Over a million developers have joined DZone.
{{announcement.body}}
{{announcement.title}}

The Role of Machine Learning in the Telecom Industry

DZone's Guide to

The Role of Machine Learning in the Telecom Industry

Have a quick glance at the below points to discover why Artificial Intelligence and Machine learning are helpful for telecoms.

· AI Zone ·
Free Resource

Start coding something amazing with the IBM library of open source AI code patterns.  Content provided by IBM.

Machine Learning is the buzzword all over the world, and several such technologies are growing rapidly. Machine Learning and Artificial Intelligence technologies are present in every industry and they are increasing the growth graphs. There are several opportunities that can transform the digital world.

Considering the telecommunications industry, Machine Learning and Artificial Intelligence are helping to build more revenues and stronger customer relationships.

After a survey done at Mobile World Congress about ML and AI, it was found that 93% of the people claim that these technologies will change every single way of pursuing things in the coming 3 years. Therefore, there is a huge chance that 76% of the people would have integrated the AI and ML into their organizations. The forerunners which are 12% in number, have already imbibed these technologies into their work culture, 62% are still in a discussion phase on how to apply these technologies.

What Is the Reason for Telecom Companies to Adapt ML and AI?

First and foremost is the data analysis and its processing. Telecoms have an ever-growing data, but slow processing of information is the primary concern. But, with ML and AI, telecoms are able to analyze huge and complex data while delivering accurate results at a faster rate. Through this change, they can avoid potential unknown risks and find great opportunities.

ML and AI technologies are getting to the core of the digital transformation era and many telecoms are living it. These technologies find a way in various functions of work like business process, network automation, customer relationships, and new digital services. This, in turn, helps to build new business models and improve the quality of functions.

Nowadays, 5G is the upcoming buzz and hence these technologies play an essential role through connectivity and network perspectives.

These can be helpful in optimizing customer relationships and lowering costs, which further enhances the business with higher efficiency.

There are some tier 1 telecoms like NTT, Vodafone, Telefonica, AT&T, SK Telecom, and Orange already know the power of these technologies. These have either already launched or want to launch their AI platforms.

Check out a few potential applications through which the telecoms can unleash the power of ML and AI.

Improving the Business Margins

Communication Service Providers (CSPs) are shifting their focus from just revenue growth to margins enhancement. By margins, we mean not only corporate margins, but also for the customer related. The customer data is scattered among various sources, and hence this presented a challenge to all the CSPs.

With AI and ML, it is possible for the telecoms to leverage present and historical data of the customers, social links, and purchase patterns. They can combine this Big Data with others like ERP, OSS, and BSS systems to build high-granular and multi-dimensional insights into potential consumer margins. It is possible for them to use ML algorithms for various tasks like recommending ways and making individual action plans to optimize margins with great customer satisfaction. Such plans can also be inculcated with CRM systems so the sales staff can give those systems as choices to the customers.

Maintaining Mobile Tower Functions

Mobile tower maintenance is a challenging task for the CSPs because they need periodic on-site inspections to check all the infrastructure and equipment are working fine. This duty consumes a lot of time. Moreover, there are also chances of theft where the valuable equipment can be stolen from the tower.

To solve these problems, CSPs can use AI-empowered video analysis through surveillance cameras planted on the towers. AI has several uses like to detect the non-likable events, for example, fire, intrusion, etc. early and to give alerts in real time. ML algorithms in combination with IoT sensors at the towers can be used to analyze the data from the surveillance cameras and can give 360-degree monitoring analysis. This data is also useful in workforce deploying systems to find out when spare parts, materials, inspections, or maintenance is necessary. To maximize network utilization and enhance coverage, asset management system can cross-check the active network modeling information.

Better Customer Service

Now lastly, let’s see how ML and AI can offer a better 24/7 customer service through automation.

Usually, telecoms get complaints from the customers regarding the connectivity of the equipment like Internet Protocol Television (IPTV) boxes, or modems. It is not possible to dispatch the technicians at all places and every time and sometimes the problems could be solved with a single reset.

If companies put a 24/7 chatbot, the customers can get quick responses and resolve many issues with the help of a ticketing system having ML technology. Their algorithms can identify faults with the context of historical information, server ticket data, and network log.  The chatbot is clever enough to find solutions for resolving customer queries, connectivity issues, etc. They can also figure out which queries need technicians themselves and which don’t. Their combination of OSS systems can further help in avoiding on-site maintenance, reducing the personal technician visits, and material shifts. Thus, business would have lots of savings.

These scenarios are happening in present and not just the prediction of what could be ahead. So, it is clear that Machine Learning and Artificial Intelligence are playing a key role in the telecom industry and it is going to enhance further.

Start coding something amazing with the IBM library of open source AI code patterns.  Content provided by IBM.

Topics:
artificial intelligence ,machine learning ,telecom ,ml algorithms ,customer service

Opinions expressed by DZone contributors are their own.

{{ parent.title || parent.header.title}}

{{ parent.tldr }}

{{ parent.urlSource.name }}