Machine Learning for Telecom

Machine learning is a branch of artificial intelligence that allows computers to learn without being explicitly programmed. It can be used to generate predictions and decisions based on previous data, making it beneficial in a variety of industries, including manufacturing. The primary goal of machine learning is to create systems that can do tasks automatically or semi-automatically by utilising prior expertise and patterns observed in the dataset. In this post, we will look at how machine learning may help your organization by enhancing quality control, increasing productivity, lowering costs, and more. We’ll also give you an outline of what you need to know about machine learning before you start applying it in your organization.

What is Machine Learning

Before delving deeper into why machine learning is vital for telecommunications, let’s first define it. “Artificial intelligence, often known as machine intelligence, refers to computer programmes meant to emulate human thought processes,” according to Wikipedia. “Any intelligent agent capable of perceiving its environment and taking actions that maximise its chances of success at achieving goals specified by itself or others” has been defined as “any intelligent agent capable of perceiving its environment and taking actions that maximise its chances of success at achieving goals specified by itself or others.” This term encompasses all machines, from robots to self-driving cars. Humans, in general, have the ability to think clearly and assimilate information fast. Machines do not inherently have these talents; instead, they rely on algorithms to perform their duties. These algorithms can be developed by hand or generated using machine learning techniques.

What Data Do Telecom Companies Collect?

In addition to data from various forms of telecom equipment, telecom data sources such as business transactions, CDR, GPS, and Internet Access Points can all provide insights into user behaviour. Access to such data is, of course, essential in order to derive those insights. Wireless base stations, mobile switching centres, radio frequency identification readers, cellular towers, and other components make up telecommunication networks. Depending on its function, each component collects different sorts of data. RFID scanners, for example, collect inventory data, whereas MSCs record call details.

Making the Most of Telecom Data

Given its multiple benefits, it’s not surprising that machine learning has become such a popular issue in telecoms today. In terms of automation, the technology has various benefits, including the potential to automate, reduce expenses, and make better human decisions.

Large telecommunications have mostly succeeded in maintaining their market leadership, but they increasingly face problems relating to growth and expansion into new business areas. Telecoms are not only maintaining their position in the tech industry, but also venturing into new areas.

Telecoms are undergoing a significant transition from the fourth to the fifth generation of cellular mobile communications (5G). 5G technology will enable smart cities by providing faster data transmission speeds, ultra-low latency, increased system capacity, and device connection fit for a world controlled by the Internet of Things (IoT) and self-driving automobiles.

Machine Learning types for Telecom

The need is urgent. Telecoms need Machine Learning to be able to process and analyze the data in many areas: customer experience, network automation, business process automation, new digital services, and infrastructure maintenance.

Machine learning is a sub-field 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.

Use-Cases/Applications of Machine Learning in Telecommunications

Almost every telecommunications company is significantly investing in AI. According to a survey at Mobile World Congress, 93 percent of telecom reps consider ML as a game-changing technology, and 76 percent want to adopt it into their business within three years. The rationale is obvious: telecoms require it and have the resources — money, people, and data – to do it.

Network Administration

RAN base station energy management

Capacity forecasting and upgrade planning

Coverage of radio holes

Sales and Marketing

End-to-end customization

NBO and optimization

Pricing and customer service

Customer life expectancy

Product aggregation and new product development

Business-to-Business (B2B) and New Business Models

Data collaboration and monetization

Advertising in the future

Credit risk assessment



Machine Learning is improving the efficiency of businesses such as telecommunications. The following are some major statistics of machine learning for Telcos to help them become more efficient.

  • After launching its chatbot TOBi, Vodafone saw a 68 percent increase in customer satisfaction.
  • According to a survey at Mobile World Congress, 93 percent of telecom reps consider ML as a game-changing technology, and 76 percent want to adopt it into their business within three years.
  • Machine Learning can also help reduce turnover rates, which can range from 10 to 67 percent on an annual basis.
  • AI in telecom technology resulted in a 68 percent increase in consumer satisfaction.
  • MIKA, Nokia’s virtual assistant, recommends solutions to network issues, resulting in a 20% to 40% improvement in first-time resolution rate.
  • According to research, “virtual assistants would enable telecoms to save US$1.2 billion on customer care management by 2022, with a CAGR of 17% over the next five years.”
  • Sixty-three percent of telecom operators have invested in artificial intelligence to upgrade and optimize their infrastructure.


Telecom businesses acquire a wealth of incredibly useful data that can be used to improve the efficiency of marketing activities, increase sales, improve retention, and, in general, raise income. In a data-driven world, any firm that fails to keep up with technology risks falling behind, and in an industry like telecommunications, there is no room for guesswork. In order to survive in today’s highly competitive market, businesses must make data-driven decisions.