Artificial intelligence (AI) has been used in the telecommunications industry for more than a decade. Expert systems and machine learning are the two AI techniques that have been widely used in telecommunications, while machine learning and distributed artificial intelligence are the two AI techniques that are most promising for the future.

Use Cases

Customer Lifetime Value

Applications based on Machine Learning allow operators to predict lifetime value. It’s now possible to classify customer’s segmentation based on Customer’s Age Group, VIP Status, Spend Status and Customer Length of Service.

Сhurn Prevention

Churn models aim to identify early churn signals and recognize customers with an increased likelihood to leave voluntarily. Many machine learning algorithms are used to tackle the churning prediction problem. These methods include: Artificial Neural Networks, Decision Trees learning, Regression Analysis, Logistic Regression, Support Vector Machines, Naive Bayes, Sequential Pattern Mining and Market Basket Analysis, Linear Discriminant Analysis, and Rough Set Approach.

Predicting Customer Experience

Telcos deal with large amounts of information, which makes it harder to extract customer insights to react to potential causes of poor customer experience. It’s now possible to classify customer experiences based on data feeds, customer care calls, spatial distribution, and temporal distribution using a supervised learning approach of Restricted Random Forest.

Detecting Fraud

Mobile communication fraud is common since it is easy to get a subscription using fake ID and mobile terminals are not bound to physical locations. Now it is possible to detect fraudulent calls in mobile phones by analyzing the user’s calling behaviour using machine learning.

Customer Service Chat Bots

AI provides a capability to automate customer service inquiries, route customers to the proper agent, and route prospects with buying intent directly to sales people.

Service Fulfillment

AI allows to predict network resources usage and allocate resources accordingly. External data such as traffic, weather, or special nevents can be taken into consideration. Machine Learning can be used to reconfigure networks either fully- or semi-automatically thus enabling Self-Organizing Networks (SON) and allowing for closed-loop automation.

Revenue Management

Operators can optimize and adjust marketing campaigns, promotions and offers based on analytics thus enabling dynamic pricing possibilities. They can discover and anticipate new trends in consumer behaviour and act accordingly to launch ad-hoc offers to target specific segments. Advanced data analysis allows operators to offer enhanced personalization and tailor their services to the various needs of their customers.


  • Improved customer service and experience
  • Optimized networks and processes
  • Increased capacity and reduced costs
  • Improved employee work performance