The Power of Recommendation Systems: Driving Engagement and Conversion

Recommendation systems are integral to the development of the internet as we know it today, and are a central function of emerging technology companies. Beyond search rankings that offer relevant information to everyone, they influence the new and exciting movies all your friends are watching, and the most relevant ads that companies pay top dollar to show you. Each day, new applications of recommendation systems emerge, shaping our digital experiences.

How a Recommender Works

The "Suggested For You" feature on Instagram, the "Selected For You" options on Amazon, and curated articles on the Wall Street Journal are all examples of how recommendation systems enhance our digital interactions. 

A recommendation system calculates and provides relevant content to the user based on knowledge of the user, content, and interactions between the user and the item. Initially, recommendation systems used basic association rules. If someone similar to someone else liked something, it was highly likely the latter would also like that item. Today, advanced technologies such as sequential transformer models, multimodal representations, and graph neural nets are among the brightest areas of R&D in machine learning, all being used in recommendation systems.

The Value of Recommendation Systems

Regardless of an organization's recommendation engine maturity, suggesting relevant information to users increases the organization's credibility, visibility, and engagement. For any business, being found in web search results can lead to an instant boost in website visits or walk‐in traffic, both of which contribute to increased sales or leads. An effective recommendation system keeps visitors engaged by offering other areas of interest, allowing organizations to identify where to focus their promotional and innovation efforts.

Recommendation engine results are one of the most reliable channels for gaining new insights on customers. They also offer one of the highest ROI activities that a business can develop, as recommendations continue to bring back visitors long after a primary campaign has ended.

Qualified Visitors

For many, recommendation engines attract the most active and qualified visitors. These visitors are often looking to solve a problem or find information and sometimes need assistance in determining an effective solution. When they find the solution, they convert. Conversion is the action that fulfills a website's goal, such as a purchase on an e-commerce website, an event registration, a subscription, or a completed lead form for a B2B business. When someone searches, they are taking an active step to solve a problem or find information, making them more likely to complete the task when they find the best result.

Behavior Data

Recommendations are an in‐the‐moment suggestion based on behavior.  People actively searching are more qualified, motivated, and loyal to the solutions they are given. Evidence is the data that reveals a user’s tastes. Collecting events and behaviors that indicate the user’s preferences allows for more refined recommendations.

In the age of Generative AI, data is incredibly important. For example, on Instagram, when offered a new account to follow, users are given options like “Are you interested in this post?” or “Not interested in this post” to continuously curate the content that is presented to them. The goal is to provide the user with what they want to see even before they know it. 

To get a full picture of the user, organizations must consider not only what users are viewing but also how long they view it, the frequency of visits, user location, and types of devices. All data sources are equally important for the smooth and consistent operation of different types of recommendation engines. Additionally, if an organization desires to take content or user features into account, they need to deal with various types of data. 

Overcoming the Cold Start Problem

If an organization is launching a new product or innovation without historical data of user interaction—in other words, facing a cold start—analyzing the content can drive traffic. Time and time again, recommendation engines prove to be the most profitable, most productive, and generate the highest customer lifetime value compared to other channels. This success stems from the power of intent, timing, and relevance provided by recommendation engines.