Telecom networks around the globe are seeing exponential growth in demand for reliable and affordable data, resulting in massive disruptions. These disruptions have brought in new business models, new opportunities, and new competition to the market place. The Communication Service Providers (CSP) will be required to deal with much more real-time and complex data to manage the networks of the future.
Smarter devices, OTT Apps (specially video) and networks are generating unprecedented demand for data on one hand and a shifting customer base on the other. Service provider business models of the future will hugely depend on who creates better insights from the data generated to sustain the level of service quality, provides those insights to ecosystem players on the network & generates newer sources of revenue.
Imagine a scenario where CSP customer issues are resolved proactively and customers are informed before they call customer service, network problems are predicted before they affect the service, marketing campaigns adapt in real time and offers are made based on customer usage and behavior and resolutions are carried out by analytics-driven runbook automation etc.
Network Analytics provides operators and enterprises with deep understanding of the network, enabling smarter data-driven decisions. The combination of big data technologies with advanced analytics methodologies such as machine learning and real-time streaming analytics provide deep insights in descriptive, diagnostics, prescriptive and predictive forms. It enables operator networks to meet key business goals such as customer retention, data monetization, operational efficiency & growth.
Availability of Networks analytics tools and solutions with CSPs shall completely transform their ability to provide cost-effective service, deliver differentiated quality to customers and plan for the next generation of networks and services. Here are few examples of anticipated impacts:
Improve Quality of Customer Experience: Network analytics enable a CSP to optimize customers' QoE by observing real & near real time insights and prioritizing actions towards networks or subscribers, as applicable. Reliable experience index models (e.g. SLI, ELI) are being developed to predict customer satisfaction to optimize networks for sustainable quality.
Network Control and Cost Optimization: CSPs get better control on their networks due to real-time insights into network traffic and effectively factoring in issues related to congestion, utilization, and service consumption. These capabilities enable CSPs direct network changes and investments to target specific, highly profitable services, applications or even customers, helping them optimize the commercial value of available capacity.
Efficient Service Operations: With data services taking an ever-increasing share of the utilization of the network, CSPs need to be able to proactively monitor network performance and provide real-time correlation or root-cause analysis to help isolate faults.
Capacity Planning and Deployment: By anticipating network bottlenecks in a more granular way and with better accuracy, CSPs can plan network expansions proactively and with more precision. Network Analytics tools can transcend the limitations of traditional traffic forecasting approaches such as minutes of use and data volumes. Newer statistical models integrate simulation techniques and can link demands for a certain quality of service to network specifications for each cell in a defined geography.
Marketing and Data Monetization: Armed with this 360-degree view, the CSP could statistically segment its mainstream and premium customers into micro segments, grouping various businesses in new ways to produce insights. This segmentation enables the provider to determine the right channel treatment and solutions for each customer grouping.
Security: Traditional security controls are no longer effective at blocking cyber threats. With Big Data analytics it is possible to process large amounts of data and analyze security attacks, potential risks and generate real-time results.
We have already seen a number of telecom operators across the world deriving great value from pervasive analytics that has helped them plan, monitor and manage their services better. To site a couple of examples- Vodafone Group is using a Customer Experience Management (CEM) solution across 22 markets for 446 Million subscribers across mobile and fixed segments. Similarly, a Finnish telecommunications company, DNA Oyj has achieved enormous business benefits in Customer Care & Operations using Network Analytics solution. It has resulted in decrease of 20% average handling time on customer care calls, a reduction of 90% escalations to the 2nd line of its customer care unit and a phenomenal 65% increase in customer satisfactions.
The next-generation networks will provide ubiquitous mobile communication not only for people but also for connected things. This will add new challenges, such as the ability for the network to handle a massive number of connected devices at low cost and the need for increased energy performance on both the client and network side. Analytics driven intelligent automation and self-managing network capabilities will become vital with the rise of IoT and machine-to-machine communications, where in some cases mission critical and live saving decisions will made using Network Data Analytics.