LTE and 5G Network Predictions
Industry
Telecommunications
Services
VIGIA Advanced Algorithmic Suite™
Our client serves a large portion of mobile phone users in Mexico. And it can’t fail. Adopting a culture of anticipating technological problems is having vision and talent.
About the Customer
Main Services:
Telephony, internet, mobile broadband services, network security, business services and more.
The Problem:
Evolve the client's Data Center to anticipate negative events in their 3G, 4G and 5G mobile networks, as well as evaluate and predict the KPIs of their services in real time on a single dashboard.
Our Solution:
Establish a platform based on statistical models and machine learning algorithms with the ability to predict the behavior of key indicators
Learn. Discover. Anticipate. The formula for mastering a network, evaluating it, and achieving substantial economic savings.
It’s impossible to underestimate the importance of a mobile phone service company using the best available artificial intelligence to monitor its technological infrastructure and maintain high quality standards.
By leveraging the predictive capabilities of VIGIA Advanced Algorithmic Suite™, we were able to provide the client with the ideal tool to evaluate their services and, at the same time, identify potential problems before they become critical failures.
Timely interventions clearly reflect a satisfactory level of user experience, through satisfied customers who remain loyal to the brand and fully recommend it, as well as reduce the financial and reputational risks associated with unexpected service interruptions. Advanced machine learning algorithms can analyze large amounts of data from network components, identifying patterns and anomalies that human operators might overlook. In return, VAAS™ has been able to ensure that the company's technological infrastructure operates at maximum efficiency, resulting in more reliable and faster mobile services for end users, as well as significant savings for the company.
What we did
We developed a set of combined statistical models and machine learning models to project the behavior of more than 100 of the client's indicators, on two timelines: long-term and short-term, both with an accuracy above 95%.
The implemented platform has dynamic dashboards that allow visualizing a comparison between the current behavior trend of each metric and its expected one.
In numbers
