Cost control is a strategic priority for communication service providers. So much so, that in a recent survey, EY – Digital Transformation for 2020 and Beyond: A Global Telecommunications Study, only 39% of operators in developed markets strongly agreed that innovation capabilities will increase as a strategic priority relative to cost control in the next five years.
Revenue is under pressure as a result of the accelerating disruption caused by over-the-top (OTT) services shifting TV subscription services away from communication service providers. In fact, according to a cg42 study, 5.4 million subscribers will drop traditional cable, satellite or fiber-optic TV bundles, and that will cost the pay-TV business $5.5 billion in lost revenue.
This is compounded by the fact that having to deliver OTT content can increase network costs – a cord cutting household generates almost two times more traffic than an average household with linear TV, thus costing the communication service provider more to provide service to the customer. The trend will continue as more than 80% of traffic will be video, at increasingly higher quality – utilizing more bandwidth – by the end of this decade.
According to a McKinsey & Company prediction, “The global market for potential customers is still growing, but the forecast for traditional telcos is for low revenue growth and shrinking margins, because of increasing competition from OTT and technology players.”
We see opportunities to help communication service providers get ahead of these cord cutting and OTT trends – and resulting rising costs – utilizing intelligent approaches to achieve cost efficiencies and superior service quality. Applying big data analytics to the vast amounts of data generated by subscribers can be one of the most impactful means to help accomplish this. By capturing and correlating data generated from each subscriber, deep intelligence can be gained which will enable the operation of a more efficient network – while also improving the customer experience.
Following are a few examples of the network intelligence that communication service providers are able to obtain from having fast, easy access to the granular data generated by subscribers along with the resulting benefits:
This enables per-subscriber insight into the categories of OTT services traversing their network, such as Netflix, Hulu, YouTube and HBO Go. It also provides consumption metrics such as bytes delivered and bitrate. With this level of insight, communication service providers can aggregate the data to understand OTT consumption not only by subscriber, but also by node, edge aggregation point (e.g., CMTS/DSLAM) and geography (e.g., city and state). This results in better forecasting for capacity requirements so that CAPEX investments can be made in the optimal areas. It also allows for intelligent node splitting, by mapping subscribers in an intelligent, balanced way to maximize the utilization of existing network resources and avoid unnecessary truck rolls.
This provides insight into how closely the content is being delivered to the subscriber, enabling an understanding as to where traffic enters the network. Actions can then be taken to deploy caches (transparent and local) in the optimal locations to realize the most efficient utilization of the network. The subscriber experience is improved by deploying the caches closer to subscribers, and traffic offload/scale greatly improves by not having to carry as much video over the network. The result is far more cost-efficient utilization of available bandwidth and hardware resources. In addition, actions can be taken to increase additional peering capacity for particular markets.
This allows for an understanding of the path that content takes through a network at the subscriber level, rather than looking at it from a pure routing perspective. Understanding whether a particular content type is taking the expected path through the network can provide insights to help reduce network traffic, latency and service quality. In the case of OTT, it is beneficial to compare bitrates across different paths as one of several indicators that provide visibility into delivering optimal service quality.
For the better part of the past decade, communication service providers have been using specialized hardware – such as deep packet inspection and network performance monitoring equipment – to gain insight into their networks. While it is possible to deploy such equipment with “full coverage” to help improve network operations and control costs, this equipment is very expensive to purchase, maintain and upgrade. In addition, it poses limitations in terms of the visibility it can provide, the length of time data can be retained, and the ability to query all of the data in an underlying relational database to easily obtain immediate answers to pressing questions as well as support new business requirements.
Modern advances in big data architectures allow for a more pragmatic approach to analytics by harnessing subscriber-generated data, including those that are designed to run on standard commodity hardware or virtual machines leveraging SQL, and industry standard business intelligence tools for data exploration, visualization and reporting. Such modern architectures are designed to scale and return queries in seconds from billions of records generated by millions of subscribers. This type of architecture requires significantly smaller hardware resources that saves millions of dollars when compared to specialized hardware solutions.
In an era of rapidly rising network costs and revenue pressures, a new approach to analytics can deliver the insights communication service providers need to thrive in today’s environment. It requires new architectures that support fine grained analysis of the data generated by subscribers, delivering intelligence throughout the service provider network – by subscriber, device, node, aggregation edge and service provider edge.
Analytics becomes a strategic weapon to maintain competitiveness and address the reality of cord-cutting and increased OTT consumption. Modern analytics solutions can help to achieve overall capital and operational cost savings on the order of tens of millions of dollars, while also optimizing service quality to keep subscribers delighted with their service and less prone to churn.