How Artificial Intelligence(AI) And Machine Learning is being used in Telecom Companies?

Customer Service and Satisfaction

Nearly every telecom uses artificial intelligence and machine learning to improve its customer service primarily by using virtual assistants and chatbots. Telecoms get a massive number of support requests for set up, installation, troubleshooting, and maintenance. Virtual assistants automate and scale responses to these support requests, which dramatically cuts business expenses and improves customer satisfaction. In one example, Vodafone saw a 68 percent improvement in customer satisfaction after introducing its chatbot TOBi.

As a gatekeeper, chatbots analyze the requests, learn to route and escalate customer queries if necessary, identify sales opportunities and alert the customer to other products and services that might be of interest to them, and handle the bulk of them without human involvement. AT&T, Verizon, Comcast as well as just about every other large-scale telco uses AI for enhanced customer service.

The ability to offer speech and voice services such as chatbots is available thanks to artificial intelligence and machine learning. Not only is this used in chatbots, but it expands the service offerings such as Comcast’s XI Talking Guide that “speaks” network names/time slots, show titles and helps customers navigate through their television options. The company’s voice remote is useful for individuals with disabilities and anyone who wants to “search” through their voice rather than hitting buttons on the remote.

AI can help telecoms identify and react to problems as well as propose the right service at the moment based on analyzing customer data. This intel, knowledge of historical info, and personalized service can also help companies develop better products and services and ways to market them to give customers what they want when they want it.

Predictive Maintenance and Improve Network Optimization

One of the most important ways to give customers what they want is for telecoms to prevent outages. Predictive maintenance enabled by AI is an essential albeit more behind-the-scenes use of AI and machine learning that also improves customer satisfaction. Data-driven insights help companies monitor equipment, learn from historical information, anticipate equipment failure, and proactively fix it.

Another important facet AI assists with is network optimization. A Self Organizing Network (SON) fueled by artificial intelligence can help networks continually adapt and reconfigure based on current needs. It is also beneficial when designing new networks. Since AI-enabled networks can self-analyze and self-optimize, they are more efficient at providing consistent service.

Robotic Process Automation (RPA)

Considering the volume of customers, any individual telecom company deals with daily, every step of every interaction opens the door to human error. By automating business processes through robotic process automation, not only are repetitive and rules-based operations done more efficiently; they are more accurate. In a survey conducted by Deloitte, telecom, tech and media executives confirmed significant investment in cognitive technologies while 40 percent said they experienced “substantial” benefits and three-quarters of them expect cognitive computing to “substantially transform” their companies.

Fraud Detection

Machine learning algorithms are instrumental in detecting fraudulent activity such as theft or fake profiles, illegal access, and more. These algorithms learn what “normal” activity looks like so can spot anomalies from enormous data sets much quicker than human analysts can to provide nearly a real-time response to activity that needs to be investigated.

Data-Driven Business Decisions: Predictive Analytics

Telecoms possess enormous amounts of data from customers. With the use of AI and machine learning, telecoms can extract meaningful business insights from this data so they can make faster and better business decisions. This crunching of the data by AI helps with customer segmentation, customer churn prevention, to predict the lifetime value of the customer, product development, improving margins, price optimization, and more.

Ultimately, artificial intelligence and machine learning have enabled the telecommunications industry to extract insights from their vast data sets, made it easier to resolve issues, manage daily business more efficiently and provide improved customer service and satisfaction. The industry provides us with a great example of how adopting AI and machine learning wasn’t just beneficial to business; it was essential for each company’s survival and ability to compete with competitors.