Riverbed increases revenue with Oracle Analytics

Riverbed Technology is an IT industry leader in the area of network and application performance management. Based in San Francisco, the company's flagship technology is the Riverbed Digital Performance Platform, which helps companies track metrics, gain insight, and increase productivity in a world where digital performance drives business success.

Riverbed's hardware monitoring platforms come with specific hardware and software solutions that track various metrics. That data delivers the insights customers want on their digital engagement and performance. On the flip side, that data could also be used to tell a story about Riverbed’s own potential future sales—if Riverbed had the proper tools.

A significant part of Riverbed's revenue comes from support service renewals for hardware. In fact, Riverbed's Vice President for Information Systems Bhishma Jani cites service renewal as a significant revenue driver for the company. However, despite having all manner of metrics from customer hardware, the company lacked the ability to leverage that data to identify renewal drivers.

This created two problems. First, any sort of behavior patterns or indicators for renewals were purely anecdotal. Second, because renewals lacked a definitive model, no initiatives existed for improving that renewal rate. “So technology-wise, we were not in a happy place even though we had best of ideas,” says Jani. “We did not have the right technology for us to put those ideas into motion.”

To understand Riverbed customer renewals, the company’s data science team focused on a number of indicators: service contract expiration, number of service cases against a contract, product mix, device telemetry, discount rate, and more. These metrics were woven together to form an AI-ready data fabric of sales history. Riverbed’s AI engines processed the data and output it to a visualization, and then drilled down further to identify indicators and consistent variables regarding renewals.

“We chose that precise use case of renewal data because we felt it was measurable,” says Jani. “It was a significant portion of our business, and we had most of the data that was influencing [it] ready to be modeled for AI.”

Analysis delivered quantifiable results that revealed what actually drove renewals. The renewal team put this information to use and saw results within one quarter: Customer renewal forecast accuracy increased by 8%, and early service renewals also increased by 8%. In addition, on-time service renewals increased 16%.

First, this process identified Riverbed products and equipment currently in use but not covered by service contracts. Riverbed was able to reach out and reconnect with these happy customers that had outlasted their old service contracts and easily convert them into renewals. Second, once renewal indicators were identified, Riverbed was able to plan resources for manufacturing and fulfillment more accurately.