Are you looking to boost your online sales and revolutionize the shopping experience for your customers? Here’s a powerful strategy backed by data: AI-powered product recommendations. Did you know that a staggering 24% of all orders can be attributed to engagement with these intelligent recommendations? By leveraging the capabilities of product recommendation technology, you can truly transform your eCommerce platform and drive higher engagement, increased sales, and even reselling and upselling opportunities. To ensure your customers embark on a delightful shopping journey, empowering them with a robust product recommendation engine is crucial. Let’s explore how you can effectively promote online sales and provide an exceptional user experience with the help of this cutting-edge technology.

So, what is a product recommendation engine? If you’ve ever used an online store as a customer, chances are you’ve engaged with a product recommendation engine. They use AI to analyze user behavior and determine the products that users are likely to be interested in. This drives revenue on the eCommerce site, through social media ads, and promotional emails or texts.

Personalized recommendations result in twice the click-through rate and quadruple the conversion rate. “AI-powered product recommendations” can also result in 26% higher order value on average. This means that an AI-powered product recommendation engine is arguably the most important arrow in your eCommerce quiver. “Intelligent product recommendations allow for natural, logical opportunities to upsell and cross-sell.”

We’ve discussed product recommendation engines in earlier blog posts, here and here.

In today’s blog post, we’re taking a deep dive into one of Magento’s most interesting features – the Adobe Commerce Product Recommendation Engine, powered by Adobe Sensei.

You can check out the previous blog post in this series, on Magento 2 Page Builder.

How do product recommendation engines work?

A product recommendation engine uses AI/ML to generate personalized purchase suggestions and promotions. Product recommendations are made based on the following kinds of data:

  • on-store searches
  • online searches
  • products purchased
  • products in cart
  • wishlisted products
  • viewed products

Broadly speaking, it’s easier to train a product using catalog-only recommendation types, such as “More like this” or “Visually similar” products. On the other hand, behavior-based recommendation types demand more training time. Adobe Sensei needs a certain amount of time and user volume in order to train the machine learning models. It’s then possible to deploy behavior-based recommendation units on the site.

As always, the more data that’s available, the more accurate and relevant the machine learning models become. For that reason, the sooner you get started on data collection, the better. You’ll need to use the magento/production-recommendations module. Behavioral data is recomputed every four hours. It’s faster to train the model on some recommendation types, compared to others.

Each customer is mapped to an individual customer profile, based on which the algorithm maps products to the user. The product recommendation engine customizes and presents:

  • contextual offers
  • product suggestions
  • topical marketing communication

Head-to-head: Salesforce Einstein vs Adobe Sensei

Salesforce Commerce Cloud and Adobe Commerce are two major eCommerce platforms with very strong AI support: Salesforce Einstein and Adobe Sensei. Before diving into Product Recommendations for Adobe Commerce, we thought we’d do a quick comparison with Salesforce Einstein Recommendation Builder.

Incidentally, Adobe Sensei also powers Adobe Commerce’s Live Search feature, an AI-enhanced search experience. It removes the manual work involved in refining the search parameters.

So, what is Product Recommendations for Adobe Commerce?

This free extension is a part of the Adobe Suite. It’s powered by Adobe Sensei, and allows for easy implementation on the Adobe Commerce platform. It’s ideal for existing users of Adobe Commerce, and primarily suited to enterprise-grade customers. Magento integrations are complex by nature, and you’ll need to work with experienced tech partners.

It can, in theory, be implemented on non-Adobe platforms. However, the implementation is not seamless and you may be better off with the platform’s native integration. Another downside was noted by Gartner in 2018. They argued that it came with a “longer-than-average deployment time frame and below-average ease of deployment.” (Magento is an open-source platform, and provides limitless flexibility – but you do need the expertise to work with it!)

You can download the infosheet here.

How does it work?

Product Recommendations by Adobe Commerce is deployed as a SaaS-based solution. It offers a completely customizable collection of AI/ML algorithms. It leverages data from users’ various digital interactions with your website. The algorithms undertake a deep analysis of aggregated shopper data and deliver suggestions from your Magento product catalog, to increase their likelihood of purchase.

From the moment that the tool is installed and configured on your eCommerce website, on-site behavioral data is automatically collected. (It also integrates existing data gathered from Adobe Analytics, to improve reporting.) This data is correlated with catalog data (like product name and availability). Based on these data elements, product associations are calculated and suggestions are provided. Adobe Commerce offers a number of recommendation types (shopper-based, item-based, contextural, and popularity-based). These can be used across shopfront pages. Associations and suggestions are calculated for each.

New recommendations can be created easily within the streamlined workflow itself. You don’t need to plan manual page tagging or manual analysis for Adobe product recommendations. It’s all automated, all the time.

Since it integrates seamlessly with Adobe Commerce’s Page Builder, you can identify recommendations that suit new pages being authored within Page Builder. You can drag-and-drop these recommendations onto the new pages.

How do you set up and integrate it?

This tool can be set up using a five-step process. During this process, you as the admin can design the algorithms to deliver the right product recommendations. It’s the recommended tool to use with Adobe Commerce, but setup with non-Magento platforms can be very complicated. The tool can be integrated seamlessly with all kinds of Adobe Commerce storefronts.

The bottomline

Magento’s Product Recommendations tool is built on a strong AI base and works across channels and for complex audiences. However, it isn’t suitable for those on a limited budget as Magento integrations can be challenging. It also isn’t suitable for those who aren’t on Adobe Commerce already.

We’ve mentioned a few times how challenging Magento or Adobe integrations can be. Having said that, as an experienced integration partner, Ziffity can help you work with your Adobe Commerce website to setup and integrate your Product Recommendations engine. Talk to our team about how you can elevate your user experience with a strong product recommendation strategy. Get in touch today.