How the Telecom Sector is leveraging Big Data Analytics?

How the Telecom Sector is leveraging Big Data Analytics?

30 Sep 2019

Do you know a telecom company serving 8 million prepaid mobile subscribers generates approximately 30 million Call Detail Records (CDRs)? That is, amounting to 11 billion records annually!

If the same operator provides post-paid and fixed lines services. Then there is even more volume and variety of generated data.

Today, data is easily accessible in the form of real-time actionable insights. Such insights enable companies to respond in real-time to behavioral changes in the customer mind-set. It also helps to respond to threats from the market competition. This is exactly where Big Data and its analysis can win the battle against traditional BI tools.

Data has become one of the most strategic assets for the telecom industry. With this treasure telecom industries are sitting on gold mines of information which allows them to capitalize on these valuable data sets. 

Big data can bring the most value to the telecom industry in the areas of customer retention; customer segmentation; network optimization, planning and delivering, and upsell/cross-sell opportunities.

Improved Customer Experience

Customer experience is the key to sustaining market differentiation and reducing churn. Telecom companies can optimize and refine the customer experience by utilizing Big Data analytics. It helps in gaining a 360-degree view of their customers and their lifestyle. This view can be used for micro-segmentation of their consumer base and offer a targeted and compelling customer experience. Analytics can also be used for personalized recommendations as well as predict and prevent churn. 

  • Targeted marketing

Based on characteristics such as usage patterns, billing data, support requests, purchase history, service preferences demographic information, location, etc. telecom companies can provide customized products. This also enables companies to proactively present the right offer at the right time that too in the right context and to the right customers to improve conversion rates. For example – offer top-up plans or upsell recommendations based on data usage.

  • Predictive Churn

The impact of customer churn is affecting the Telecom industry today. So Big data analytics helps to bring together various data points including the quality of service, network, performance, subscriber billing information, details on calls to the customer care centers, and social media sentiments. This builds an effective model to predict and prevent churn. 

  • Customer Lifecycle

Telecom companies can provide the best offers and convert interested prospects into customers using real-time analytics that map the user journey. Data such as customer demographics, purchasing behavior and clickstreams are being combined with attributes such as location and content preferences for generating new and beneficial offers. This benefits the companies so that they can map specific customer’s interactions at various stages of the lifecycle to promote tailored offerings and campaigns.

  • Proactive Support

Utilizing big data telecom companies are building intelligence and analytics tools so that they can proactively identify issues and fix them. Also, they can provide a solution before it affects the customer. They have also introduced specialized bots so that customers can directly ask questions and get the answers. They help in proactively fixing and reaching out to customers to resolve the issues before they negatively impact the experience.

Network Optimization

In the telecom sector, the network is a vital resource. For every application, network capacity is a highly valuable resource. The telecom sector has started leveraging big data and analytics to effectively monitor and manage the network capacity, build predictive capacity models, and use it for planning network expansions.

  • Capacity Planning

Using the data that provides the correlation of network usage and network capacity, telecom companies can locate the highly congested areas where network usages are near to the capacity threshold. This analysis can be helpful for the capacity expansion plan.

For the areas having excess network capacity, they can run specific campaigns or promotions to increase usage. Predictive capacity forecasting models can also be developed based on real-time analytics and traffic. On the basis of data collected by comparing actual and forecasted traffic, they can plan for supplemental capacity in case of outages. This data can also help in drop-call area detection and corresponding cell-tower position prediction.

  • Investment Planning 

Telecom companies need to plan their investments and resources based on several parameters such as future connectivity needs, strategic objectives, projected ROI, forecasted traffic, customer experience, etc. The effective combination of network traffic data, customer experience metrics, revenue potential, and location data along with customer value data ensures that the investment is most effectively utilized.

  • Real-time Analytics

The telecom sector was dependent upon historical data for its network management. Now they have started using big data and analytical tools to build real-time capacity heat maps to monitor the quality of user experience and send alerts at the time of network congestion or potential outages. Big data analytics helps in continuously monitor the network activity and map future demand. Also, the engineers can monitor any drop in service performance at a specific location on the basis of real-time data collected from the cell towers.

Operational Analytics

The telecom sector is utilizing big data analytics for company operations such as minimizing revenue leakage, managing network, and cybersecurity to resolve customer issues and reduce usage risks.

  • Revenue leakage

For eradicating any revenue leakage and fraud, telecom companies are using big data-based solutions. These solutions help in analyzing both structured and unstructured data. Enabling them to gain a better understanding of the behavior of customers.

  • Network Security

A major concern for telecom industries is associated with network security. Nowadays optical fibers are utilized for networking and there are several problems associated with data leakage in this case. Data related to these risks can be analyzed in real-time to mitigate risk, detect incidents, and respond to breaches.


Telecom companies can access subscriber location, network usage, device, application usage, preferences, etc. This data can be used to create powerful statistics that can be of significant value to other businesses and verticals.

  • Data Analysis

The telecom sector has started providing data analytics as a service to other key verticals. There is a wide variety of applications and use cases for such analytics.

  • IoT/ M2M Analytics

Telecom companies have started providing complete M2M solutions. With ever-increasing IoT devices in the network, network analytics around IoT sensor traffic in the next area of exploration. They now can add location-based and geospatial elements to the streaming data. ultimately provide valuable insights into enterprise verticals. 


Big data promises to stimulate growth and increase efficiency and profitability across the telecom sector. It will enhance routing and quality of service. Fraudulent behavior can be identified immediately. It allows agents to flexibly modify subscriber plans immediately. It will help in customizing marketing campaigns to individual customers through location and social networking-based technologies. By utilizing the data related to customer behavior and usage, new products can be developed reducing costs and providing new sources of revenue. 

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