When a customer has a problem with a service, it’s usually down to them to report it. They call, email, text or use social media to contact customer service, setting off a chain of events that hopefully ends with the issue being solved – though they may have to waste time stuck in a call queueing system or chasing the company for updates.
This is what’s known as reactive customer service. And in a world where people expect quick resolutions to their problems through their channel of choice, it can be a source of frustration and negative brand perceptions. But what if companies could solve problems before customers even knew they existed?
That’s the promise of AI and machine learning, which are changing the way businesses approach customer service issues. “We talk about [the shift from] reactive customer service to proactive customer service to predictive, so there’s a logical flow there,” says Ian Ashby, principal strategist for customer service at ServiceNow, a leading digital workflow company.
AI and machine learning tools are fuelled by data, so sectors that operate digitally connected ecosystems of sensors and devices – eg telecoms, energy and IT – are at an advantage when it comes to shifting to more proactive and predictive customer service.
Ashby uses the example of a wind farm to illustrate this point. “Say your sensors are telling you that the bearings are vibrating or the temperature is increasing on the engine casing,” he says. “There are various things you can do off the back of that …[like] schedule a preventative maintenance visit at a time that’s convenient.”
ServiceNow’s Customer Service Management (CSM) solution enables operations teams to keep customer service in the loop when there’s an issue. For example, if the company’s AI-enabled monitoring systems pick up a trend or multiple alerts that indicate a high chance of future service disruption, the CSM solution can automatically generate a case and associated workflows for the customer service teams to take action. That team can then immediately identify which customers might be affected by the disruption and notify them if necessary.
Without this level of visibility, organisations may struggle to provide truly proactive and predictive customer service. This was an issue Vodafone faced before it implemented the ServiceNow customer service solution on the Now Platform.
“Before digital transformation, Vodafone was a collection of siloed systems, multiple platforms,” stated Chris Holmes in a ServiceNow video, who at the time was head of digital experience at Vodafone. “It was a really complex environment. What that led to was quite often the customer would know about a problem before we would. Now with the ServiceNow platform we’re much more proactive. We’re aware and we’re notifying our customers of an issue and that we’re dealing with it.”
The move to predictive customer service makes it possible for companies to fix problems faster. Using the example of the automatic response to broadband outage, Ashby says: “If it [the outage] comes in at two o’clock in the morning, you can start working on it immediately. You can schedule the engineer to go out, you can inform the customer that it’s expected to be down for three hours, and give them an update when it’s finished.”
Granted, no technology is a silver bullet for every customer service issue – and that includes AI and machine learning. “Without a deep understanding of the end-to-end customer service journey, technology is a shot in the dark: it might bring some relief to an isolated pain point, but it won’t solve its root cause,” says Karine Cardona-Smits, senior analyst at Forrester and co-author of the report How AI and automation drive better customer service experiences.
A proactive or predictive approach to customer service also means companies need to be comfortable telling their customers if there’s a problem. “My take on that is that you should, because not only are you being open and honest, you’re also avoiding a situation where they [the customer] are having to put in the effort to find out what is going on,” says Ashby.
He believes a text that informs someone that an issue has been identified and is being addressed creates a sense of gratitude rather than frustration. From the business’s perspective, it’s also a lot more straightforward and cost-effective than the alternative: customer service agents fielding endless calls from confused (and possibly irate) customers.
“The last thing agents want is people ringing up going: ‘My broadband is down, I’m stressed, I’ve got a meeting in three hours,’” says Ashby. “Those are really high-stress situations for the agents because they’re dealing with angry customers. So there’s an added benefit in terms of the wellbeing and the job satisfaction of the contact centre agents, because you’re taking away a substantial amount of the difficult conversations that occur in that scenario.”
Reducing the number of stressed customers that call centre agents have to deal with not only makes their job more pleasant, it also means they can focus on more high-value or complex customer service issues. “The ideal would be that customer service processes were designed so humans can do what they do best and AI does what it does best,” says Ian Jacobs, vice-president, research director at Forrester and co-author of the aforementioned report on AI and customer service experiences. “The service would be a blend of the two.”
Want to learn more about the future of customer service? Visit servicenow.co.uk
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