Telecom industry has become highly competitive than before and the operators are trying hard to survive in the global market. This capital-intensive industry has more customers to cater to. Technology and globalization have rather pushed the industry to perform better not just in the market but in customer services. According to Statista, US$1.45 trillion is expected to be spent on the telecom industry in 2020.
The network structure and the communication network are extremely complex in the telecom industry. It demands extensive and regular maintenance to be on par with the customer services and functioning.
Machine learning is booming the telecom industry. It is a type of Artificial Intelligence which involves a learning process and is not programmed to function always. Telecom industry works with big data which is why it has to be intensively involved in machine learning processes to upgrade.
Just as any other device, the towers of radio and telecom communication get under the weather. Traditional methods engage personnel to fix the network performance. With machine learning in the picture, it can trigger sleeping cells and initiate a restart. Without machine learning, the cell towers were left unattended for a long time. Machine learning has not only automated the process but reduced the complexity and time consumption. Consequently, it has improved the customer service.
Utilization of machine learning helps to develop algorithms for the industry to learn from the data about the subscribers churning. With these algorithms not in place, it was comparatively hard to recognize the potential subscribers who churn at any point of time. It has also aided in understanding the patterns via the new data obtained through the algorithm.
Market spending is managed via the machine learning tools. Algorithms are developed to optimize the customer outreach and identify customer failure. Moreover, these algorithms also sort late-paying customers into groups such as—self-cure, in need of reminders, and bad payers—and target each group with different measures.
Machine learning algorithms have helped the operators to geographically optimize the consumer experience. Enhanced and supervised machine learning has enabled the industry to manage the user profiles. There is a limit to the intake of user profiles but algorithms, when fed with user behavior, can recognize subscribers. In this way, there is an improved service operation in the telecom industry.
There is a list of fraud mitigation tools to cease the fraudulent behavior of customers. Machine learning with its supervised operation has made possible detection of frauds easier than before via algorithms relevant in the field.
More social Involvement has led to more data available for comparison and management. The telecom operators now have more to handle which also facilitates them with more data to learn customer patterns and behavior. With ample of social media data, machine learning through its algorithms has helped understand language patterns. It has also availed platforms to study trends.
Telecom industry has been boosted up by machine learning and for the better. Various issues have been made easier to tackle the incursion of machine learning. It has a big impact and is positive. Apart from this, the recurrent management and maintenance have seen a dramatic improvement. To answer the question how machine learning has improved the telecom industry one has to browse over the newly created methods that made the telecom industry boom.