In the modern hyper-connected world, telecom networks are the backbone of the digital age, keeping us connected in our personal lives and running essential businesses and services. But disruptions like fraud, outages, or dropped calls can cause some serious headaches to both the service provider and their customers.
Traditionally, anomalies within telecom networks were only uncovered at the end of each billing cycle, if detected at all, requiring analysts to comb through seas of data. But now thanks to Machine Learning (ML), we can process and analyze huge stacks of data records to spot trends, detect suspicious outliers, and create connections that humans may have overlooked.
In this blog post, we'll explore how our Event Storage & Analytics Platform (ESAP) puts statistical analysis and machine learning to work for telecom companies, unlocking valuable insights that were previously hidden within their data.
Improving the Customer Experience
Call Detail Records (CDRs) are a critical asset for anomaly detection, containing a wealth of detailed information about each call without revealing the call's actual content.
The ESAP analytics module uses real-time data crunching and analysis to keep a close eye on how calling and network traffic behaves to identify deviations from the norm. When a higher deviation of dropped calls are detected, we pinpoint the service area affected and alert the provider to resolve the problem.
These types of analytics not only keep the network in good shape, but also give in-depth predictive insights for planning and future growth. We can figure out where, when, and how customers are using the network to predict when resources will need to be allocated to minimize service disruptions that can impact service quality.
As our models get better at understanding key features to learn from, they become more accurate to improve the monitoring experience internally, and improve service quality to the external end-user.
Network Outages, Server Failures
Network outages are another common challenge for telecom carriers that can lead to customer churn, tarnished reputations, and financial repercussions.
Advanced algorithms, complemented by classification models that automatically trigger alerts when unexpected patterns are detected, serve as early warning systems for impending problems.
The deep analysis provides insights into specific network trunks, geographical locations, and voice traffic patterns, enabling telecom carriers to quickly and effectively address outages once detected.
Stopping Fraud in Real-Time
Another harsh reality of telecom providers is the inevitability of fraud, with the question not being "if" but when it will occur. Advanced analytics are the frontline defense against these fraudsters by combing through large datasets of near real-time network traffic.
Utilizing the same anomaly detection methods discussed earlier, these machine learning solutions distinguish regular usage patterns from irregular ones to identify potential fraudulent activities. Cluster analysis helps in the creation of user profiles based on call behavior, location, and other variables. By comparing an individual's usage to their cluster, deviations from the norm are highlighted as suspicious activities warranting further investigation.
When anomalies in usage patterns, call destinations, or unexpected surges in traffic emerge, our models promptly alert the relevant personnel to safeguard your services.
As new data analysis techniques emerge, we plan on implementing more machine learning-driven processes to drive innovation through modern models and architectures.
The future of telecom hinges on proactive management, with AI leading the way to ensure seamless and reliable connectivity. Big data analysis is no longer only for the ‘big guys’, and providers of any size can modernize their operations to take advantage of these systems.
The items mentioned in this article represent only a subset of the modules integrated into our ESAP interface. Advanced Technologies & Services specializes in crafting robust data analytics frameworks, implementing scalable infrastructure, and deploying advanced algorithms to extract valuable insights from telecom data.
To discover more about our Event Storage & Analytics Platform (ESAP) and to request a free demo, please don't hesitate to contact us today.