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Few industries are as primed to be radically improved by Machine Learning as the Telecoms industry. About 1.5 trillion U.S. dollars is forecast to be spent globally on telecom services in 2018. In the United States, citizens don’t believe they’re getting good value from their in-home Telecom services, Internet and TV services in particular, despite the fact they spend an average of $1,848 a year on them, according to a new market report from Consumer Reports National Research Center.
Nowadays, factors such as globalization and technology innovation offer further challenges to telecommunication operations, and the industry must become more and more competitive in order to survive in a global market with many more competitors and pressures for increased customer choice, lower price for connectivity and improved service quality.
According to Wikipedia, the top 59 telecoms companies generated about 1.44 trillion dollars in annual revenues with AT&T, Verizon Communications, and China Mobile leading the way respectively. In 2016, the top three companies generated 398 billion dollars combined, which is no small feat.
The telecommunication services sector is by nature a very capital-intensive business with large spending on capital expenditures (CAPEX) in recent years. In 2015, AT&T spent $19.2 billion while Verizon spent $27.7 billion on CAPEX.
Telecom operations are built on massive communications infrastructure and use a lot of energy and equipment, requiring advanced fixed investments to ensure quality and up-to-date communications services to customers.
Machine Learning: Any changes so that the same work can be done more efﬁciently than previously can be called learning. ML involves the learning ability of the computer. The machine learning algorithm is the main task in ML.
Here's how Machine Learning can improve efficiency and boost profits in Telecoms:
Telecom operators can use Machine Learning to reduce customer churn, make better marketing-spend decisions, improve collections, and optimize network design. For instance, customers at risk of defecting can be identified even before they consider doing so, enabling operators to target their retention efforts, reduce spending, and maximize impact. One company raised its telephone-outreach hit rate from 60 to 500 at-risk customers out of every 1,000 calls, and reduced its attrition rate from 24 percent to 19 percent a year. At the same time, network operators can optimize their marketing spending by using advanced analytics tools, such as forecast simulators and econometric models, to predict acquisition and retention at the level of individual advertising channels instead of relying on predefined percentages or gut instinct to allocate their advertising budget and ceding the details to an agency. In collections, meanwhile, operators can use analytics to identify customers at risk of default and optimize outreach for those they wish to retain. One company created an algorithm that sorted late-paying customers into three groups—self-cure, in need of reminders, and bad payers—and targeted each group with different measures. The new approach cut churn among these customers by 90 percent. In network design, clustering customers according to their daily travel patterns has enabled some operators to fine-tune their geographical networks to optimize customer service and investment. One company increased the ROI of network deployment by 10 percentage points and reduced capital spending by 38 percent.