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Big data in telecom can not only elevate companies’ services and generate more revenue but also create a more customer-centric approach to stay ahead of the competitors. As the number of connected devices and generated data increases, the need for telecom data analytics becomes evident.
Big data analytics in telecom: market overview
Telecom companies adopt big data analytics to make the most of their data. It helps them collect, analyze, and interpret information more deeply to eliminate service pain points, improve customer experience, and predict future outcomes. Being a widely used technology, big data analytics comes with the following compelling statistics:
- According to Global Market Insights, the global big data analytics market in the telecom sector is expected to reach $10.5 billion by 2034.
- According to Precedence Research, the U.S. telecom analytics market is expected to reach $9.06 billion by 2034.
These numbers highlight that telecom analytics is widely recognized as one of the most important ways to improve the industry. But what exactly is big data analytics in telecom?
What is big data analytics and why does it matter?
Traditional information is relatively small in volume and has a more structured format, suitable for standard databases and tools. In contrast, big data is much more complex. It implies large datasets consisting of different types of information collected from various sources. The three V3s define the concept of big data: volume (massive scale of data), variety (diverse information types and sources), and velocity (speed of information generation).
However, it’s not enough to collect the information. The most important thing is to understand all the insights that may be hidden within it. For this purpose, companies of all sizes and industries use big data analytics as a way of processing and analyzing complex and different datasets to gain deeper business insights. Regardless of data analytics types, it includes the following stages:
- Data collection. Analysts collect structured and unstructured information from various sources.
- Data storage. The collected information is stored in a data lake or a data warehouse for further information management.
- Data processing. It includes data cleaning, transformation, and integration to ensure the data are in a suitable format for analysis.
- Analysis. The process involves various techniques and methods (machine learning, data mining) to explore and extract valuable insights from complex information.
With a basic understanding of data analytics, let’s look at how telecom companies leverage this technology.
10 use cases of big data analytics in telecom
Here are some of the most compelling applications of telecom analytics.
1. Improved customer experience
A deep understanding of customer needs and behaviors is the key to keeping current customers and winning over new ones. Telecom companies analyze information from call histories, subscriber usage patterns, and location. Then, they create detailed customer profiles and provide personalized offers and promotions.
Moreover, analytics is also beneficial for the prediction of future needs. With the help of it, telecoms can forecast their customers’ behavior and deliver what they want in the right place. Therefore, predictive maintenance based on data analytics helps companies improve customer satisfaction by 20–25%, and establish strong relationships with them.
2. Customer churn prediction
Powered by big data, predictive analytics helps telecom companies identify customer churn risks. Whether it is due to network issues or poor customer service, it allows companies to continuously monitor and manage any problems with their services and make strategic decisions to improve customer retention.
Based on the collected information, telecom businesses can detect early signs that customers may consider switching to a competitor or abandoning their service. This allows companies to predict churn early and reduce it up to 15–25%.
3. Network optimization
Telecom companies can significantly improve their network operations by analyzing information from OSS systems, network equipment, user devices, and other sources. By doing so, they can identify network congestion and bottlenecks, reducing downtime in networks by 30%, and ensure a smooth data flow.
Moreover, analytics of data helps in load balancing, allowing telecom companies to distribute network traffic evenly. Such a proactive approach helps prevent overloads on specific network nodes, resulting in consistent service quality, fewer disruptions, and improved operational efficiency. By identifying and resolving network issues promptly and reducing downtime, telco companies can ensure that customers receive the level of services they expect.
4. Predictive maintenance of equipment
Apart from network reliability, telecom companies must maintain a high level of equipment efficiency to prevent downtime during service delivery. Big data analytics allows them to analyze network performance, equipment condition, and environmental factors. As a result, telecom providers can identify anomalies and early warning signs that precede failures and take actionable stages to overcome these problems.
Predictive analytics allows telecom companies to plan maintenance ahead of time. In some cases, the process involves replacing worn components, optimizing network configurations, or performing preventive maintenance on equipment. As a result, unplanned downtime reduces by 30–50%, equipment replacement costs are minimized, and resource allocation is improved.
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5. Price optimization
The telco industry deals with various operational costs, such as network maintenance or personnel costs. Telecom companies use analytics to gain insight into their operational expenses from BSS systems, billing, tariffs, and identify opportunities for cost reduction. For example, analytics can monitor energy consumption across the network infrastructure. Telecom operators can improve energy-intensive areas, resulting in substantial savings on energy costs.
Also, big data analytics is used for price optimization. Telecom firms leverage big data analytics to set reasonable prices for products and services. By aligning these prices with factors like customer lifetime value, tariff plans, and distribution channels, companies can maximize ROI and profitability. By improving pricing strategies based on profit and revenue, telecom providers can increase sales and, most importantly, accelerate their growth.
