If there is an industry that should be collecting data in every possible way, then it’s telecommunications. The telecommunications industry serves millions of people every day, producing massive amounts of data. Out of the many telecom companies are processing this data by the help of data science, machine learning and artificial intelligence in this industry are not compatible. There are various ai service providers and ai companies in the market. Thus artificial intelligence in telecom is going to bring a revolution in this sector.
A study based on a survey of directors from 250 telecoms companies, it is found that most companies had not yet seriously generate the data at their system to increase profits. Only 20 percent say they have made investments in big data, machine learning, and artificial intelligence.
So while there is certainly a doubt within the telecom companies about whether the return on investment and payback time is worthwhile in these particular sectors. However, there is no question that data science, machine learning (ml), and artificial intelligence cannot be ignored when it comes to the future of the telecom industry.
With the help of data science, machine learning, and artificial intelligence strategies, telecommunication companies can improve the following areas of their services. The importance of data science, machine learning, and artificial intelligence in Telecom industry will represent itself in these particular areas.
It is estimated that in 2014 that fraud costs the telecom industry more than $4 billion a year. However, the faster that telecom companies studied even large amounts of data, the better off they are in identifying suspicious call patterns that correlate with duplicate activities.
One of the significant challenges for telecom providers is being able to guarantee quality service to the users. Analysing call detail records generated by users at any given moment of the day is the key to solve troubleshooting problems. However, call detail records are challenging to work with because the volume of data gets very quantitative and unwieldy fast. For example, the largest telecommunication companies can save and process up to seven billion call detail records per day.
Telecommunication companies can, however, increase their services by processing the millions of user complaints they get every year to figure out which types of modifications will have the greatest impact on customer satisfaction and thereby increase customer numbers. They can also process data at a larger and more automated scale to gain insights into the performance of their employees.
The more that telecommunication companies can process data on customer calls, the more they can start to recognize which types of problems are most likely to dominate to unwarranted truck rolls and put in preventive measures to prevent these particular calls. Given the number of calls and the duration of analysis required, this is necessarily of machine learning approach more specifically, a depth learning approach. Because processing the calls themselves means dealing with lots of random data, it’s the perfect place to expand into machine learning and artificial intelligence.
The top churn rate in telecommunications, estimated at between 30 to 40% annually, is the highest challenge for telecom companies. Telecommunication companies can use artificial intelligence to build better profiles of customers, figure out how to best achieve their loyalty, and adequately allocate in a particular marketing budget. With improved data architecture, they are able to cultivate and store a greater range of data that provide insights into each user such as demographics, location, gadgets used, the frequency of purchases, and person patterns. By collecting data from other sources like social media, they can have a stronger understanding of their users.
Using machine learning gives a more pinpoint picture of which channels are most responsible for customer conversions for best ad buying as well.
The future of data in the telecom industry is mainly governed by artificial intelligence.