Big Data and Analytics – The Big Driver for Telecom Profitability

Feb 20, 2018

telecom analytics metrics

In today’s interconnected world, telecom companies are playing pivotal in facilitating the transfer of information and communication across the globe. Rapid globalization has augmented the growth in network traffic leading the telecom companies to increase infrastructure investments. However, such investments don’t always impact the profitability of telecom companies positively. So telecom operators have to look elsewhere to optimize their operations and increase profitability. Big data and analytics is the solution for telecom operators looking to enhance the overall value of their business. Big data has the capability to handle large datasets generated by the telecom operators to identify problem areas and new revenue opportunities.

Customer Churn Prediction

Of all businesses in the world, the telecom industry is the one which is plagued by high customer churn rates. Customers usually churn when they are dissatisfied with their current service provider or when they find better offers elsewhere. An average customer retention rate of 60-80% in the telecommunication industry doesn’t seem that impressive. As a result, telecom operators are resorting to big data and analytics to predict customer churn. To do so, telecom companies use various data sets including usage patterns, social media feedback, negative comments, and transaction history. A robust machine learning algorithm is then created to predict which customers are more likely to churn accurately. Preventive measures such as improved offers, reduced rates, and customized tariffs can then be employed to stop such churn.

Customer Insights

Telecom operators have access to a large repository of data related to customers. With advancements in data storage and data processing technologies, telecommunication companies can store and analyze diverse data sets including customer information, device information, usage data, and location data. Big data and analytics solutions can go through all such structured as well as unstructured data sets to generate actionable insights. Various metrics and tools such as sentiment analysis, churn analysis, and clickstream analysis can be used to effectively understand the customer and personalize their experiences. For instance, based on customers’ needs, behavior, location, and device details, telecom companies can offer tailored products to meet their requirements.

To know more about how big data analytics is driving the profitability of telecom companies contact us today.

Identify New Business Areas

The revenue source for mobile operators has changed significantly over the past few years. With revenues from voice and messaging drying up, data sales make up a large part of their earnings. They always need to be on their toes to identify new opportunities in such a dynamic world. Consequently, major operators are exploring new business areas including IoT integration, 5G network, and cloud computing. Additionally, telecom companies can explore new business models, which look to increase their profitability by offering location-based and event-based campaigns to identify cross-selling and up-selling opportunities.

Improve Service Quality

The key to improving profitability in the telecom industry is tied closely with high customer satisfaction rates. As a result, mobile operators are constantly looking for ways to enhance the quality of service. Apart from network infrastructure management, big data and analytics can help companies improve network performance and optimize capacity. For instance, operators can improve network performance by optimizing call routing and quality of service (QoS). They do so by using real-time CDR analysis and location-based analysis, which provides them insights on which location should be prioritized to improve the 4G network. Service providers can effectively plan maintenance schedules and enable proactive care with the help of big data analytics.

Enhanced Security

Telecom companies rely on big data analytics to identify anomalous and fraudulent activities. They do so with the help of sophisticated machine learning algorithms that monitor huge data volumes including sentiment data, customer demographics, usage patterns, geographic trends, and behavior data. Mobile operators can predict the likelihood of unexpected behavior and take corrective action with the help of analytics-driven surveillance. By enhancing security measures, big data analytics can help improve company profitability through damage limitations.

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