6. Fraud detection
Today, 69% of telecom organizations consider fraud prevention as a top strategic priority. Telecom providers are highly susceptible to fraud due to the high volume of transactions and the complexity of their networks. SIM swapping, account seizures, and interconnection bypass fraud can cost a telecom company millions of dollars every year.
The International Revenue Share Fraud (IRSF) is a significant concern in the telecom industry. In IRSF, hackers create premium-rate phone numbers and generate high call volumes. This forces the operators, from which the calls originate, to pay high termination fees, resulting in direct financial losses.
Telecom providers use advanced analytics and system monitoring to combat security risks. Analytics helps identify anomalies in call records, financial transactions, or customer behavior, enabling prompt detection of fraudulent activity. For example, telecom providers can monitor network performance in real-time, identify suspicious events, such as multiple login attempts from different locations, and immediately alert security teams.
The value of telecom data
Telecom data analytics is enough to optimize almost every process inside a company, but the biggest revenue potential is data products. Telecom operators use rich, longitudinal datasets about mobility, connectivity, device usage, and network behavior. When combined with robust ML techniques (anonymization, aggregation, federated learning), this data can be transformed into products that are both compliant and extremely valuable.
Unlike internal optimizations, these data products scale beyond the operator’s own cost base and are not capped by Average Revenue Per User (ARPU). They also benefit from strong defensibility. But these products come with some challenges and the main of them is governance. Operators need to earn and maintain trust, and prove that data is properly anonymized. Those who succeed can transform from basic connectivity providers into data and intelligence platforms with very different economics.
7. Customer segmentation
As more information is generated, telecom providers use more sophisticated methods to segment their customers. Instead of relying on traditional data such as demographics, age, or location, companies use bid data analytics to investigate call records, browsing history, and social media activity to gain more actionable insights. By doing so, telecom companies can better understand their customer needs.
Depending on the segment, telecom firms can create detailed profiles of their customers and offer them targeted promotions, customized service plans, or specific content. The detailed level of segmentation allows telecom providers to offer a more personalized experience, which can lead to increased customer satisfaction and improve marketing ROI.
8. Customer lifetime value (CLV) prediction
In today’s competitive market, customers are constantly seeking the best value, switching easily to competitors. To mitigate this, telecom providers use analytics to accurately predict customer lifetime value (CLV). Using machine learning models, historical data, and segmentation, CLV is forecasted for each customer. These models consider a variety of factors such as customer longevity, service usage, average revenue per user (ARPU), and churn likelihood.
Customer lifetime value predictions enable telecom providers to create precise strategies to improve customer retention, boost revenue, and gain an edge over competitors.
9. Data monetization
Big data enables telecom companies to collect a wealth of information about users, including demographics and location data, network and app usage, device information, preferences, and more. While this data is confidential, it can be anonymized and aggregated so that individual users cannot be identified. After that, telecom companies can sell this data (without violating user privacy) to other companies across various industries, such as healthcare and financial services. Further, this data can be used by businesses to make strategic decisions.
For example, tourism companies can use this data to learn about peak travel periods and popular destinations. Then, they can plan promotions or develop new services tailored to users. Retailers can use this data to identify high-traffic areas to open new stores or to improve their marketing campaigns.
10. Targeted marketing
Telecom providers harness the power of big data to monitor real-time marketing efforts. By analyzing customers, such as purchase history, service preferences, and feedback, companies can develop a comprehensive strategy to improve marketing campaigns accordingly. Data visualization helps telecom businesses test different pricing levels, advertising packages, or service levels without having to act blindly. When a specific campaign underperforms, they can promptly make adjustments to optimize it, resulting in improved conversion rates and reduced costs.
Benefits of big data analytics in telecom
The use of big data analytics in telecom provides companies with numerous tangible benefits that improve their growth.
Improved network performance
Telecom companies use data analytics to monitor and analyze network performance in real time. By doing so, they can detect network congestion and enhance overall network performance, increasing bandwidth usage by 25%.
Better customer satisfaction
Telecom operators gather customer data from multiple sources, including call records, service usage, and customer feedback. By analyzing this data, they can offer personalized services and promotions that improve customer satisfaction by up to 15%.
Enhanced risk management
With big data analytics, telecom companies can process historical failures, customer behavior data, or real-time network data. By doing so, they are able to detect potential risks, such as abnormal traffic spikes or unusual usage patterns, and take immediate steps to address them early.
Reduced operational costs
Using big data analytics, telecom companies can monitor their networks in real time and create self-optimizing networks (SONs). By automatically adjusting network parameters based on current traffic and performance data, companies reduce the need for manual intervention and lower operating costs.
Revenue growth
With big data analytics, telecom companies can effectively manage network capacity, reducing revenue losses due to congestion and outages. They can analyze customer behavior to offer more relevant tariffs and additional services. These improvements lead to better telecom service and revenue growth.
