Role of Big Data Analytics in Telecom Industry

We are moving toward an information era in which real-time acumen can and must be converted to enable companies to address changes in their customers’ behavior in real-time or to respond swiftly to market-based threats. This is exactly where big data and its interpretation will win the battle against conventional BI instruments. Telcos today have access to extraordinary quantities of data sources, including client profiles, user data, data network data, patterns of consumer use, location data, downloaded apps, etc., with the growing proliferation of smartphones and growth in mobile internet. The Big Data is all these data combine together.


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Big Data Analytics in the Telecom Sector


  • Customer Experience


The greatest marketing practice of all is a happy client. Companies have to keep up to date with the latest technology to help them thrive in this ever-evolving and highly competitive environment. A dashboard can be built, which provides a 360-degree view of customer needs. Customized deals can be delivered to consumers using these dashboards on the basis of their patterns of data usage. Appropriate deals can be sent to the customers at the right time to raise customer numbers. The easiest way to attract customers is to support them if they are in trouble. Big Data has been at the forefront of its growth with the digitalized customer service systems. This includes online chatbots, emails, SMS, social media, etc. Today Telcos refines and optimizes the customer experience, which is key to market growth and churn reduction. Telcos use Hadoop and big data analytics to offer their customers a true 360-degree view of their lives. The telco would then target the micro-segmentation of its consumer base, offer an inspiring customer experience, develop personalized offering recommendations, and prevent/develop churn on the basis of the detailed customer profiles.

As an example, the following is:

1. Targeted Marketing

Similar product offers are focused on features such as user models, billing details, support requests, purchasing history, demographic information on service preferences, location, etc.

2. Predictive Churn

Telco also uses massive data analytics effectively to collect different data points, including service quality, networking results, information for user accounts, details on customer support center calls, and social media sentiment analysis, in order to develop an efficient model to predict and avoid churning.

  • Customer lifecycle
  • Proactive support:


  • Network Optimization

Network traffic is one of the telecommunications company’s most essential properties. Big Data Analytics is used by telecommunications companies to track and control their network resources and thereby make informed choices for the expansion of their network data platforms. Telcos aim to produce more efficient data network models through predictive analyzes. The decision for network expansion can be made clear by examining real-time network traffic in highly congested areas. It helps to reduce the total network growth costs. The introduction of suitable deals in regions with relatively low network traffic would help to improve traffic and thus produce more revenues.

  • Capacity planning: In order to prioritize increased capacity for the creation of new technologies, highly overcrowded areas may be defined by correlating network usage to the real-time capacity uses.


  • Investment planning: With so many factors, telcos need to be willing, based on potential communication needs, strategy goals, expected ROI, projected traffic, clients’ experience, etc., to efficiently prioritize investments and resources. 


  • Real-time analytics: Though Telcos now started to use Big Data and Analytics tools to create heat maps for real-time, to track the quality of users’ experience and to submit network congestion warnings or possible outages



  • Operational Analysis

Telcos use the large data around the core Telco operations to drive domestic performance, enhance the processes, and reduce costs. We are beginning to implement Hadoop-powered large-format approaches on all areas, including plugging and minimization of revenue leakage, network management, and cyber protection to the constructive detection of and resolution of consumer issues in order to reduce truck rolls.

  • Revenue leakage
  • Network / Cybersecurity


  • Data Monetization


Telcos have a unique benefit of accessing demographics for customers, user position, network use, currency, device use, preferences, etc. When all data telcos begin to mine, model, aggregate, and anonymize these data sets, they generate strong statistics that can be useful for other companies and verticals.

  • Data analysis
  • IoT / M2M analytics


Big data pledge growth and productivity and gain in the telecommunications value chain.

  • Enhancing routing and QoS through real-time analysis of network traffic
  • Real-time analysis of call records to instantly detect fraudulent activity
  • Allowing call center agents to alter consumer strategy flexibly and profitably instantly
  • Changing customer marketing plans through location-based and social communication technologies
  • To create new products by means of insights into consumer behavior

Big data can also open up new revenue streams, including supplying third parties with information on customers. Big data analysis also tends to reduce the CAPEX or OPEX relevant to corporate transactions.


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