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Technical Video

Vantage with Sri Raghavan

Despite having access to more data than ever, companies continue to struggle to turn analytics into answers. That's because analytics can be complex, posing barriers to getting real-time answers and driving high-value outcomes.

Hear from advanced analytics expert, Sri Raghavan, as he provides a comprehensive expert walkthrough of Teradata Vantage™ in action. Vantage is a leading analytics platform. It's part of a modern cloud architecture that unifies analytics, data lakes, and data warehouses in the cloud.  

Watch as Raghavan takes a deep dive into Vantage’s analytic functions. He demonstrates how to use native path analytics and a machine learning model to solve a real-world use case of a retailer needing to understand customer behaviors in order to predict those likely to churn.  

See how you can use your programming language of choice, including SQL, Python, and R, and native Vantage functions to get answers quickly. Also find out firsthand how Vantage delivers scalability, performance, and ecosystem compatibility to solve problems and deliver answers.

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Today, I’m going to take you into a deeper dive off the power of Vantage analytic functions. One of the big things about Vantage that you are going to keep hearing over and over again is the ability to deliver impactful business outcomes. After all, what’s the point of doing analytics if you can’t really deliver outcomes that make a difference in the lives of people and businesses? So here’s one example of a business outcome. Customer churn. Retailers in many other industries are always interested in acquiring more customers and making more customers stay. So here’s an example of what I’m going to go through today to talk about how an analyst is going to deal with the issue of customer churn. So here’s an example as you will see of customers engaging in numbers of activities and in this case, this retailer wants to understand how to look at behaviors that end up in members cancelling their membership. They want to look at their behaviors that lead up to membership cancellation. They want to see if there are certain kinds of sentiments that are being extracted during the cancellation process. And number three, if they can actually model it somehow they can predict who is going to cancel and when. Sign up to watch video and read full transcript.