Data and AI: the key to improving network reliability and managing customer satisfaction

We all rely on broadband connectivity in our everyday lives, yet we so easily take it for granted. Whether it’s using social media to keep up with family and friends, or online shopping, banking, booking a table at our favourite restaurant, or even travelling by plane, Uber or train - we often don’t realise that this connectivity underpins so much of what we do. For businesses, it’s even more critical.

With short supply chains, a company can literally grind to a halt within days if communications with suppliers and customers is lost.  We probably don’t even think about the operator that provides our fixed and mobile connectivity, until it stops working. Then we want it sorted. Immediately.

This is a huge challenge for operators. When things go wrong, we as customers expect them to know exactly what the problem is when we contact them - and to be able to tell us exactly when it will be fixed. The broadband or mobile provider may not always have the answers to those questions, and that can be costly for them.

They might even lose me as a customer if they can’t fix the fault quickly, and losing one customer can lead to reputational decline, as they will tell their friends about their poor experience, and more may walk as a result. This is measured by Net Promoter Score (NPS) – the more positive my experience, the more I want to share that experience with others, but if my experience is bad, that has a negative effect which will also rub off on others.

So how can providers stay one step ahead to keep their customers satisfied?

Firstly, they need to understand what the network is telling them. Network elements will often start to raise low priority alerts when things start to go wrong (congestion, intermittent faults, a re-route around an issue perhaps), which may not be picked up immediately by the team monitoring the network, as they are not service-affecting. What the team may not be aware of is that these could be warning signs that problems are starting to occur. With the right tools, these weak signals can be amplified to shine a light on the underlying issue.

This is where data also has an impact. The vast amount of real-time data from the network allows operators to quickly separate the signal from the noise, using Machine Learning (ML) and Artificial Intelligence (AI) capabilities to distinguish hidden patterns embedded in the signal to then identify signs that what is currently being observed, simply isn’t what is expected. The fusion of such cutting-edge approaches enables pin-point accurate diagnostics, allowing work to be scheduled to quickly fix it before it becomes an issue for customers.

This data-driven, proactive maintenance approach is much more cost effective than the traditional reactive approach because the repair work can be scheduled to better fit the workforce, making it much cheaper than having to respond to an emergency.

Fixing issues quickly can mean the difference between a quiet day in the contact centre, and the centre being overwhelmed with multiple unhappy customers getting in touch about the same underlying fault. The very best organisations share data transparently between the network, IT and customer service teams, so the right decisions can be made for the customer in a timely manner.  

Next time my broadband stops working, I want to be confident that the provider knows about it before I do and is already putting a fix in place.  Will my provider be able to deliver on that expectation?

At CGI, we understand the power of a data-driven approach to solving complex business challenges, which can lead to improved customer outcomes, better services and stronger revenues. Wherever you are on your data journey, we work in partnership with you to identify priority areas, leveraging our expertise in ML and AI to understand and unlock the value held in this strategic asset.

To find out more about our capabilities, visit our Artificial Intelligence, Advanced Analytics and Communications pages.