Big Data Analytics in Telecom Industry. Use Cases

The big data market value stood at USD 162.6 billion in 2022 and is projected to reach USD 273.4 billion by 2026 at a compound annual growth rate of 11%. [1] One of the many reasons behind these huge numbers is that big data solutions have revolutionized many industries, and the telecommunications industry is no exception. Read on to find out how the telecom industry can exploit the power of big data.

Telecom companies generate huge volumes of data from mobile phone usage, call detail records, server logs, network equipment, social networks, and billing. In other words, they are sitting on a goldmine of valuable data. But how can they derive actionable intelligence from the generated big data at their disposal?

The answer is through big data analytics. Big data opens up a sea of opportunities in the telecom industry, including improved customer experience, fraud detection, data monetization, and more.

Today, many telecom companies express a budding interest in big data, with global telecom analytics expected to reach USD 8.7 billion by 2025.[2]

Use cases of big data analytics in the telecom industry

Telecom companies can apply big data analytics in the following areas:

• Targeted campaigns
• Network optimization
• Customer experience
• Operational analysis
• Fraud detection
• Data monetization
• Price optimization
• Customer lifetime value

Targetted campaigns

Big data analytics help derive deep insights about the customer by reviewing how they interact with the operator’s products, services, and channels. Thus, you can categorize your customer base into different categories based on:

• Buying history
• Device
• Location
• Preferences

Out of the customer micro-segments, you can tailor targeted and relevant product offerings that entice the customer. For example, a customer who makes frequent calls to a specific country can be targeted with a specific marketing campaign in real-time.

Assuming they’ve just finished the overseas phone call, a promotional text can pop up on the mobile phone screen and say- ‘sign up to make calls abroad at 40 percent off. The user is more likely to subscribe to this voice bundle offer because it applies to their immediate situation.

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Network optimization

The biggest cost center for communication service providers is network costs, which account for a huge percentage of capital and operating expenses. Network optimization entails complex and quick evaluation of large volumes of data in real-time mode, including:

• Data usage
• Network logs
• Mobility patterns
• Hardware challenges
• Peak loads

With big data analytics, telecom companies can monitor their network performance. For example, in the event of network congestion or failure, the network service provider can quickly resolve the problem since they receive big data analytic reports in real-time.

Big data can also help with developing predictive capacity models to predict network problems that may arise in the future. This includes predicting peak network loads and allocating adequate network resources to relieve congestion. Communication service providers can also harness big data analytics to schedule network expansion, especially in areas with high congestion.

Customer experience

Churn rate, also known as attrition rate, refers to the percentage of customers that stop using a particular service within a forecast period. In a study report by Statista, the telecom industry has a churn rate of 21 percent, [3] closely linked to poor customer service.

The high churn rate in the telecom industry is a result of various factors, including slow networks and data speeds, inaccessible call center support, inaccurate billings, and so on. In an industry rife with customer dissatisfaction, the ability to offer a positive customer experience is the clearest path to improving revenue.

Based on big data analytics, telecom companies can develop a 360-degree view of customers via user data, customer profiles, network performance metrics, social media streams, and location data. Here are a few use-case examples:

• Omnichannel customer experience: The modern customer is tech-savvy and wants a digital, interactive engagement. They want the option to interact with the company via their preferred channel. With the help of big data analytics, telecom companies can stay on top of things at all customer touchpoints by ensuring consistent customer service across all channels, whether it’s via chatbot, live chat, or virtual assistance.
• Call detail record (CDR) analysis: CDR also plays a role in customer experience. Thanks to CDR, Telecoms can assess real-time metrics like packet loss, call latency, and call quality and take necessary action to meet customer expectations.
• Personalized offers and recommendations: Telecoms can use big data to segment customers into special categories based on their network usage data. This helps them present deeply personalized offers and services to the respective customer micro-segments, promoting customer retention and halting growing churn trends.

Operational analysis

Maintaining peak operational performance has a knock-on effect on reducing expenses, alleviating risks, and increasing revenues. Daily operational data sources offer practical insights across the operator’s organization.

For example, the service provider can monitor network traffic via heat maps in real-time and respond appropriately by increasing or reducing the network bandwidth during peak or off hours in a specified location.

Monitoring may also reveal the operator’s usage of network resources. This would, in turn, help with better resource allocation to prevent waste, leading to significant cost savings.

Real-time operational analysis may also help to establish the schedule for data updates and equipment maintenance. Thus, the telecom operator can best adapt the big data solution to their unique business needs.

Fraud detection

Invisible and immediate: those are the main traits of telecom fraud today. Cybercriminals are raking millions of dollars by employing sophisticated scams across different geographies and vanishing even before the communication service provider knows.

The revenue lost through Telco fraud amounts to billions of dollars each year. More worryingly, many operators opine that fraud schemes often bypass their defenses, and they cannot pinpoint what it is or how it happens.

Luckily, combining big data and machine learning solves telecom fraud. Telcos can detect up to 350% more fraud activities using data analytics and machine learning algorithms, according to research from the Communications Fraud Control Association. [4]

Already employed by major telecommunication operators, the strategy involves monitoring vast amounts of customer data to ascertain normal traffic patterns. All outliers are flagged immediately, and an alert is issued in real-time.

