Top Opportunities for AI in Telecommunications

With mobile usage and traffic exponentially increasing every second, if there is one industry that should be leveraging data in every way possible, it’s telecommunications; yet, experts estimate that most companies have not yet seriously leveraged the data at their disposal to increase profits.

The largest telcos collect up to 6 billion call detail records (CDRs) per day, and this amount is increasing exponentially. This is beyond the capacity of human analysts, and is where AI can provide value. Supplementing CDRs with other types of data (like that from CRMs, social media, and more), and telecom organizations have a shot at capturing some of the enormous value their abundant data can provide.

According to a survey of 1,200 global organizations, 66% of telecommunications companies believe AI is considerably or very important to the future of the business, but they face challenges associated with managing data governance and regulatory compliance, identifying the most trusted source of data, and providing data security.

High-Value Use Cases for AI in Telco

Providing and maintaining good service: Using machine learning and eventually AI systems, telecommunications companies can instantaneously parse through tens of millions of CDRs, identify patterns that may point to problems, create scalable data visualizations, and leverage predictive maintenance techniques to reduce dropped calls, poor sound quality, and the myriad of other issues that could cause customers to seek a new provider.

Combating Fraud: While scams involving automated calls or premium rate charges don’t represent a direct threat to telecom companies, they can decrease long-term customer satisfaction. The quicker telecom companies can identify suspicious call or customer patterns, the faster they can combat fraudulent activity (including first-party or true-party fraud). AI strategies and techniques such as advanced anomaly detection make it easier for telcos to identify true-party fraud. If companies know how to distinguish between legitimate credit defaults from frauds, they can focus their collections efforts on the cases that are most likely to generate a positive return.

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Churn Prediction: The high churn rate in telecommunications, estimated at between 20-40 percent annually, is the greatest challenge for telcos. Using churn analysis and churn prediction techniques, providers can build better profiles of their customers and sketch out a strategy to retain their loyalty, thus evaluating who is likely to churn and who might still respond positively to marketing messaging.

churn prediction

Improving Customer Satisfaction: A major value add for telcos is to reduce service calls, which represent heavy costs for operators and divert technicians away from their work. By analyzing massive amounts of data using machine learning techniques, teams can identify unwarranted service calls and assess the performance data of technicians to further improve customer service.

Dataiku for Telecommunications

The transformation to leveraging machine learning and AI across the enterprise requires a coordinated change not just in technology platforms and tools, but in processes and — most difficult of all — in people. To extract maximum value, data must be at the hands of everyone in the business (not just a specific, siloed team) to use it to its full potential for every possible use case. 

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In telecommunications, this means empowering people across the organization to use machine learning- and AI-powered systems to enhance their ability to make decisions and serve customers. Dataiku is one of the world’s leading AI and machine learning platforms, supporting agility in telecommunications organizations’ data efforts via collaborative, elastic, and responsible AI, all at enterprise scale.

Dataiku is for:

  • Senior- and C-level management, for better decision-making and bubbling up insights from data created by lines of business and middle management. 
  • Line-of-business management to spearhead machine learning and AI projects no matter what the use case, providing robust monitoring, governance, and change management solutions.
  • Data scientists and data teams to connect to, explore, prepare, build, automate, and deploy machine learning-based models.
  • Sales and marketing teams, who can use Dataiku’s point-and-click data preparation and modeling features to bootstrap their own data exploration or collaborate with data teams to deploy larger AI projects. 
  • Finance departments, as an alternative to spreadsheets for more governable data analysis, plus the ability to work directly in collaboration with data experts for fraud detection and prioritization use cases.
  • IT and network teams to work on use cases like advanced anomaly detection for better network quality for customers plus internal network security.
  • Customer service and technicians, who can use the outputs of projects built by data teams to provide better answers and service to customers.
  • …and more — Dataiku is flexible yet robust, built for everyone in the data-powered organization.

Dataiku’s centralized, controlled, and elastic environment fuels exponential growth in the amount of data, the number of AI projects, and the number of people contributing to such projects. The platform was built to scale as businesses strive to go from a handful of models in production to hundreds (or thousands).

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