How can machine learning and artificial intelligence help operators succeed?

There is a great deal of talk at the moment about the wonders of machine learning and artificial intelligence (AI), with predictions ranging from the fantastic to the apocalyptic. But among all the hype, where are the real-world opportunities and challenges for operators in analytics, machine learning and AI automation?

OpenSignal's CEO Brendan Gill recently moderated a panel including these speakers: Rajeev Chandrasekharan, CIO of Verizon Business Markets; Dahn Tamir, CIO of VOLTN; and Mary Clark, CMO and EVP of Product, Synchronoss Technologies. The panelists discussed "Maintaining A Top-Notch Service with AI" at the TM Forum Digital Transformation World event in Nice, France. Ahead of the event, he shared his thoughts on the topic in this TM Forum Inform article on AI in the telecoms sector.

Photo by OpenSignal Photo by OpenSignal

Brendan states that AI and machine learning are already beginning to penetrate the hardware and software used by organizations around the globe, helping them to make better decisions and automate processes – and telecoms operators are no exception:

"Operators manage large estates of network infrastructure and service teams; any efficiencies gained can have a huge impact on the bottom line.
These digital transformation megatrends can help operators run better, build smarter networks that keep customers happy, reduce costs and open up new market opportunities."

One of the areas of AI already being implemented in the operator sector is in customer service, where it can be used to analyze customer relationships to spot early signs of discontent. Brendan says this will be invaluable to stop churn by launching highly personalized retention campaigns, beginning by managing the customer relationship in the right way:

"AI-powered chatbots are a simple, cost-effective way of handling common queries. They can also redirect more complex issues to a call center.
While many operators have outsourced their contact centers, some have repatriated these services to bring agents closer to customers. Potential increased costs can be offset by services that analyze data, such as customer history, credit scores and social media behaviour, to match people to the most-suited agent, boosting satisfaction and reducing call times."

But all effective machine learning and AI must be built on on good data. Operators often have large troves of consumer data stored away, but the challenge is to make this data accessible, focused and relevant. For example, if the goal is to improve customer experience, then the machine learning model should be trained with a ‘customer-centric’ perspective. Brendan concludes:

"The real competitive advantage for operators using AI comes not from creating a better machine learning algorithm, but in using the most relevant and extensive data to train the AI engine. Operators will never be Google DeepMind, but they do have access to unique and comprehensive datasets. Those who focus on unlocking the value in that data they have access to, and on closing customer-centric data gaps will be the real winners in the age of AI."

Be sure to read the full column in TM Forum Inform, and let us know us know your thoughts in the comments section below.