A recommendation engine refers to the technology used to tailor which pieces of content or particular products will be shown to an individual while they interact on a brand’s digital properties. Sometimes referred to as a recommendation (or recommender) system, a recommendation engine is fueled by a web of complex algorithmic decisions. These algorithms mine user data, which includes interactions both onsite and offsite, to present that user with a personalized experience. Not only does this improve the discovery process, helping users find what they want more efficiently, it also allows the business deploying recommendations to learn more about the unique preferences of their customers and optimize results in real time.
A Product Recommendation Engine
A product recommendation engine is a specific algorithm-powered system on an eCommerce site that suggests products visitors may be interested in. These recommendations are typically suggested using widgets, each listing a handful of items to the user.
There are two main schools of product recommendations: global and personalized. The main distinction between the two is that global recommendations suggest the same products to all users browsing through items on a site, whereas personalized recommendations are specifically designed to meet a specific visitor’s tastes and preferences.
With global recommendations, there are two key strategies marketers employ to drive engagement.
The first, generic ranking, is based on product performance, meaning the current popularity of certain products. Generic ranking ignores individual consumer behavior and instead, programs recommendations around categories like “most popular” or “currently trending.”
The second strategy is contextual recommendations. These are product recommendations based not on consumer behavior, but on the context of a consumer at that current moment in time (ie. a category they are browsing). Examples of contextual recommendations are “similar items,” “frequently purchased together,” and “most popular in this category.”
A Content Recommendation Engine
Content recommendation engines function similarly to product recommendation engines but are designed specifically for publishers and brands looking to promote media, whether video, print, or any other form of content hosted on a website. Businesses can use content recommendations to deepen engagement while evolving with readers’ behavior and preferences.
Affinity profiles can reveal a site visitor’s most preferred content categories and tags. Affinity profiles are created using a weighted score based on a user’s digital interactions and activity. If a user interacts with a high volume of content with a particular attribute, such as articles about pop music, their affinity profile will attribute this type of content as an interest and refine content recommendations to meet the user’s interests.
The Amazon Recommendation Engine
Amazon has an especially sophisticated recommendation engine. Their engine has endless use cases that adjusts to a number of variables, from previous purchases, to geolocation and gender. Its eCommerce inventory is exceptionally vast, yet its engine is able to effectively tailor recommendations according to the user browsing through its site. It not only tailors experiences for its known users (Prime subscribers), but also for first-time and anonymous users as well.
Unknown or first-time users are served recommendations based on a form of collaborative filtering. It tracks a user’s behavior in the current session to start tailoring recommendations to serve them later in their session. Amazon recommends products based on what similar consumers have engaged with. It tracks user behavior, collects this data, and eventually analyzes it to predict what consumers are most likely to purchase. And while these recommendations are individually tailored, they are based on info collected about other consumers.
The experience for known users is a little different. The experience is hyper-personalized and employs a form of content-based filtering, which recommends products based on similar items a specific consumer has previously engaged with. It takes product attributes and affinity profiles into account to then recommend products that are similar to items they’ve previously viewed or engaged with. These attributes include any specific information about a specific product (ie. AAA Duracell batteries), and are then matched with user profiles, which are built based on what a user has searched, viewed, added to their cart, previously purchased, recommended to friends, and more.
The Dynamic Yield Engine
With a powerful recommendation engine, you are able to drive conversions, higher AOVs, and design the best possible shopping experience for your customers. Recommendation engines are the secret behind powerhouse names like Netflix, Amazon, and Spotify, but any eCommerce site can employ one for their site using a number of strategies and tactics to start witnessing positive results instantly.
Make sure you look for a recommendation engine that allows for flexible strategy setting, testing, and advanced settings to leverage your own expertise so you can extract the highest value from your recommendations and realize your highest ROI. Using our recommendation engine, you can deepen customer engagement. Our machine learning-powered algorithm puts your data to work to find the most effective strategies to maximize cross-sell and upsell opportunities. Tap into customer affinities, context and real-time intent to make recommendations across your site, mobile app, emails, and ads, and on any page, from the homepage to category pages and display banners. And not only do we ensure that your recommendations render results based on real-time data, but our engine also adapts layouts according to context and gives you the ability to customize recommendations according to your business goals.