Data monetization

Telecom companies harness big data to generate tons of consumer-related information, such as:

• Demographics
• Location
• Network usage
• Used devices
• Different preferences

The outcome of processing such raw data is valuable, insightful data useful to the communication service providers and other companies. Thus, big data analytics in telecom industry allows operators to sell customer data to other third parties while maintaining users’ privacy.

For example, retailers may use operators’ subscribers’ location data to target customers nearer their stores with customized promotions. Big retail chains like Amazon and Wal-Mart may also acquire data about customer shopping habits from telecom operators to target consumers with tailored content.

Price optimization

The Telecom industry is rife with cutthroat competition amongst different service providers, competing to have the largest subscriber market share. That being said, product pricing is a key factor for operators competing to gain more subscribers.

Telecoms use big data technologies to review several real-time metrics and settle on a particular product’s price. The price is tested among different customer segments in different locations to settle on the best-optimized price.

For example, an operator may announce that users would get free minutes daily for calls made at a particular time frame under a particular voice bundle subscription. This sort of product offering comes about after analyzing data sources and then deriving actionable insights from them.

Lifetime value prediction

With fierce competition among Telecom operators, retaining existing customers is more profitable than acquiring new ones. However, retaining existing customers is not cheap. It comes with related costs, such as targeted offers, discounts, and loyalty rewards.

Thus, telecom companies should measure, manage, and forecast their customer lifetime value (CLV). CLV is all about forecasting all the value that the service provider can derive from its existing customers.

Big data analytics can process a large volume of customer data to distinguish between:

• High profitable customers
• Medium profitable customers
• Low profitable customers

The simple aspiration behind CLV is to identify the most valuable customers, their expected future spending, and the best way to engage with them, leading to more sales. This is a crucial metric to analyze because it determines the number of resources to be allocated to every customer segment.

Final thoughts on big data in telecom companies

Today, the application of big data in the telecom industry is multi-fold. Telcos can leverage big data technologies to turn vast amounts of raw data into actionable insights that predict customer lifetime value, create positive customer experiences, and perform real-time operational analytics and data monetization.

If done right, big data is the fast route to customer retention, growing subscriber base, and profitability. By leveraging big data analytics in telecom industry, companies can gain a competitive edge by using real-time analytics to improve network performance and increase efficiency, identify new opportunities for revenue generation, and optimize marketing and sales strategies to retain customers and attract new ones.

Furthermore, big data analytics can provide valuable insights into customer behavior, such as preferences and usage patterns, which can be used to create personalized services and offerings. This not only improves customer satisfaction and loyalty but also helps to reduce churn rates.

In conclusion, the application of big data in telecom companies is crucial for success in the current and future telecom industry.

Leverage the power of big data analytics in telecom industry to guarantee your position in the future of the telecom industry.

To find out how it can be done, see our big data consulting services.

Big Data Analytics in Telecom Industry. Use Cases – FAQ

What is big data analytics in the telecom industry?

Big data analytics in the telecom industry refers to the process of analyzing large volumes of data generated by telecommunications networks and services to extract valuable insights. These insights can be used for various purposes such as improving network performance, enhancing customer experience, and optimizing business operations.

What are some use cases of big data analytics in the telecom industry?

Some common use cases of big data analytics in the telecom industry include predictive maintenance of network infrastructure, churn prediction to retain customers, personalized marketing campaigns based on customer behavior analysis, network optimization to improve quality of service, fraud detection to prevent unauthorized usage, and real-time monitoring of network performance.

How does big data analytics benefit the telecom industry?

Big data analytics offers several benefits to the telecom industry, including improved operational efficiency, better decision-making based on data-driven insights, enhanced customer experience through personalized services, increased revenue through targeted marketing strategies, and proactive identification and resolution of network issues.

What technologies are used in big data analytics for the telecom industry?

Technologies commonly used in big data analytics for the telecom industry include data mining, machine learning algorithms for predictive analytics, real-time data processing frameworks such as Apache Kafka and Apache Flink, distributed computing platforms like Apache Hadoop and Spark, and data visualization tools for presenting insights in a comprehensible manner.

How does big data analytics contribute to the future of the telecom industry?

Big data analytics plays a crucial role in shaping the future of the telecom industry by enabling innovation, driving digital transformation, and creating new opportunities for growth. By harnessing the power of big data, telecom companies can stay competitive in a rapidly evolving market landscape and meet the evolving needs of their customers effectively.

This article is an updated version of the article from Aug 16, 2022.

References

[1] Marketsandmarkets.com. Big Data Market. URL: https://www.marketsandmarkets.com/Market-Reports/big-data-market-1068.html. Accessed July 26, 2022
[2] Kbvresearch.com. Telcom Analysis Market. URL: https://www.kbvresearch.com/telecom-analytics-market/. Accessed July 26, 2022
[3] Statista.com. Customer Churn Rate By Industry-US. URL: https://www.statista.com/statistics/816735/customer-churn-rate-by-industry-us/. Accessed July 25, 2022
[4] Cfa.org. Fraud Loss Survey. URL:  https://cfca.org/wp-content/uploads/2021/02/CFCA-2019-Fraud-Loss-Survey.pdf, Accessed July 25, 2022