enterprisenetworkingmag

Run Smarter, More Efficient Networks with Analytics, Machine Learning, and Automation

By Brendan Gill, CEO, OpenSignal

Brendan Gill, CEO, OpenSignal

Machine Learning and Artificial Intelligence (AI) will change virtually every industry. Smart tools are penetrating the hardware and software used by organizations around the world, helping them to make better decisions and automate processes. Communications Service Providers (CSPs) are no exception. Automated, data-driven decisions can help manage large estates of network infrastructure and optimize services. And any efficiency gained can have a huge impact on the bottom line. These digital transformation megatrends have the ability to help CSPs run better, smarter networks, and more efficient services that keep customers happy, reduce costs and open up new market opportunities.

Smart networks

In today’s 24/7 connected world, consumers expect always-on service. Even the shortest service interruptions can create customer backlash that negatively impacts brand reputation. Take over the top (OTT) services, which consumers increasingly rely on to communication and collaborate. Any infrastructure failure can be catastrophic and seriously damage credibility. Equally, if the network consumers use to access these services fails, then the service provider often bears the brunt of customer negativity – as social media can testify. With so many of these services accessed on a mobile device, smart networking technology can give mobile operators the power to improve service reliability and deliver a better customer experience.

"Automated, data-driven decisions can help manage large estates of network infrastructure and optimize services"

Data modeling can predict potential demand surges, such as a major sporting event, and recommend deploying additional network capacity. Additionally, Self-Optimizing Networks (SON) analyzes call, text and data quality and makes remote adjustments using equipment such as tilting antennas. CSPs can train machine learning algorithms using historical and real time data to predict infrastructure failures, such as power outages or transmission issues, before they happen. Symptoms such as unexpected packet loss could lead the model to issue an alert to maintenance teams who can sort the problem before customers notice anything at all. AI-powered Software Defined Networking (SDN) can dynamically allocate more bandwidth to a customer that requires additional bandwidth during exceptional times of the year. For live sports broadcasting, not having sufficient capacity to upload content and distribute it to viewers could genuinely be a business-destroying disaster.

Service improvements

AI and data analytics can also boost frontline services. OTT streaming services already use machine learning algorithms to recommend new content to customers, encouraging them to consume and maximize the benefit of their subscription. Similarly, AI can analyze customer relationships to spot early signs of discontent and take pre-emptive action to stop attrition or to launch highly personalized retention campaigns. AI-powered chatbots are a simple, cost-effective way of handling common queries. They can also redirect more complex issues to a call centre. While many CSPs have outsourced their contact centres, 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 behavior, to match people to the most-suited agent, boosting satisfaction and reducing call times.

Some start-ups are looking at how AI can be used to augment the human-led customer service approach, such as offering automated suggestions. This can speed up resolution times while also retaining the ‘human touch’ that some customers prefer.

Quality of data

Machine learning models are only as good as the data that trains them. CSPs have treasure troves of information to draw on but these traditional ‘network-centric’ metrics have their limitations. If the goal is to improve customer experience, then the machine learning model should be trained with a ‘customer-centric’ perspective. The only way to do that is to base it on actual customer experience data. Network-related performance data may suggest that an individual customer is receiving the optimal level of service. However, CSP’s can’t see how buildings, Wi-Fi quality and congestion from other users in the same location are impacting experience. This data is missing the true customer centric picture. That’s where analyzing billions of measurements collected from actual users, as OpenSignal does, can help close the data gap and give CSP’s an edge in building AI that maximally impacts customer experience. The real competitive advantage for CSPs 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. CSPs 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 in closing customer-centric data gaps will be the real winners in the age of AI.

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