According to a new report by the TM Forum, telecoms executives expect AI / ML to have a huge impact on customer experience and customer service – even 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. Which begs the question as to how AI / ML will be applied to customer service, as operators shift from digital to intelligent operation.
“Quite simply these technologies are being deployed to solve many of the intractable problems that operators have struggled with for years,” says Teresa Cottam, Chief Analyst of Omnisperience and author of a TM Forum report on AI in customer service. “By joining up the network and customer service domains, AI / ML will help deliver the type of effortless customer support journey that customers now expect,” she says.
The aim is to make customer service both efficient and effortless as, according to Gartner research, reducing customer effort is the single most important thing operators can do to increase customer satisfaction and loyalty.
In the customer service context, delivering an effortless experience means providing easy-to-access support in a customer’s channel(s) of choice, reducing wait times, removing the need to repeat information 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.
Done well, AI / ML have the potential to go a step 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 look at how it can be used to reduce one of the biggest causes of customer frustration – long wait times for customer service.
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Use AI / ML to empower zero-touch self-service
The first vital element of 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 instant answers and zero queuing, but frees up agents to handle more complex queries or to support customers who really need the human touch.
Find out more about Subtonomy’s new AI-Powered Chatbot support here.
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Increase your customer service agents' efficiency through AI tools
With customers 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 troubleshooting activities or speed tests. 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 an accurate and transparent dialogue with customers and faster resolution of problems – with greater efficiency of resolution reducing queue times for customer service.
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Introduce AI-empowered active queueing
A huge amount of customer frustration can be removed by changing queuing from a passive to an active experience – using similar technology to that employed 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, automated checks, simple fixes (including restarting routers and checking the performance of handsets), 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.
Subtonomy’s Andreas Jörbeck explains: “Smarter, active queuing effectively blends the digital and call center channels. Using this approach many calls can be fully resolved in the queue, but even for those customers who still need human assistance we can speed the time required to resolve their problems, while engaging them to distract from their wait time.”
In our next post discover more ways in which AI / ML is empowering intelligent customer service.