THEN AND NOW
Gone are the days when the telecom industry used to be involved solely in providing basic phone and internet services. The telecom industry trends show that the future of the Telecom Industry is AI-driven. With the bloom of Artificial Intelligence, this particular industry has seen exceptional technological growth. (Read – Telecoms have unique challenges in adopting AI). The use of AI in the Telecom industry is booming. And it is still growing, with experts expecting it to grow at least 42% by this year.
One move that could transform this sector would be to leverage AI’s ability to engage in active learning while analyzing very large amounts of data collected from their massive customer base.
Where does this data come from?
This data is culled from devices, networks, mobile applications, geolocations, detailed customer profiles, services usage, and billing data. Harnessing this Big data through AI could open up a wide range of uses across management and operations departments.
In a majority of companies, the role of AI in the telecom industry has been limited to chatbots that are automating customer service inquiries, routing customers to the proper agent, and routing prospects with buying intent directly to salespeople.
However, it is also possible to provide better customer experiences, improve operations, and increase revenue through new products and services by gaining actionable insights from data collected. Through the use of AI in the telecom sector, operators can improve network efficiency; lower operating costs, and improve both the quality of service and customer experience.
Tom Anderson, a Principal Technologist at Atis, writes that as operators transition their network architectures with software-defined networking and virtualization technologies that enable automation, AI will leverage these capabilities to self-diagnose, self-heal and self-orchestrate the network.
He says that through the use of algorithms that look for patterns, AI will be able to both detect and predict network anomalies, enabling operators to proactively fix problems before customers are impacted. This pattern-recognition capability is particularly useful for network security as AI will be able to help identify suspicious activity related to potential security threats, allowing the network to “take-action” in real-time before it impacts network performance.
From a subscriber’s intelligence perspective, AI will allow operators to collect, store, and analyze data from across an operator’s entire customer base to achieve real-time behavioral insights. Their social media, brand coverage, customer sentiments, and other telecom industry benchmarks could also be analyzed to learn what drives customers to the service provider and what drives them to leave.
The information thus gained can be combined with machine learning algorithms to make personalized recommendations based on a user’s behavioral patterns and content preferences. Relevant up-sell and cross-sell offers to the right users at the right time can be made. Data could be analyzed and the call & data package that best suits different types of users can be offered, increasing the sales success rate. AI and machine learning could also be used in detecting and fixing potential issues for market customers of the telecom sector even before they’re apparent to the end-user.
Big data will be essential for operators to achieve better utilization of network resources, allowing the network to adjust services based on user needs, environmental conditions, and business goals resulting in better network optimization.
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