Communication service providers (CSPs) have long talked about achieving better “business synergy” through greater cross-departmental coordination and sharing of data. Unfortunately “synergy” never seems to get a high enough priority. Historically, different departments in a CSP—finance, billing, marketing, network engineering—have built or bought their own systems, with little thought as to how to share data and make it truly useful across the organization.

However, CSPs today are realizing that they cannot continue to operate this way, as it puts them in danger of falling behind at a time when the telecom business is changing so radically. Not only are telecom margins getting thinner and the list of competitors growing, but the sheer number of services and the way those services are being offered has exploded. Plus, many of the services, such as content, are off-net services that the CSP neither owns nor controls. In short, it’s much tougher to understand the bottom line impact of decisions on purchasing, third party partnering, prices, discounts, offers, network build-outs—even customer support policies.
Now telecom departments certainly know that greater coordination and better data exchange would help, but each is saddled with its legacy data and processes. What they lack is an internal data currency and mechanism for monitoring business performance.

Yes, they can roll up performance data at a macro level to produce a quarterly report for stockholders, but as they say, “the devil is in the details”. The most valuable intelligence here is not the macro intelligence provided by existing DW/BI systems, but actionable intelligence—the ability, for instance, to look at the revenues and costs associated with individual subscribers, business customers, or products. Without that low level of detail, business decisions about the prices, offers, and markets for a niche telecom product or service are mostly guesswork. Why? Because no granular data exists to base an informed decision on.

Not only is the kind of intelligence important here, but also the timeliness of it. Because DW/BI systems typically run in an offline mode for analysis and reporting purposes, they are not meant to provide intelligence in an operational time frame. CSPs are finding that expecting this of their existing DW/BI infrastructure is like asking an elephant to dance—they’re just not built for that purpose.

Therefore, having the right software platform is critical, because it will need to aggregate and correlate data from hundreds, if not thousands of data sources. In addition, it needs an ability to collect, classify and roll up data records into a robust hierarchy in near-real-time. It also needs to be able to:
• Apply different types of business logic to this data, depending on the specific business problem to be solved
• Run automated analytics on this data, to identify micro trends which are operational in nature
• Present the information so gleaned in a manner that business users can make sense of it and ‘play around’ with it (to do what-if analyses, for example)
• Provide the CSP with a mechanism to act on this intelligence—trigger actions on this information, and to track such actions to closure
Once the above is in place, it can be used to solve a variety of business problems that CSPs are facing today. Possible applications include:
• Computing the propensity of customers to make inbound calls to the call center. If a CSP can identify this, and get a view of why certain customers are likely to call the call center, they can potentially save millions of dollars in call center costs by proactively reaching out to these customers using a cheaper mode of communication (an e-mail, for instance).
• Giving CSPs granular visibility into their profit margins, such as margin by customer / customer segment, by product, by geography, etc. This information can be critical in making multiple business decisions – for example, how to respond to a price cut by a competitor? Or which customers to focus on most for retention policies?
• Monitoring the performance of new products, such as actual versus anticipated uptake goals. This can include analyzing trends in the outcome data to predict deviations before they occur (such as margins not succeeding in the future, even though they are on track today). Also, by carefully tracking, in near-real-time, a number of metrics related to service delivery, CSPs can ensure that rollout problems can be readily detected and repaired preemptively, before they negatively impact the customer experience.

One company that identified this impending crisis before others is Subex. And the solution they have been recommending to address it is the ROC. The ROC, or Revenue Operations Center, is a vision that a CSP can monitor their business through a virtual operations center analogous to the NOC, or Network Operations Center. While a NOC monitors network performance across specific nodes, regions, and layers, a ROC monitors business performance by analyzing data that resides across a host of B/OSS systems from billing and provisioning to customer care.