magento storefront and backend. The Magento storefront includes the event collector and recommendations display template, whereas the Magento backend includes the data services, SaaS export module, and the admin UI. Once the recommendation modules are installed and configured, the storefront starts collecting behavioral data. Adobe Sensei processes the collected behavioral data along with the catalog data and evaluates the associated products that are leveraged by the recommendations service.
Personalized Recommendations recipe provides suggestions tailored to an individual customer’s needs and interests. The Magento performance optimization prediction model provides insights on what products the customer’s may be interested in based on the purchase history of a customer. The eCommerce product recommendation recipe uses machine learning to analyze an individual customer’s interaction with the products in the past and create relevant and personalized product recommendations quickly and effortlessly. This creates a seamless eCommerce customer experience for the customers and also improves a customer’s overall purchasing experience, which leads to a higher engagement, stronger brand loyalty and better sales.
Adobe Sensei-led product recommendation engine uses a different combination of algorithms to showcase personalized eCommerce recommendations to an individual customer. Some of them are: content-based similarity algorithms, collaborative filtering, popularity-based algorithms, etc. The results based on these algorithms are very effective since the customers are shown the content/products that they’re really interested in, which improves their experience, leading to higher conversions and more revenue.
There are different types of eCommerce recommendations provided to the merchant. The admin can place the recommendations at either bottom of main content or top of main content. The bottom of main content is enabled by default and showcases the recommendations below the main content area and before any other content blocks on the page. Whereas the top of main content showcases the recommendations above the main content area just below the top navigation bar.
The following types of recommendations are provided by Magento product recommendation engine:
Magento also provides backup eCommerce recommendations to fill items for which there is not sufficient input data to provide the requested recommendation. The most viewed recommendations will be shown to the customer incase if there is not sufficient input data collected for the following recommendation types:
Default filters are defined in Magento for the most popular, trending, and recommended for you recommendation types, by using these filters Magento provides more relevant results. Admin is also provided the option to enable/disable the display out-of-stock products option, if the display out-of-stock products option is set to No, out-of-stock products will not be recommended to the customers.
In this blog, we have explored and covered different facets of the product recommendations with Adobe Sensei. The advantages that Adobe Sensei brings to business are undeniable. It is one of the powerful technologies that Adobe Commerce eCommerce platform offers to its users and equips brands to target their audience with effective strategies and achieve their commerce goals. Merchants can easily leverage product recommendations in their Magento store and fine-tune the type of recommendations they prefer for their store.
If you are looking to implement Adobe Sensei recommendations into your Magento eCommerce store, or need help in developing an ecommerce store from scratch, contact us!