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Machine Learning in Telecom: Examples for IT Teams

Telecom networks process terabytes of data, and we depend on their ability to maintain uptime. Machine learning offers a path to manage the deluge of data.

With rapidly changing telecom industry scenarios, telecom industry leaders are facing many new challenges. These include managing the massive volume of data, network optimization, and fraud detection. Machine learning in telecom enables companies to manage tons of invaluable information. This can improve the efficiency of marketing efforts, boost sales, improve retention, and generally lead to an increase in revenue.

Artificial intelligence (AI) is booming as an essential part of any industry, particularly the telecom industry as it handles tons of extremely valuable data. Also, in this data-driven and competitive world, there is no space for guesswork. Machine Learning (a branch of artificial intelligence) can be used to generate predictions and decisions based on previous data. This makes it beneficial to optimize, disrupt and innovate.

ML is boosting innovation in telecom, providing opportunities for new ideas to accelerate digital transformation. These technologies will not only lead to improving overall efficiencies, but also to increased revenue and improved margins. To reach revenue goals, every telecom leader should focus on use cases across all aspects of their business, build common data infrastructure, and integrate ML into workflows and processes.

Let’s explore what is driving the adoption of Machine Learning by telecom companies. Has ML proven itself essential to these enterprises’ digital acceleration strategy? How does it address the key challenges they face today?

Has Machine learning in telecom created a new path to predict, prevent, and manage?

Telecom enterprises are investing in opportunities by leveraging the terabytes of data collected year over year from their customer bases. A more intelligent and automated environment will increase margins and increase customer satisfaction as well. Here’s a few key use cases and why they matter.

Use case 1: Network monitoring and optimization

Network monitoring and optimization give a chance to identify the root causes of any complications in telecom operations. Machine Learning is crucial in supporting communication providers to build self-optimizing networks. These enable them to automatically optimize network quality based on traffic information by region and time zone. Inspecting the past data and predicting or identifying possible future problems or beneficial scenarios is an advantage for the telecom service providers.

Use case 2: Smart ticket routing and automated resolution

Machine Learning can help accelerate customer service workflows by automating routine tasks like ticket routing. Ticket routing tags incoming tickets and assigns them to team members best equipped to handle them. Using machine learning for ticket routing is more objective and precise than manual tickets, since these AI/ML tools apply the same criteria to all tickets without any hesitation. This prevents errors, inconsistencies and handles tickets at scale. Once you implement Machine Learning in Telecom, you’ll be able to analyze tickets in real-time with faster resolution.

Use case 3: Fraud detection and churn prediction

Telecoms are harnessing powerful Machine Learning analytical capabilities to combat fraud. Machine Learning algorithms can detect anomalies in real-time. Thus efficiently reducing telecom-related fraudulent activities, such as unauthorized network access and fake profiles. The system can automatically deny access to the swindler as soon as suspicious activity is detected, minimizing the damage.

Machine Learning in Telecom: Challenges and Obstacles

Machine Learning solutions are ample to solve various problems in the telecom industry. However, the real challenge lies in implementing Machine Learning in networking. The problems include privacy issues and network issues. Let’s take a deeper dive on the ML problems in the telecom sector.

Complexity of Business Process

Usually, new business processes get more complex with the high influx of data. As the data flows at high velocity, processing them or extracting information out of them becomes significantly complex. It is impossible to handle this enormous amount of data with only human computing abilities. And once data is breached, businesses start losing their growth, revenue, and customers.

Lack of Appropriate Tools

A significant challenge in ML implementation is the lack of tools in the telecom sector. Over the past decade, telecoms have used some legacy tools. These can improve the functionality to solve only a particular set of problems. But, the growth of emerging technologies demands new tools to increase productivity and prevention. These new tools can be modified considering the fact we have made significant progress in understanding the fundamentals of ML applications.

Network Management Implications

Managing the complexity of huge networks is overwhelming for network technicians. Implementing ML in Telecom provides a great opportunity to address service issues, reduce manual tasks and improve efficiency, which ultimately funnels resources towards creating a better, more customized, and smoother customer experience. This is the ultimate objective of an adaptive network utilizing Machine Learning.

An Unrelenting belief in the power of AI

Machine Learning transforms how the telecommunication industry operates. Telecom leaders want to find a way to leverage these technologies to enhance the customer experience, enable self-service, improve equipment maintenance, and reduce operational costs at the same time.

Windward has experience implementing Machine Learning in Telecom seamlessly. For us it’s more than an implementation process; it’s an unrelenting belief in the power of AI. Talk with our team members about starting your journey to AIOps.

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