The telecom industry has always seemed to navigate well through tech changes. As the industry has evolved, it’s managed to transform from landline to mobile carriers, then from voice calls to messaging and data-centric networks. In many developed markets telcos are creating ecosystems for the data-driven economy.


 

The next frontier is shaping up to be one driven by machine learning (ML) and artificial intelligence (AI). AI and ML are poised to be the core technologies that help companies build new revenue streams, renewed innovation and stronger customer relationships.

As in the past, the industry seems to be responding well to this shift in technology. After a recent survey at the Mobile World Congress on AI/ML, 93 percent of respondents said that these technologies will change every single way of pursuing things in the coming three years.

The Continuous Drive to Boost Revenue

Along with this opportunity, however, comes the continuous pursuit of and need for revenue growth. AI is helping telecom organizations reach this goal quickly, while also improving network capabilities and enabling faster processing of large volumes of data.

Telecom operators are now looking for ways to optimize, disrupt and innovate. They can do so by embracing data, ML and AI. These technologies will not only lead to greater efficiencies, but also to increased revenue and improved margins. To reach their revenue goals, telcos should focus on use cases across all aspects of their business, build common data infrastructure and integrate ML into workflows and processes.

New Opportunities Await

AI and ML are boosting innovation in telecom, providing opportunities for new ideas to accelerate digital transformation. These opportunities can come in a variety of ways, including:

  • Better customer service – such as automating customer service inquiries, routing customers to the right agent, etc. 
  • Network optimization – giving telecom companies the ability to identify the root causes of complications in network operations. The ability to optimize customer interactions, network design, planning, operations and more allows for optimized CAPEX and OPEX resource allocations.
  • Predictive maintenance for example, AI-based predictive analytics can help telecoms provide better services by proactively fixing problems with communications hardware; monitoring the state of equipment, anticipating failure based on patterns and more.
  • Fraud detection – including theft or fake profiles, behavioral fraud and other activity. Applying ML algorithms to customer and operator data can help prevent fraud and provide real-time responses to any suspicious activity. 

Anomaly Detection and Forecasting: The Future is Here

In our new guide, “Extending the Competitive Advantage in Telecom, we dive into how the dynamic nature of services and networks will require two key core areas of AI/ML technology: anomaly detection and forecasting demand/behavior. As network complexities grow, anomaly detection will be needed to manage everything from devices to legacy and core networks to IT operations. Forecasting will also be critical, to respond to the growing need to automate, provision and scale network resources and support new usage patterns.

Topics: AnalyticsTime Series Data
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Written by Vikram Pulakhandam

Vikram Pulakhandam is Anodot's Solutions Director for Asia-Pacific and Japan, leading pre-sales technical engagements across the region. Prior to joining Anodot, he led the Big Data Engineering team for an Australia operator. He has more than 20 years of mobile telco experience with a variety of vendors across the globe.

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