The Telco Cloud, defined as virtualized infrastructure to run digital services and agile operations, has not only paved the way for digitalization of CSP businesses, but also opened up new avenues for data monetization.
Through 2017, CSPs will continue to make investments in analytics to drive focused investments in network and services, manage customer experience better and personalize services. To support this, data monetization solutions that offer customer intelligence out of a Telco Cloud and how it can be monetized will be critical and much in demand. The solutions will include clever manipulation and reporting of data for innovative services and increased customer satisfaction. Analytics based on machine learning and artificial intelligence will introduce novel ways of data exploitation, aimed for unprecedented customer experience.
The Next generation of OSS systems need to inherently provide analytics that are proactive, predictive, descriptive, diagnostic and prescriptive for both network and business functions. They benefit all teams of the service provider: Operations, Planning, Marketing, Customer Care, Data Scientist, CTO, CIO, CMO and Chief Digital Officer (CDO).
Analytics can be broadly classified into the following 2 categories:
- Connectivity analytics for Telco Cloud network and IoT network
- Intelligence analytics for Telco Cloud customers and IoT verticals
Analytics with automation capabilities can solve many investment needs and network related problems. To optimize infrastructure expansion, CSPs need to utilize analytics that add business value to decisions for planning new sites and capacity upgrades. Analytics help in the identification of revenue-generating locations, capabilities of handsets, customer behavior, uplink and downlink video traffic, consumption of video/conversational services, etc. Using analytics (real-time and non-real-time), CSPs can trigger processes for proactive or prescriptive actions to solve capacity issues. Analytics can also help in failure prediction and assessment of service impact, followed by automated root-cause analysis.
With explosive IoT growth around the corner, service providers need to create intelligence out of the IoT traffic that will flow through their Telco Cloud networks. With billions of dollars predicted for the IoT industry, intelligence on the usage and preferences for IoT will be critical through use of analytics to forecast patterns and prevent IoT network/service/device failures. This includes building dashboards for service availability, incident/unavailability breakdown by region/location and also geolocation-based service impact.
Some of the top use cases for connectivity analytics are:
- Predictive troubleshooting - Care and Operations
- Prediction of faults - Operations
- Proactive messaging to customers - Care
Connectivity analytics offer deep analysis of standard KPIs and identify patterns, root causes and opportunities or risks related to network behavior. Using machine learning, users can explore large volumes of history to find patterns of behavior and create correlations, trouble tickets or remediation tasks. Automating workflows from repetitive problems in the network can reduce the number of alarms to action, ultimately leading to reduced operational costs and Mean Time to Repair (MTTR).
Telco Cloud service providers consider data and analytics as their top investment priorities to improve customer experience, to contextualize and personalize services for customers. Monetizing data helps in directing marketing campaigns for maximum business impact, designing new digital services, providing information to external advertisers and agencies. Also, analytics are seen increasingly important to understand the experience, needs and behavior of the IoT industry verticals.
For all of the above mentioned reasons, marketing/sales analytics will be treated by the Telco Cloud provider as shareable assets, which will need to be assured, managed and sold as services. As digital Telco Cloud business expands into newer areas, analytics sharing will be the norm, however there will be complexities in the generation and distribution associated with this.
Some ways through which the complexity can be reduced is by splitting the intelligence analytics into the following buckets:
- Service intelligence: Machine learning, when integrated with service data, offers powerful predictive service quality management capability to anticipate problems and helps service provider in protecting their SLAs. This is truer for NFV and IoT environments, where services need to be managed proactively and improvised dynamically
- Customer intelligence: This includes advanced customer data discovery and profiling to predict customer behavior, such as churn and customer’s next action. It includes also creation of personalized offers and campaigns that target for maximum results, segmenting customers by value and creating/optimizing tariff plans
- Device intelligence: These analytics help in tracking usage of popular devices and apps as well as in the reduction or removal of undesirable devices or content. IoT device analytics are used to market IoT services for IoT providers of energy, smart cities, connected home/vehicles, manufacturing and asset tracking. These include IoT-user and IoT-device behavior, trends and predictions
In summary, analytics not only make the Telco Cloud provider an ‘intelligent platform’, but also support new revenue and facilitate automated actions for remediation. With connectivity and intelligence analytics, the Telco Cloud provider can fuel the growth of digital services, as well as the IoT services. With the use of machine learning in analytics, many operational problems will be solved automatically and customer experience will be improved through targeted messages, campaigns and services totally tuned to the customer need.