Real-Time Personalization with Redis and RudderStack | RudderStack

By neub9
2 Min Read

Personalization is a significant factor in the success of businesses, with companies experiencing a 5-15 percent increase in revenue through high-quality personalization. Despite the buzz surrounding personalization, many businesses are still stuck at the basic level, offering only basic customization. A McKinsey survey shows that only 15 percent of companies believe they are on the right track with personalization, and a major reason for this struggle is technology.

Technological challenges around personalization can be divided into two primary categories: behavior-based personalization and profile-based personalization. Behavior-based personalization relies on in-session customer behavior to drive personalized experiences, whereas profile-based personalization uses demographic or characteristic data to determine the next steps in the customer journey. Both methods have their own technical challenges, such as collecting and unifying event and profile data, and building robust pipelines to handle high volumes of data with low latency.

To address these challenges, many companies are using technologies like Redis and RudderStack to drive personalization use cases. Redis serves as the repository for recent behavioral and profile data, while RudderStack provides real-time event streaming and low-latency job updates from user profile tables.

Using Redis and RudderStack, companies can build a real-time personalization engine that can deliver personalized experiences to users based on behavior and profile data. Redis’s in-memory database with low latency and support for multiple data structures makes it an ideal solution for storing and accessing the various types of data needed for personalization. RudderStack’s event stream and reverse ETL capabilities provide the real-time event feedback loop needed to drive personalized recommendations and experiences.

By leveraging Redis and RudderStack, companies can overcome the technological challenges of personalization and build a centralized engine for delivering real-time personalized experiences to their users.

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