Role of Big Data in Telecom Industry with Case Studies

Generally, Telecommunication companies collect a large amount of data. This data includes mobile phone usage, records, network equipment, server logs, billing, and social networks. This data provides a lot of information about their customers and network.

Today, with the rapid expansion of smartphones, connected mobile devices, and telecom services over the internet, communications service providers (CSPs) need to handle a lot of data. They need to rapidly process, store and get insights into the diverse volume of data. This data generally travels across extensive networks.

So, Big data analytics can help CSPs and telecom industries to improve profitability. It can be enhanced by optimizing network services, enhancing customer experience, and providing security.

Big data analytics mainly helps in:

  • Predicting the most substantial network usage periods, and then further targeting steps to relieve congestion.
  • Identifying the customers facing the problems in paying bills and targeting steps to improve the recovery of payments.
  • Analyzing the root cause of the problem to prevent customer churn.

“ According to research by McKinsey, by digitizing customer service, customer satisfaction can be increased by 33%, while cutting costs by as much as 35%.”

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Interesting Big Data Case Studies in Telecom Industry

1. Reliance Jio

I hope you remember earlier we have only 1GB of mobile data to use for one month. And now, in just 24 hours or even less, we use 1Gb of data. Isn’t it surprising?…. All this is possible only because of Big Data. Such a revolutionary technology Big Data is.

How Big Data makes JIO so powerful ???

With the help of Big data, JIO acquired around 130 million users within one year of its launch only. And on 31st May 2019, Reliance Jio is declared as the second-largest network operator in India. Also, Jio is the fifth-largest in the world and the only company in India, which is 5G ready.

While other companies are busy underestimating the power of data, Jio used it to its full advantage and established an empire of its own in the telecom industry. Currently, other telecom companies are facing huge losses in the market. And these losses result in the merger of the two biggest telecom companies in the country – Vodafone and Idea.

Today, Reliance Jio is such a powerful company that it can alone rule the whole Telecom Industry.

2. Deloitte – Helped in Real-time customer insight and foresight with Big data analytics

Deloitte helped a large wireless telecommunications company to implement a platform. This platform collects, stores, and analyzes data from millions of customers and billions of transactions. This helps in achieving real-time marketing effectively.

Telecommunication companies need to generate insights into the current data. These help them to understand their customer base better and also open new markets. They also desire to explore how to shape new revenue streams and capture a more aggressive market share.

Deloitte helped the company to design a solution using the SAP HANA platform. It is an in-memory solution designed to deliver versatility and real-time analytics. It also involves the functions to perform scenario-based analysis and campaign stimulations in real-time. The solution uses live or recent data, thus opening the possibility of discovering new markets.

The company expects that using this solution, data lag will be reduced from the current 45 days, which facilitates operation in real-time. This solution will also help companies to leverage social media.

And by exploring it, a company will better understand the customer sentiments to achieve more effective CRM. It also helps in quickly respond to issues, and efficiently monitoring the success of its marketing groups.

Use Cases of Big Data in Telecom Industry

1. Fraud Detection

The most widespread fraud cases in the telecom industry are illegal access, fake profiles, authorization, cloning, behavioral fraud, etc. Fraud directly affects the relationship established between the company and the user.

Therefore, there is a need for fraud detection tools and techniques. This can be done by data visualization techniques that involve unsupervised machine learning algorithms. These algorithms get insights into customer and operator data and inform if any alteration in the regular data traffic over a network is found.

Thus Big data analytics helps in real-time monitoring to prevent fraud. The efficiency of this technique is very high as it allows an almost real-time response to any suspicious activity.

2. Predictive Analysis

Predictive analytics uses historical data to build forecasts. It helps telecommunication companies get valuable insights into customer data which further helps them to become faster, more efficient, and better. It also helps them to make data-driven decisions. Analyzation of customer preferences gives a better understanding of the customer.

3. Customer churn prevention

It takes a lot of effort to engage the customers for a long time. Analyzing the customer’s behavior and taking actions accordingly is necessary to prevent customer churn. Therefore, Big data enables smart platforms where customer transaction data and data from real-time communication streams are brought together.

Insights into this data help to disclose the customer’s feelings regarding a particular service. This allows telecom companies to address the satisfaction issues immediately and thus helps to prevent churn.

4. Lifetime value prediction

Customers always search for better and cheaper services. Therefore, it is essential to measure, manage, and predict customer lifetime value (CLV). Failing to predict CLV value may result in profit or loss.

The CLV model focuses on customer purchasing behavior, services utilized, activities, and average customer value. Big data helps to distinguish between profitable, nearly profitable, and unprofitable segments of customers. This helps to predict future cash flows.

5. Customer Segmentation

The content and strategy for each segment of the market can not be the same. So, telecom companies generally segment their market and then target the content according to each group.

The company usually divide the customers into four segments:

  • customer value segmentation
  • customer behavior segmentation
  • customer lifecycle segmentation
  • customer migration segmentation

Segmentation and targeting help in predicting needs, preferences, and customer’s reactions to the telecommunication services and products.

6. Product development

Product development is a complex process that needs control and careful management. Smart data solutions like data analytics help ensure the product’s high-quality performance according to the customer’s requirement.

Data analytics implementation helps in the data-driven product development process, internal feedback, and marketing intelligence.

7. Customer sentiment analysis

There are many customers associated with the telecom companies, due to the increase in the use of Internet services. And to maintain a good relationship with such a large number of customers is not easy. Therefore, Big data is used for customer sentiment analysis to understand them in a better way.

Customer sentiment analysis involves a set of methods for information processing. This analysis helps to assess the customer’s positive or negative reaction regarding a product or service. Modern tools also collect feedback from various social media resources to analyze the customer’s thoughts and need more appropriately.

8. Recommendation Engines

The recommendation engine is a set of smart algorithms which depicts the customer’s behavior. Based on that behavior, it predicts possible future needs of the product or services required by the customer. Recommendation engines use collaborative filtering and content-based filtering approaches.

Collaborative filtering relies on the analysis of data according to the user’s preferences and behavior. And Content-based filtering utilizes the attributes which show the relationship between the customer profile and the product or service customer chooses.

9. Price Optimization

The growth rate of the number of users associated with the Telecom industry is growing extremely fast. Therefore, pricing emerged as a tool to limit congestion and increase revenue at the same time.

The concept of a dynamic pricing approach involves mapping of lifetime values, tariffs, channels to calculate the price elasticity, and a pricing plan to combine this data. And based on these insights, the interdependencies between the promotion, pricing, and future revenues can be defined.

10. Network Management & Optimization

Network management and optimization allow looking into the customer’s historical data. And based on that data, telecommunication companies can obtain the root cause of the problems and complications customers find in-network connectivity.

It also predicts possible future problems and tries to optimize network management so that the company’s smooth functioning can be ensured.

Conclusion

Undoubtedly, Big Data brought a revolutionary change in the telecom industry.

It helps the telecom industry to establish its extensive networks firmly. It helps in fraud detection, analyzing customer’s preferences, watching traffic over the network, providing data safety, etc. In the coming years, Big Data will introduce more exciting features that will further help the telecom industry.

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