Is machine learning mania by mobile operators the new telecom thunder?

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Telecom Business in the global scenario is constantly in the phase of adapting to new technologies. Machine learning is now being adopted by the businesses with a view of maximising revenues, and enhancing customer’s usage experience.

The area of machine learning deals with the design of systems that can learn from data, adapt to changes, and improve performance for the business and customer.

Use of IoT and 5G will create a large number of patterns which are not easy to manage for humans. However, machine learning can help manage and monetize these patterns.

Operators have started working on machine learning for improving their functions, such as:

  • Predictive maintenance: The ability to fix problems with telecom hardware before they happen, where the network’s goals and limits are set by designers and actions are taken within those boundaries keeping the environment and predicted capacity demand in account.
  • Machine Learning may enable machines perform human like tasks, such as by automating customer service enquiries by understanding their usage behaviour and thereby providing better customer engagement.
  • Personalisation: It is based on adapting the offering to customers based on preference of customer base. Today, it is done by surveying a subset of customers to understand consumer behaviour. However, by applying machine learning techniques, businesses can automate the process of grouping customers into segments by assessing customers according to their usage behaviour. This can help to give personalised view of services to users even before user interacts with the service(s).

Potential problems with machine learning in telecom are to be addressed before they are deployed. Data will be gathered from multiple nodes in the system, hence, problem of computational power, memory, communication, and processing time will be a limitation considering the sheer scale of data process needed by Machine Learning algorithms. Moreover, the methods and objects that help the business in predictive models will evolve overtime and they will have to be updated regularly and connected to older platforms as well.

In order to reap benefits of machine learning to drive telecom business, it is important to account these major answers:

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1. Defining the business problem, to which solutions are being sought

2. Choosing the most appropriate algorithm to generate the best results

3. Careful selection of data sets and its sources. Better data will help generate better performance

There is no question that machine learning has huge potential. It is surely only a matter of time before all telecoms providers are using them.

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