By Anis Cheikhrouhou, Jim Davenport & Anish Kelkar, Nokia, Bell Labs Consulting
Customer satisfaction drives better performance in the market and increased company value.
Unfortunately, as is evident from lagging NPS scores, traditional service providers including MSOs have not performed as well as other industries despite allocating a proportionally much larger share of human resources in customer care. In contrast, web scale providers allocate very limited human resources for customer care, but have much stronger customer intimacy.
To make matters more challenging, customer preferences are evolving rapidly. Access to content is being diversified away from a single static subscription service delivery toward more granular consumption.
Consumers and enterprises are demanding a more transactional model for personalized, context aware services that adapt rapidly to changes in need or demand.
To meet these current and evolving challenges, MSOs must transform to a customer centric operations.
The paradigm shifts from managing a network to delivering a service that delights the customer. Service customization, user control of their services and automatic adaptation of service delivery are the new requirements for operations. In this model of operations with capability to take autonomous actions, it is critical to define the optimum level of human responsibilities and the insights that they need to perform their functions.
This paper presents four core principles of future operations: customer-centric automation, hyper-scale analytics, an open and programmable architecture, and cognitive awareness.
While MSOs have moved aggressively towards automation of the manual operational functions, customer centric automation will enable MSOs to assure and fulfill dynamic services that adapt to changes in network state and customer demand. Deploying hyper-scale analytics at the edge and across all business functions helps to predict, minimize and potentially prevent customer impact due to network outages as well as to rapidly isolate root causes.
MSOs have started partnering with each other to deploy services across their footprints. Adopting an open and programmable architecture with API exposure to 3rd parties will accelerate deployment and reduce costs for such future services. Cognitive awareness and self-learning capabilities in the network enable delivering a personalized service to customers by learning from their past usage of service and their current context (in transit, location, home/business/school, etc.). Autonomous, dynamic actions based on the state of demand and resources will prevent customers from being impacted from network and service issues and address demand surges faster than humanly possible.
This new operating model should either be neutral or reduce operational costs for the MSOs. We expect mean time to restore service and service cycle time will be less than 5 minutes compared to multiple hours or days. Today the goal for network availability is typically 99.999%. With the adoption of this architecture, the goal is to deliver services that are available all the time. From a customer perspective, we should aim to have at least 60% of services be fully customizable, and at least 80% of interactions should be done through self-care. No Faults Found truck rolls will be minimized to less than 5% because analytics will identify root cause before the truck rolls are launched.
These benefits are achievable, but MSOs need to transform and make foundational changes. On the people side, MSO’s need higher skilled roles with data sciences and software automation skills.
Processes need to be updated so that they drive standardization and enable automation. Data will need to become available freely across all business silos. Tickets and workflows management will need improved discipline so that correct data is entered consistently and these artifacts can be used across the organizational silos for training cognitive platforms.