Artificial intelligence. The term tends to conjure up visions of computers taking over the world: 2001’s HAL, Skynet in the Terminator movie series, and many others. But Artificial intelligence and its related concept, machine learning, can have many practical applications today, and global domination is not one of them.
Machine learning has been around for many years but today’s machine learning is fundamentally different. In the mid nineties machine learning changed from being knowledge-driven to being data-driven. Machine learning programs were developed that could analyse large amounts of data and draw conclusions — or ‘learn’ — from the results.
The current view is well expressed by computer software company SAS Institute, whose web site says: “The iterative aspect of machine learning is important because as models are exposed to new data, they are able to independently adapt. They learn from previous computations to produce reliable, repeatable decisions and results. It’s a science that’s not new – but one that’s gaining fresh momentum.”
This means that a machine learning system can gain knowledge about any subject, and become more useful the more data it can be fed.
If you need convincing that this topic is hot just put the phrases "machine learning" and "customer service" into a Google search: you’ll get in excess of one million hits.
Global telecoms equipment maker Nokia announced in November two machine language customer experience solutions for telcos that it said used advanced machine learning algorithms, developed by world-renowned Bell Labs (now part of Nokia). They are telco service provider specific, but they well illustrate how machine learning can be used to improve the customer experience, and create efficiencies.
One is described as “a new self-optimising system that determines the ideal sequence of tasks that deliver the highest probability of resolving billing, subscription and network service issues in the shortest amount of time.”
Nokia claims that, by analysing data from previous workflow executions, the network, customer premises equipment and trouble tickets, the technology “enables service providers to quickly find the optimal remediation to issues when subscribers contact help desk agents or use self-care.”
The other “automatically correlates customer help desk calls and self-care actions with network, service and third-party application topologies to identify call anomalies, such as unusual patterns in help desk calls that indicate the location of customer-impacting network and service issues.”
In combination Nokia claims these two can reduce average help desk handling times by five to 15 percent and eliminate unnecessary truck rolls (dispatching a service technician to a customer location) related to network outages by as much as 90 percent, and eliminate 85 percent of outage-related help desk calls.
If those claims are true, it clearly won’t be long before machine learning starts to make significant inroads into the wider customer service market.
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