Automated optimization of customer QoE
Service providers are under pressure to simultaneously maintain their quality premium versus “over the top” competitors and dramatically reduce their operational expenses (OPEX). Key candidates for OPEX reduction are labour intensive areas like network operations and customer care, but cutbacks in such groups are historically associated with quality problems and customer dissatisfaction. Achieving these historically competing objectives will require new technologies and business processes.
This catalyst is specifically focused on the role automation can play in detecting, troubleshooting, and resolving issues affecting the experience of subscribers on the network. Our goal is to define and characterize use cases that can benefit from automation, the logical architecture and APIs between components (customer experience assurance, orchestration, machine learning, etc.), and best practices for implementation. The team will also demonstrate an example of how one use case was implemented using these technologies and processes.