Customers are constantly pressuring mobile operators to deliver faster, better and more available services. In an environment where commoditization has made competition between providers intense, operators seek to protect their customer base by maximizing the customers’ experience to increase loyalty and reduce churn, while simultaneously increasing efficiency and reducing operational costs and risk.
A growing number of operators are exploring new revenue streams such as smart grid, mHealth and mobile payments to supplement the rapidly dwindling returns from basic voice and data services. The question is, what is the right approach to these challenges, and why should operators invest in solutions to these problems?
Operational intelligence (OI) is a new approach to solving the volume, velocity and variety challenges, while delivering the value. It enables operators to gain visibility into real time events, insight into actual and expected performance and key metrics using real-time analytics and take action in the form of automated responses to exceptions before they become visible to the customer.
Here are five signs that indicate service providers may need operational intelligence. First, a lack of insight into the behavior of subscribers. To maximize customer experience, OI addresses these challenges by enabling the operator to combine the intelligence derived from deep network-signalling events with the breadth of customer information held across the organization.
This builds a holistic picture of an individual subscriber’s experience, providing visibility into that experience, insight into customer behavior and the ability to respond automatically to problems or opportunities in real time. The ability to exploit network-signaling data provides an enormous advantage for understanding customer experience on an individual basis.
Second, fraud threats continue to be a growing issue. To prevent fraud and increase enterprise security, OI can determine a threat by monitoring, filtering, and correlating events to identify patterns indicative of threats based on frequencies, sequences, origins, timing, and divergence from some norm. It is only when such seemingly innocuous events are filtered against a “watch list” and further analyzed through more complex logic rules that their patterns reveal them as threats.
Third, operations are only performed on big data ‘at rest.’ To take advantage of the big data opportunity, OI combines ‘at rest’ analytics with the real-time analytics, providing operators with a formidable set of tools to tame the data deluge for both big data ‘at rest’ and big data ‘in motion’.