Using GenAI to Improve Customer Experience
In today’s increasingly competitive environment, a focus on improving customer experience can set operators apart. Incorporating GenAI can help.
In saturated markets where customer acquisition costs are high and customer loyalty is low, providing easy access to service, network assurance and reliable performance are key ways to improve customer satisfaction.
Easy Access to Service
GenAI with large language models (LLMs) can be a game changer for customer interactions, allowing AI agents to handle a huge proportion of incoming customer tasks and requests, as well as AI assistants to help customer service representatives. Implementing this functionality can improve productivity by reducing call duration, handling time and case volume.
Chatbots powered by GenAI models can answer customer requests in any language in a natural and intuitive manner, and if they cannot resolve an issue they can push a summary of their interaction to an AI-assisted customer service representative. GenAI makes that first all-important interaction short, concise and painless.
While there may be some concern about replacing human interaction with GenAI, many consumers are already favoring this technology. With this in mind, GenAI can drive improvements in both productivity and customer satisfaction, provided that when requests become complex there can be a smooth transition to a human agent. GenAI can also make it much easier to create and modify workflows, further increasing productivity.
Major operators have already begun implementing this technology, including T-Mobile Wholesale, which announced in early 2024 that it was evolving and modernizing its wholesale BSS system as part of the multi-year program. This modernization includes the use of the Netcracker GenAI Telco Solution to improve interactions in the customer interface and ultimately improve customer experience.
Network Assurance and Reliable Performance
Over the past few years, it has become clear that maintaining connectivity is crucial to how we live, work and interact with each other. With more people working from home, losing fixed or mobile connectivity has a significant impact, so the ability to assure networks is vital. However, the ability to assure networks and understand the underlying intent is even more important, and this is where AI is needed.
Intent is predominantly about understanding what customers are trying to do over the networks and then making decisions about the management of resources, capacity and service delivery. In a network where capacity is finite, AI can help determine what to allow, what to block and what to throttle if an issue arises.
When a component in the network fails, that failure can generate multiple alarms from multiple monitoring systems, so a key requirement is to collate and then correlate those alarms. AI can be used to determine the root cause and impact of that failure, whether it is a redundant component or risk of service interruption.
Once the issue and impact have been determined, AI can be used to implement solutions either with human intervention (open-loop assurance) or automatically (closed loop assurance). Service providers are increasingly implementing closed-loop assurance for certain common faults and failures, and using human network operations teams to manage and implement solutions for more complex failures.
AIOps, the ability to monitor, collate, analyze, assess impact and implement changes to the network improves overall performance by preventing minor issues escalating, and also reducing the duration of any outages. It can also be used for predictive analytics, which helps to understand and mitigate future problems.