AIOps: the key to unlocking superior in-home quality of experience

Communications service providers (CSPs) face the intricate task of managing home networks that accommodate an escalating number of IP-connected devices. These networks, often a complex subnet of Wi-Fi extenders, mesh hardware, and personal area networks, will come under pressure to support the growing demand for new applications such as AR/VR and cloud gaming. 

CSPs are struggling to meet these demands. Consumer behaviour is creating bottlenecks in home network performance, particularly among gamers who have a heightened awareness of quality-of-service (QoS) metrics. This presents an opportunity for monetisation, but also a challenge in improving customer perception of the speed and reliability of a service – both factors that have a direct impact on customers’ overall satisfaction with their service and intention to churn, as highlighted by our primary research. 

Data from Analysys Mason’s 3Q 2023 survey of 18 000 consumers worldwide reveals that engagement with cloud gaming and interest in XR technologies are growing. Both of these services are exerting additional demands on in-home connectivity. AI for operations (AIOps) can potentially help. It represents a consolidated data-driven approach to automating operations using analytics and machine learning (ML) techniques applied to real-time and historical data.

The key findings from the research are as follows.

• CSPs’ customers are not all happy with their broadband connectivity. However, there are clear levers that CSPs can pull to improve customer experience (CX) and reduce churn: small improvements to how happy customers are with the speed or reliability of their service can have a positive effect on Net Promotor Scores (NPSs) and can reduce customers’ intention to churn.
• Implementing AIOps can enhance CX, operational efficiency and cost optimisation. It can improve KPIs such as first contact resolution (FCR) and mean time to repair (MTTR), leading to fewer site visits and truck rolls, opex reduction and happier customers.
• AIOps can provide relevant insights from highly complicated models that represent the relationships between different and varying data sets. These insights can be fed to operations support systems (OSS) such as service management systems or self-care management systems to provide just-in-time information to CSP operations and customer care agents and customers.
• AIOps can be combined with GenAI capabilities to augment existing operations’ team functions, improving productivity and efficiency, enabling CSPs to deliver better experiences to customers.
• The implementation of AIOps can deliver commercial benefits. CSP marketing and sales teams can leverage insights and recommendations gleaned from AI models to target and upsell new services and devices to customers, while network operations teams can reduce costs.
• CSPs such as Fujian Mobile (FJMCC) are using AIOps to transform fixed broadband operations and have recorded tangible results from these implementations. FJMCC has achieved a 53.5% improvement in customer satisfaction score and a significant reduction in MTTR, while another CSPs based in Asia–Pacific saved EUR6.5 million in network operations-related opex.