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Telecom Data Solutions

Top 5 Use Cases of Machine Learning in the Telecom Industry

Introduction Benefits Reviews Case Studies Services Insights Speak to an expert

INTRODUCTION

Advanced Data Science and Analytics in the Telecom sector

Machine learning enables telecoms companies to gain valuable insights from large volumes of data and make more informed business decisions.

Machine learning has several benefits for the telecoms sector. It can help improve network performance and capacity planning, optimize resource allocation, and enhance customer experience by predicting and addressing service issues. Machine learning algorithms can also be used to detect fraudulent activities, prevent network failures, and improve the accuracy of billing systems.

BENEFITS

How can Calligo help the Telecom Sector?

Optimize Call Centre Staff

Optimizing staff scheduling reduces labour costs and increases customer satisfaction.

Market Penetration

Understanding market penetration helps organisations identify high-potential markets that represent the best return on investment.

Store Location Optimisation

Choosing the best locations for new stores maximises revenue potential and saves building costs.

Service Interruption Detection

Eliminating and quickly responding to network problems maintains revenue, helps customer retention, and increases customer satisfaction.

Customer Segmentation

Creating customer groups makes marketing more targeted and effective. This can lead to more profitable product selection which informs where product expansion should occur.

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REVIEWS

See what our clients say about us

Calligo’s dashboards and visualizations allowed our teams – most of whom had no analytical experience – to interact with some of the most advanced data science they would perhaps ever come across, and make strategic decisions simply and confidently.

Teams that can create models that are both mathematically precise and also commercially savvy are very rare, and it’s what Calligo specializes in.

Michael Boese
Senior Manager of Strategic Transformation at T-Mobile

The skills required to perform this sort of analysis were almost as varied as the datasets themselves. The project had to be planned strategically, requiring in these early stages more business analysis capability than data science. There were then substantial amounts of expert data cleansing and re-architecture required to ensure the intended insights would be extractable. And then once the data scientists had built and painstakingly fine-tuned the custom models, the team then also distilled the mathematical outputs into comprehensible, usable outputs. From commercial strategy to data architecture and data science, Calligo’s special mix of capabilities was exactly what this project needed.

Michael Boese
Senior Manager of Strategic Transformation at T-Mobile

Data science projects often slip up by not sufficiently considering the end user, and how the output is intended to be used in practice. If humans are expected to make decisions based on the model’s calculations, then the data needs to be presented contextually, interactively and attractively. Calligo’s dashboards and visualizations allowed our teams – most of whom had no analytical experience – to interact with some of the most advanced data science they would perhaps ever come across, and make strategic decisions simply and confidently. The importance of this skillset cannot be underestimated.

Michael Boese
Senior Manager of Strategic Transformation at T-Mobile

The reason Calligo’s machine learning predictions have been proven to be so accurate is not just down to their data science skills. It is because they have an unusual blend of technical expertise and commercial understanding. Statistically-accurate models are commonplace. But teams that can create models that are both mathematically precise and also commercially savvy are very rare, and it is what Calligo specialises in.

Michael Boese
Senior Manager of Strategic Transformation at T-Mobile

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