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Riverbed Uses Oracle to Drive Sales and Renewals

Michael Chen
Senior Manager

What converts a lead into a sale? Or in the case of a subscription service, what converts an existing customer into a renewing customer?

That was the dilemma that San Francisco-based Riverbed Technology was facing not too long ago. Riverbed’s primary service is delivering performance management and analysis for networks and applications. Hardware monitoring comes via embedded solutions that track key metrics as defined by customers. From a customer side, that volume of big data gives critical insight into performance. But for Riverbed, that data could also be converted into identifying key metrics and points that signaled the likelihood of a customer renewal towards the end of a service agreement.

Identifying those patterns, though, meant being able to do several things with that data:

  • Customer data needed to be collected into a single source for analysis.
  • Historical renewal data had to be folded into this analysis.
  • A unified analytics tool was needed to provide insights and correlations between customer status and key events for renewal.

Riverbed lacked all of that. Its sales team had hunches as to what triggered likely renewals but without a way to quantify that data, it was all anecdotal. Which created another problem for the sales team—there was no way to identify paths for improving sales metrics. This was frustrating for Riverbed’s Senior IT Director Bhishma Jani, as all of the information Riverbed needed was at its fingertips; the company simply lacked a good way to put it together. “So technology-wise, we were not in a happy place. Even though we had the best of ideas, we did not have the right technology for us to put those ideas into motion,” Jani says.

From a big data perspective, it was important to understand just how many sources existed. Consider that Riverbed was taking customer performance metrics and combining that with sales data, which involved many external data sources (customers) and Riverbed’s internal data sources from various departments (sales, marketing, logistics). To put this in perspective, let’s look at the effort required to get a conclusion from a single customer:

  • Sales would have to look at a customer’s sales and renewal history to identify various points of engagement and conversion.
  • A data analyst would have to look at that customer’s hardware performance metrics to identify notable events around performance, service requests, and other elements that could point to customer satisfaction. Riverbed’s customers themselves are producing big data and involve monitoring app rollout and health for huge corporations such as Michelin and Hilton.
  • Other variables would have to be factored in, including product mix, whether or not discounts were offered and applied, and contract length.
  • For that single customer, a data analyst would have to draw conclusions about the influence of any variables on eventual renewals (or terminations).

Even for a single customer, significant work is involved to sift through all that data—but then look at the fact that Riverbed has a giant roster of customers (over 30,000 customers in the company’s history) and any ongoing contracts would be continuously producing new data every hour, every minute.

Simply put, that’s a lot of data points. That data is sitting right there, waiting to deliver insights into sales possibilities; but without proper tools, it is simply numbers without context or meaning.

Riverbed began looking at the various options and ultimately went with Oracle’s combination of Oracle Cloud Infrastructure and Oracle Analytics Cloud. This platform enabled the staff to gain deep insights into the company’s historical sales data while connecting with its internal AI core. “We chose that precise use case of renewal data because we felt it was measurable,” Jani says. “It was a significant portion of our business and we had most of the data that was influencing and ready to be modeled for AI.”

The result? Within a single fiscal quarter, Riverbed was able to build a data model and implement it for its sales team—and this led to a 68% improvement in forecasting customer renewals. It also set the stage for further analytics integration into other operational elements within the company, such as human resources.

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