Why the "Real-Time" in RTI is so Important

Why the “Real-Time” in RTI is so Important

Why the “Real-Time” in RTI is so Important

Welcome to the Kibo blog series about Real-Time Individualization. We invite you to learn about the difference between individualization and personalization in the first of our blog series here.

What is Real-Time Individualization?
The addition of real-time technologies to drive more individualized experiences is a key part of the modern commerce experience. Customers expect websites and mobile apps to understand them and their environment and react to their inputs in real time. So, what does it mean to be real-time in the context of individualization? Here is what you need to know:

Responding to a real-time request for content
The most basic aspect of a real-time system is being able to respond to a request from another system in real time (i.e., such that that the response data can be used to satisfy a need of the other system during the rendering of the current interaction with the customer). This aspect of real-time has been around forever. The systems we built in the early to mid-two thousands could respond in half a second, which was good enough for the time. With newer technologies, we have just pushed down this real-time response window to a few milliseconds.

The latest and greatest customer data
Probably the most touted aspect of modern real-time systems is moving customer data in real time. In older systems, the customer data is updated in large batches every few hours or every day. This means that marketers and websites have to wait to take advantage of new customer information. With real-time customer data, systems can start using new customer data right away and react to the needs of the consumer immediately.

Machine learning models
The final aspect of any true real-time system, which is often overlooked, is the ability to update models with the latest data. This requires a real-time modeling engine powered by technologies like Spark streaming. With these technologies, the machine learning models use micro-batches of data to update their relationships. Instead of waiting for hours and possibly millions of data events, these systems can use minutes or even seconds of data to update their understanding of the real-time trends.

With advances in technology, the individualized customer shopping experience we have all been waiting for is finally here. The gem that is hidden-in-plain-sight is the power of real-time to deliver these one-to-one experiences.

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