The operational landscape of communications service providers (CSPs) has been in a state of flux. The past few decades have seen unprecedented changes driven by evolving market demands and regulations. As digital networks usher in a dynamic era of convergence, the need to revise business strategies and operating models is becoming increasingly apparent.
For fixed line and mobile network carriers and operators, the pressure to retain the existing customer base has been especially high as new players have begun to erode their market share, leaving little room for improving margins or services. Dipping revenue per user, increasing data traffic, and customers’ growing expectations around service quality further complicate the scenario. Major network investments are inevitable in this tough environment to leverage the power of convergence, better handle network expansion and control, and ensure enhanced quality of service (QOS).
As CSPs scour different alternatives to improve ROI, network analytics is emerging as a powerful tool to drive informed decision making in this challenging environment. Network analytics is critical to a CSP’s network strategy as it enables robust planning for future expansions and upgrades through data-driven insights into customer expectations and network service, while ensuring cost efficiency.
Getting proactive with Network Analytics and Artificial Intelligence
Currently, most CSPs leverage several disparate systems and tap into their traditional knowledge base for activities such as root cause identification, trouble ticket creation, and escalation. This increases response time and reduces efficiencies.
Moreover, traditional service assurance measures tend to be reactive in nature—actions such as troubleshooting and diagnosis begin after a certain problem has occurred. A service technician grapples with multiple types of data loads from different network systems in the aftermath of an incident, impacting turn around time and related functional metrics such as service availability and customer satisfaction.
Using network analytics can improve the overall efficiency. An AI engine collects the network logs and clients can notify the engine of significant events at the access network. Information about circuit details, provisioning logs, and inventory utilization data are then aggregated to formulate a unified view. Neural intelligence prediction models assist in anticipating faults and analyzing the impact of failed predictions on service delivery and customer experience. They also help correlate the impact with the operations and business support systems (OSS and BSS respectively) of CSPs.
Searches based on text corpus, not just key words, help support conversations in natural language for a more human advisor-like experience. This helps users quickly identify the right solution based on the various sources of structured and unstructured information. What’s more, the solution continuously self-learns from these interactions and is able to build superior domain expertise over time to support automated resolution of problems where applicable.
Proactive approach to fault identification and resolution is the way forward to significantly improve QOS—the primary goal of any network service provider today. The scaling up of OSS functions against a backdrop of continuously evolving network operations can be achieved by using artificial intelligence (AI)-based models. The models enable the development of efficient networks ensuring high uptime, and connect multiple facets of service delivery ranging from fault propensity to proactive remedial service, positively impacting customer experience. Neural prediction algorithms help CSPs achieve higher prediction accuracy and an end-to-end view of the linkage between the network, devices, and services, enabling superior scalability. In essence, this model enables CSPs to gain critical competitive edge by creating a truly customer-centric business model based on proactive measures.
Cognitive computing: The future of network analytics
That network analytics is critical to CSP operations today is beyond debate. But the future will require higher speed and scale of analytics and that is where cognitive capabilities can be a key differentiator. Cognitive computing not only helps solve problems that traditional analytics can’t, it can also help automate decision-making to reduce operational costs and missed SLAs, besides improving network and service availability. Problem resolution becomes easier with improved turnaround time, overall efficiency and customer satisfaction.
Clearly, deploying intelligent systems that understand the current and upcoming trends, coupled with heightened emphasis on security of all involved systems, will be critical to future success for CSPs. Such systems enable real time status monitoring and address the core customer need for an interactive interface that can seamlessly and securely operate across multiple platforms and devices —without any space or time constraints. The result: exceptional QOS that leads to several cross-selling and up-selling opportunities and use cases such as personalization, fraud detection and more.