Virginia, USA-Based Telecommunication Industry Get Solutions in User Behavior with NMF

Introduction

A large telecommunications provider sought to improve customer experience by making better use of its existing network and usage data. While they had access to vast amounts of information—such as internet traffic volume, duration, and connection quality—they lacked a clear understanding of user behavior and how it correlated with network performance. To address this, they collaborated with Codinix Technologies, a company specializing in Machine Learning and AI Development Solutions, to apply advanced data analysis techniques including Non-negative Matrix Factorization (NMF).

Client Overview

The client is a national telecom operator delivering broadband internet services to a broad user base. With millions of users and a high volume of traffic data generated daily, they needed a method to interpret usage trends, monitor service quality, and guide infrastructure investments using data-driven insights.

Project Objective

The project aimed to achieve several goals:

• Understand different types of internet usage behavior (e.g., streaming, browsing, downloading)
• Measure and improve quality of service in real time
• Identify early signs of service degradation
• Support data-driven decision-making for network upgrades and customer engagement strategies

Challenges

Some of the core issues identified at the start of the project included:

• Unstructured Data: While usage data was available, it lacked the structure and context needed to understand behavior.
• Behavioral Segmentation: It was difficult to distinguish how different user groups consumed services (e.g., casual browsers vs. high-bandwidth users).
  Limited Insight into Experience: Network issues were often detected only after users reported them.
• Unfocused Resource Allocation: Investments in infrastructure weren’t always aligned with actual usage patterns or performance bottlenecks.

Solution Approach

Codinix Technologies worked with the telecom provider to implement a structured machine learning solution. The approach included:

• Non-negative Matrix Factorization (NMF): This dimensionality reduction technique was used to uncover hidden usage patterns and group users based on behavior.
• Data analytics platforms: These were used to monitor session durations, bandwidth usage, and application-level activity.
• Network quality metrics: Factors like latency, throughput, and signal strength were analyzed alongside user behavior to evaluate service experience.

The solution was designed to support interpretability, scalability, and integration into existing operational workflows.

Outcomes

The implementation provided several insights and operational benefits:

• Improved Service Insights: User behavior could now be segmented into distinct patterns (e.g., frequent streamers, light users), allowing for more tailored service tuning.
• Early Detection of Network Issues: Performance issues could be identified proactively based on observed patterns, reducing dependency on user complaints.
  Smarter Infrastructure Planning: Network investment decisions were more focused, prioritizing high-impact areas based on real usage.
• Targeted Communication: With clearer segmentation, customer messaging and offerings could be adapted to reflect actual usage behavior.

Conclusion

This project highlights how Machine Learning Consulting Companies, such as Codinix Technologies, can support telecom providers in converting raw data into meaningful operational insights. By integrating Machine Learning and AI Development Solutions with performance analytics, the telecom company was able to better understand its user base, improve network reliability, and make informed strategic decisions.

Such applications of machine learning are an integral part of broader digital transformation solutions, especially in industries where real-time data and customer experience are critical to success.