5 ways Machine Learning is used in Telecom Industry

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5 ways Machine Learning is used in Telecom Industry

Machine Learning (ML) is becoming more prevalent every day. Telecommunication companies are not avoiding these features, in fact, these computing processes are becoming extremely popular. 

Soon machine learning will infiltrate every industry and bring technologies to new heights. The telecom industry is ahead of the trend. Well-established companies such as AT&T, CenturyLink, Comcast, Spectrum, and Verizon are leading the way for machine learning.

HOW ARE THESE BIG BRANDS AND OTHER TELECOM COMPANIES IMPLEMENTING MACHINE LEARNING?

Telecom companies are implementing these new processes in various ways. The 5 ways that Machine Learning (ML) are being utilized by the industry are data-driven business decisions, network and infrastructure optimization, preventative maintenance, robotic process automation, and verification or fraudulent detection. Each of these will be further explained in this article. 

1.  DATA-DRIVEN BUSINESS DECISIONS

Machine learning can assist leaders in making efficient and swift data-driven business decisions. A trend that has been seen with location-based intelligence is the ability to sift through copious amounts of data and have machine assistance with data interpretation and application. ML can be used as a connective thread in location-based intelligence, and with machine learning together they can discover hidden patterns, automate processes, predict analytics in customer value, product development, process optimization, and more. 

2.  NETWORK AND INFRASTRUCTURE OPTIMIZATION

Machine learning allows for an optimized network and infrastructure. According to TechSee, 63% of telecom operators have invested in ML to improve and optimize their infrastructure. These computing processes can analyze and make corrections in real-time and provide continual service in ways better than a third-party director could enable. This creates a Self-Organizing Network (SON), or a network that can self-configure, self-optimize, and self-heal.

Once the root cause of an issue is resolved a corrective action will be made, and machine learning will allow for this to be implemented into daily computing processes. It will also enable the system to be able to predict when a similar issue will arise and be able to handle it preventatively.

3.  PREVENTATIVE MAINTENANCE

If an electrical company could prevent thousands of homes from losing power, there would be a lot of happy customers. For telecom companies, preventing outages or network disruptions are extremely satisfying to end-consumers. Machine learning supports company monitoring of equipment which can be proactive and anticipate the failure of various sorts. Predictive analytics is what makes these processes possible. Collective data is processed through sophisticated algorithms and compared with historical information to predict future results and apply preventative maintenance. These techniques can be applied to various forms of telecom equipment, i.e., data centers, cell towers. Maintenance that is performed proactively creates happy customers, which allows other forms of customer service to be focused on such as immediately responsive bots.

4.  ROBOTIC PROCESS AUTOMATION – CUSTOMER SERVICE BOTS AND VIRTUAL ASSISTANTS

One way that machine learning serves consumers for telecom companies is with robotic process automation. This can be seen in customer service bots, virtual assistants, and other processes.  

Customer service can be improved with responsive chatbots. With a high influx of customer interaction, it may not be feasible for human employees to provide immediate responses. Machine learning allows chatbots to cross-reference previous customer inquiries and respond accordingly with solutions to customer complaints or issues.

5.      VERIFICATION OR FRAUDULENT DETECTION

Algorithms that employ machine learning allows for immediate responses to fraudulent activity such as theft or illegal access. This is possible by the algorithms learning what normal or regular computing trends are and be able to identify and investigate anomalies from large sets of data more quickly than human analysts.

Machine learning has impacted telecom companies in a plethora of ways. Utilizing these computing processes allows for vast data sets to be analyzed quickly, be able to detect when issues arrive, and manage data efficiently. Adopting these processes is essential for company growth. Being able to process data quickly saves time and money.

By |2020-03-06T06:56:37+00:00March 4th, 2020|Communication, Machine Learning|Comments Off on 5 ways Machine Learning is used in Telecom Industry

About the Author:

Sonal Mehta is a Content Lead at Solulab, USA based leading Blockchain Technology, mobile apps and software development agency, started by Ex vice president of Goldman Sachs, USA and Ex iOS lead engineer of Citrix. Solulab help build startups - we are a no-sweat technical partner for early stage entrepreneurs to launch ideas from scratch and for later stage startups to build more quickly and affordably.

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