How Does the Web Smart Recommender Work?

In today’s world, one-size-fits-all strategies no longer work—users are more diverse than ever, and their needs are constantly evolving. That’s where Insider's Web Smart Recommender comes in, offering a cutting-edge AI-powered solution that delivers personalized and relevant content to each user. It’s a game-changer in creating unique, tailored experiences that resonate with customers.

But how does Insider’s Web Smart Recommender work? It all starts with data—specifically, understanding what users are doing and what they’re interested in. This data, known as clickstream data, is collected from both desktop and mobile web interactions, and is processed using System Rules to power recommendations across Web, Email, App, InStory, and Web Push notifications. But data alone isn’t enough. We also need product catalogs to generate personalized product lists. This catalog information can be collected in three ways: via Clickstream data, Catalog Integration through XML, or Catalog Integration through API.

How Smart Recommender Delivers the Best Recommendations

Generic Algorithms: In these algorithms, product recommendations are driven by product performance rather than individual consumer behavior or context. It's a broad approach to showcasing top-performing products.

Contextual Algorithms: Here, the system zeroes in on the current context of individual consumers—such as the product category they're viewing—tailoring recommendations based on that context.

Collaborative Filtering: This powerful approach takes personalization to the next level by using the behaviors of similar users. It identifies what products to recommend based on the actions of users who share similar interests and behaviors. It’s all about making smarter, more intuitive suggestions.

User-Based Collaborative Filtering: How It Works

In user-based algorithms, a user-product-rating matrix is used to determine recommendations. It considers actions like product visits, purchases, and items added to the cart within the last 30 days. By comparing the current user with similar users, the system suggests products that similar users have shown interest in but that the current user has not yet explored. For example, if User X visits products A, B, C, and D, and User Y visits A, B, F, and G, the system would recommend products C and D to User Y, and F and G to User X—personalizing the experience to each user’s unique tastes.

With Web Smart Recommender, you're not just keeping up with changing customer behavior—you’re ahead of the curve, creating richer, more meaningful experiences that drive customer engagement, satisfaction, and sales. It’s time to unlock the full potential of personalization and elevate your business to the next level!