5 Applications of Machine Learning in Telecoms

Machine learning is growing fast, and for all the good reasons; Google is using it to improve user experience, hospitals are using it to improve their healthcare services, and basically, every sector is using it to improve their systems. But then the one of the most interesting uses of machine leaning is in the Telecom industry.

What is Machine learning?

Machine learning is a subfield of artificial intelligence and computer science that allows software applications to be more accurate in predicting results. The prime objective of machine learning technology is to build algorithms that can get input data and leverage statistical analysis to predict an acceptable output value. There are two primary branches of machine learning; supervised learning and unsupervised learning.

Supervised learning: In this a computer is provided example data with defined inputs and outputs, with the goal of learning a general rule that maps these inputs to the output options.

Unsupervised learning: In this learning a computer is provided data, but without any defined inputs and outputs to help learn a general rule. Instead, the computers goal is to discover hidden patterns independently.

Further into the article, we take a look at some of the applications of machine learning that are beginning to emerge within the telecoms sector.

Machine Learning in the Telecom Industry

The telecoms industries are specially prepared for machine learning. Since network operators have huge piles of data, network operators already collect and store few sources of customer data, network performance data, network traffic data, and social media data. They also make use of the patterns in data for network planning, root cause analysis and much more. So there is no doubt that a number of machine learning applications are already beginning to emerge within the telecoms sector.

Use of Machine Learning to identify and restart Sleeping Cells

Cell tower across the radio network may unfortunately crash our PC or laptop. This can have a serious impact on service, especially within busy regions or busy times of the day. Machine learning can be used to analyse, learn from network performance data and identify sleeping cells and initiate an automatic restart. Currently, it is a manual process, but it is being worked upon extensively to automate it, in order to utilize the full potential of Machine Learning.

Using machine learning to identify Potential Churners

For network operators, all types of customers are now a common existence as competition has increased and new deals are continuously coming to the marketplace. Many operators already have simple-patterned matching programmes in place, which helps to identify potential customers. However, these are not perfect and require regular maintenance. Machine learning algorithms are being developed to continuously learn from new data to understand why subscribers bail, in order to build a new strategy for customer retention.

Using machine learning to improve service application

As we know the telecom industry is well known for creating user profiles to enable targeted marketing of new services. However, there is a limit to the number of user profiles that can be identified, managed and kept up-to-date. To work on this, unsupervised machine learning algorithms can aid in identifying new subscribers that were previously unnoticed, with the use of algorithms that are fed data on user behaviour across the network. The data is then used to determine which package would be suitable for which customer.

Using Machine Learning for Detecting Frauds

Another major machine learning applications in telecoms is finding fraud and revenue assurance, which directly impacts the bottom line. Currently, most of the network operators utilise fraud mitigation tools that use rules-based logic to identify fraudulent behaviour. However, today we can identify such activities by using supervised machine learning algorithms. Machine learning may also offer the potential to spot early trending differences associated with criminal behaviour.

Machine Learning in Social Media

Now-a-days, social media has become an important business communication channel. It has transformed from a way to keep in touch with friends and family, to a fully-fledged business communication channel. This has brought both new opportunities and new challenges to telecoms operators. Network operators have turned to machine learning to analyse brand coverage and customer sentiment with so many social posts to monitor. Some real social posts are used to create an algorithm that can recognise language patterns and sentiment to identify trends.

Conclusion:

Apart from what we could mention here, the applications of Machine Learning USA and world wide are vast. In most of the sectors, Machine Learning has become one of the important components that companies lean on for day-to-day functions. Interestingly, Machine Learning was never meant to be an individual technology, and as a result we see exponential growth in the Machine Learning market as the figures are a combination of Artificial Intelligence and eventually IoT shares as well. Long story short, the synergy Machine Learning has developed with other technologies, is going to be another one of the significant factors why it is going to be an indispensable part of the future.