Data Science in Telco

Improved user experience and churn prevention

Telco and Big Data

Until recently, working with data in the telecommunications industry was a major problem. Large data volumes, little computing power and high costs prevented companies carrying out in-depth studies on the data or even innovate drastically their operations in order to be more predictive, rather than reactive.

Times have changed:

  • The storage cost is dwindling;
  • The computing power has grown exponentially;
  • Low-cost analysis tools are now available.

The main efforts in Data Science related to telecommunications are focused on improving the user experience. To do so, they are creating sophisticated 360-degree profiles based on:

The behavior of customers:

  • Use patterns in voice calls, SMS and data transfer;
  • Video choices;
  • Customer service history;
  • Social activities;
  • Previous shopping patterns;
  • Business patterns, duration, navigation and search.

A demographic study of customers:

  • Age, address and sex;
  • Type and amount of used devices;
  • Using services;
  • Geographic location.

Practical Examples


Network Optimization

Telco companies are willing to combine their knowledge in network performance with other internal data (e. g. CRM or Marketing Campaigns data) to redirect the resources (for example, offers or capital expenditures) for certain hotspots network. Perhaps as important, real-time analysis can be used for damage control and for allowing companies to become predictive, rather than reactive.

In one example, a telco company can count on sensor data installed in various parts of the city to predict weather problems that could lead to a network disruption in some areas.

Say for example, in a network outage: Each department (sales, marketing, customer service) can observe the effects, locate the affected customers and immediately implement efforts to minimize the impact to customers.


Social networks and Sentiment Analysis

The evolution of social networks have transformed the way companies view their clients. Data scientists are collecting feedback information, complaints and social feeds and subjecting such information to the sense of analysis, with goals such as:

  • Improve the brand image before clients
  • Monitor the reaction to new products, offers and campaigns
  • Combat potential problems and alleviate customer concerns
  • Identify new sources of revenue

Churn prevention

The customer churn - when subscribers leave an operator in search of cheaper plans - is one of the biggest challenges that telecom companies face. It is much more expensive to acquire new customers than maintaining the existing ones. The most common causes of churn include high prices, poor service, poor connection quality, new competitors and outdated technology.

To avoid turnover, data scientists are employing predictive analytics with some objectives:

  • Create personalized product offerings and services
  • Improve existing profitable relationships and avoid customer churn
  • Create better marketing campaigns and products with more attractive offers
  • Development of products tailored to specific customer segments
  • And much more...

To help your business leverage your business, Semantix's team has several qualified experts to implement a complete solution for Big Data from the infrastructure, Data Ingestion up Analytics in Real-time with complex algorithms and Machine Learning techniques.


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