Telecoms executives expect AI / ML to have a huge impact on customer experience and customer service, according to a new report by the TM Forum. This even will be more than its potential to transform network operations. Based on a survey the TM Forum conducted of 104 executives across 73 operators, 87% of respondents said they expect to gain very high returns in the customer experience and customer service domains, and 14% expect medium returns. This raises the question as to how AI / ML will be applied to customer service, as operators shift from digital to intelligent operation.
The aim is to make customer service both effortless and efficient. According to Gartner research, reducing customer effort is the single most important thing operators can do to increase customer satisfaction and loyalty.
Within the context of customer service, delivering an effortless experience means providing easy-to-access support in a customer’s channel(s) of choice, removing the need to repeat information, reducing wait times and resolving customer problems faster. AI / ML play important roles here by reducing customer effort at scale, smoothing customer journeys, speeding up resolution and helping deliver a less stressful and more personalized experience.
When executed effectively, AI / ML have the potential to advance even further – by helping operators move towards proactive and preventative care. In other words, fixing problems, even before the customer has a chance to complain, by reaching out to them proactively.
To illustrate how AI / ML is being applied to customer care, let’s explore how it can be used to reduce one of the major causes of customer frustration – long wait times for customer service.
1. Use AI / ML to empower zero-touch self-service
The first key factor in providing intelligent customer support is to use AI / ML to empower smarter digital self-service. One of Subtonomy’s customers discovered that between digital self-service and AI-assisted IVR, 75% of queries and problems could be fully resolved without any human assistance. This not only meets customers’ needs for zero queuing and instant answers, but frees up agents to handle more complex queries or support customers who really need the human engagement.
2. Increase your customer service agents’ efficiency through AI tools
Customers are more impatient than ever. Subtonomy research has revealed that the average customer is only willing to wait 7 minutes on hold before becoming frustrated. Subtonomy SubSearch helps reduce queue times by boosting agents’ efficiency. It provides a 360˚ customer-centric service view to support agents, along with insight into all the actions a customer has previously taken – including speed tests or troubleshooting activities. Agents can instantly see what the problem is with the customer’s network experience or device performance via color-coded and clear text messages, while next-best-actions are provided to further speed resolution. All of which enable a precise and transparent interaction with customers and faster resolution of problems – with greater efficiency of resolution reducing queue times for customer service.
3. Introduce AI-empowered active queuing
A considerable amount of customer frustration can be removed by changing queuing from a passive to an active experience – using similar technology to that found in self-service channels to transform the queue experience itself. Active queuing uses chatbot technology to gather essential data (such as customer ID or the nature of the inquiry) which keeps customers engaged and prevents them from getting bored and frustrated, while simultaneously ensuring that time isn’t wasted on gathering this information during the call itself. Similarly, simple fixes (including restarting routers and checking the performance of handsets), automated checks, or providing answers to common customer queries can resolve many inquiries before the customer is even connected to an agent – removing the need to continue queuing.