Nokia: The AI-Driven Rise Of Predictive Customer Service

There are few experiences more miserable for both parties than trying to resolve a technical problem with a service over the telephone.

The customer, already frustrated that their service is not working properly, gets to struggle to describe the exact nature of their problem. The customer or technical care representative on the other end of the phone gets to try to resolve that frustration by making the best diagnosis possible off of problem descriptions like “the picture is blurry” or “the internet is going too slow.”

Not only does this experience do little to please or delight its participants, Nokia’s Head of OSS Marketing Rich Crowe told Karen Webster in a recent conversation — it is bad for the bottom line as well.

“The cost of actually operating a call center continues to expand as the agents are getting involved in more and more complex use cases. Obviously, the goal is to arrest those costs and reduce them if at all possible, especially if they can do it by being more accurate and reduce the number of calls to the center.”

Because the cost of inaccuracy is high — it means a service truck will be rolled out to send a technician — even for problems that could have been solved remotely.

“I think we’ve all had the conversation where you try something with customer service. It doesn’t work and they say, ‘Well, why don’t we do a swap on your device.’ And they do, and it works — but when they get the devices back, there is absolutely no fault found with it 40 percent of the time,” Crowe said.

The consumer isn’t lying or crazy, Crowe noted — there was something wrong with their device, but it just wasn’t a hardware issue; it was likely a setting issue that the rep couldn't “see” over the phone. And making the customer wade through a service call and possibly wait for a service visit and an equipment change also comes with a cost — the cost of an unhappy customer having an experience totally out of line with their expectations for service.

“What service providers are competing [with] and being compared to are web scale companies, and every customer knows: your apps and web services just work. That is becoming the new expectation. If my web services just work, why can’t my communications with my service provider just work?”

But, as it turns out, they can just work, according to Crowe — with a little bit of a technical assist for the team and Nokia, which has recent unveiled a new host of machine learning capabilities and expertise to help service providers strengthen the digital customer experience.

Zero Touch Care

The goal, Crowe noted, isn’t to find ways to merely fix customer problems better or faster, it is bigger than that. The real goal, he noted, is to find and fix what could be a problem long before the consumer ever sees that it is there and has to make a call.

That, he noted, is the vision behind Nokia’s Autonomous Customer Care — a smart system that monitors itself and takes the consumer’s place as the official QA monitor.

“This requires nothing from the consumer — that would defeat the purpose of no-touch care ... instead, all the magic is on the service provider side. And we offer three things: we connect at the service level, the device level and offer a workflow engine to find the best route to a solution.”

The smart AI behind Autonomous Care combines the data it pulls from the devices and services it monitors and runs them through that workflow engine to predict and resolve service-impacting issues before they happen — and take corrective action before they impact customers.

It also was built to recognize natural human speech, meaning it can also interface with AI assistants like Siri, Alexa, Cortana and Google Assistant, as well as social platforms like Messenger — meaning subscribers can use natural language to troubleshoot and request services without ever having to phone customer service.

And though the Autonomous Care will have devices making decisions for customers, Crowe noted, they will mostly be technical decisions meant to optimize function — not decisions that would affect things like billing or payments, which would require a living human’s intervention and permission.

“There is nothing private we can see into — billing information or your monthly service plan, for example. We might be able to see that you have a pixelation problem and that this is not the result of technical failure, but because your router is only supposed to ... get so much data per month, not because there is a technical failure.”

What Nokia knows by monitoring the channels, however, is that its service provider partners tend to up sell to a customer who clearly needs a more robust data package.

And, Crowe noted, the ability to hand partners data that allow them to better build their services is a lot of what drives another of its recent big analytics offerings: Nokia Cognitive Analytics for Crowd Insight.

Following the Patterns

Much of knowing what a consumer will do — or what they want or need — can be gleaned from knowing something else: namely where they are. Which led to the development of Cognitive Analytics for Crowd Insight, which uses machine learning algorithms to track and analyze the movement of subscribers using real-time network data instead of GPS or application data.

“We use the constant stream of data available on the network on where people are and how they are moving, which updates more often and offers larger sample sizes than GPS or app data. That is data that is anonymized, so we don’t know any specific person, but we can see movement in crowds.”

And that movement — and knowledge about what it means — becomes monetizable by allowing service providers to operationalize their data, such as helping retailers identify the best high-traffic areas for new stores; allowing municipalities to identify the optimal location for a bus stop or helping advertisers determine the appropriate content for digital billboards based on subscriber travel patterns at any given period in time.

The goal, Crowe noted, is all of a piece: help service providers know their customers and use that knowledge to service those customers better.

“The biggest challenge in the digital age for service providers isn’t the technology, it is the consumer expectations and the fact that it is harder to keep consumers happy.”

That is the bad news. The good news, he said, is that the technology for doing so is getting better.



The How We Shop Report, a PYMNTS collaboration with PayPal, aims to understand how consumers of all ages and incomes are shifting to shopping and paying online in the midst of the COVID-19 pandemic. Our research builds on a series of studies conducted since March, surveying more than 16,000 consumers on how their shopping habits and payments preferences are changing as the crisis continues. This report focuses on our latest survey of 2,163 respondents and examines how their increased appetite for online commerce and digital touchless methods, such as QR codes, contactless cards and digital wallets, is poised to shape the post-pandemic economy.

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