If you’ve ever used Amazon or Netflix, you’ve experienced the value of recommendation systems firsthand. These sophisticated systems identify recommendations autonomously for individual users based on past purchases and searches, as well as other behaviors. Customers get algorithmic recommendations on additional offerings that are intended to be relevant, valued, and helpful. Consumers can use recommendations to:
Marketers can enhance offers that proactively build better customer relationships, retention and sales. For example, organizations typically realize:
SAS’s vision is to help marketers be effective through analytic techniques. Consumer preferences are hard to predict. By using SAS’s deep library of algorithms, recommendations can automatically shapeshift to meet the demands of the consumer, and create brand relevancy through data-driven personalization. A significant differentiator is the analytics lifecycle: only by recognizing and fully supporting the phases around data, discovery and deployment will brands get a complete process to take advantage of impactful insights. Everything SAS does is built around the need to get from data to value, with the analytics lifecycle as the underlying principle.
Figure 1: Recommendation systems
SAS Customer Intelligence 360 uses two algorithmic techniques in a hybrid approach to deliver recommendations. Research has demonstrated that a hybrid approach, combining collaborative filtering and content-based (item) filtering is more effective, while providing a solution to overcome recommendation system challenges common in marketing, such as the scalability, sparsity and cold-start problems. The platform supports both embedded and customizable analytical approaches to deploying a recommendation system.
Let's walk through a detailed presentation and demonstration together in the video below to address how SAS Customer Intelligence 360:
Learn more about how the SAS Platform can be applied for embedded customer analytics here.
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