Challenges of big data analytics in telecom
Despite its advantages, the use of big data analytics in telecommunications can also pose certain problems. Being aware of them in advance, telecom companies are more likely to avoid them.
Integration issues
Telecom providers collect data from various sources. Each source produces information in different formats, structures, and protocols. Thus, effectively integrating and analyzing this data is a complex task that requires specialized processing techniques and tools.
Moreover, telecom companies often operate legacy systems that may be incompatible with modern data formats or communication protocols, leading to slow down data processing and limited analytics capabilities.
Solution: To streamline data integration from multiple sources, companies can use platforms such as Apache Camel, MuleSoft, and IBM DataStage. They should use data normalization, schema mapping, and data transformation techniques to ensure that data from different sources is standardized, aligned, and formatted correctly. In addition, they should use middleware or API gateways to help them integrate legacy infrastructure with modern platforms.
Data quality
An average, poor data quality costs organizations at least $12.9 million per year. Data quality issues, such as missing values, inconsistencies, and inaccuracies, can arise for multiple reasons, including network errors or integration problems. Thus, incomplete or inaccurate data can lead to flawed insights and incorrect decision-making.
Solution: To overcome this problem, companies need to implement robust data governance frameworks that include establishing data quality standards, conducting regular data audits, applying data cleansing techniques, and implementing data lineage tools. To make data management easier and more automated, businesses should use tools such as Trifacta, Talend Data Quality, and Apache Griffin.
Security issues
As telecom organizations collect, store, and analyze large amounts of sensitive data, they face increasing security and privacy risks. AT&T, a major telecommunications company, experienced a massive data breach affecting more than 86 million customers, whose personal data allegedly ended up on the dark web. Each data breach can cost time, money, and reputation. The complexity of telecommunications networks, combined with the scale of data generated by mobile systems, IoT devices, and customer interactions, makes it challenging to implement robust security measures.
Solution: To mitigate security and privacy risks, telecom companies must implement comprehensive data protection measures. These include end-to-end data encryption, data anonymization, and masking. They should establish user roles, permissions, and multi-factor authentication controls to regulate data access. They need to implement solutions to monitor user activity and detect suspicious patterns, ensuring the security of communication protocols, firewalls, and networks.
Network failures
Network downtime can occur due to various factors, including technical glitches, hardware failures, cyber-attacks, or even natural disasters. A reliable and consistent network connection is essential for an effective big data initiative. Any interruption in network service may result in data loss, processing delays, or vulnerabilities that may lead to poor network connection and customer churn.
Solution: Telecom operators can address this issue by using predictive maintenance. It relies on analytics and enables companies to monitor equipment status, detect anomalies, and schedule repairs before problems occur. In addition, companies can monitor network performance in real-time. It allows them to identify network congestion or bottlenecks and take immediate steps, such as load balancing, to overcome them.
High costs
Although large, complex volumes of data offer enormous opportunities to improve the telecommunications sector, leveraging bid data analytics requires significant financial investment. Telecom companies must invest in building new infrastructure, implementing new technologies and tools and hiring qualified specialists. It may be too expensive and ,as a result, challenging for many companies.
Solution: To optimize implementation costs, companies should choose from available cloud-based solutions, such as Google Cloud, Microsoft Azure, to eliminate the costs associated with deploying and managing their own equipment. An additional option is to contact big data consultants. They can help you analyze your data infrastructure, identify project risks, and develop a comprehensive strategy to prevent unnecessary costs.
Real-life examples of using telecom big data analytics
There are many telecommunications companies that have already used analytics in their operations and have benefited from the results.
Vodafone
Vodafone is the multinational telecommunications company operating in 21 countries that provides connectivity to individual customers and businesses. In partnership with Celfocus, the company has launched its Vodafone Analytics platform. It is used to collect, process, and analyze large volumes of data generated by Vodafone’s operations and customer interactions, including mobility patterns, visitor profiles, in-store experiences, and more. Data can be retrieved in real time from any point in the country.
By using visualization tools, such as Citilogic and Carto, this platform offers its users an easy way to access and understand collected data to make strategic decisions. By leveraging this platform, companies can optimize business operations, improve accuracy, and enhance ROI without requiring extensive in-house research.
Deutsche Telekom AG
Deutsche Telekom is one of the largest telecommunications operators in Germany, offering a wide range of services, including mobile and fixed-line communications, broadband, and digital solutions. The company uses big data analytics to collect customer behavior data, improve personalization and streaming quality. It examines how individuals interact with their mobile plans, data streaming habits, and how they use their devices.
Based on the analysis, Deutsche Telekom dynamically generates micro-segmented offers and optimizes video content delivery by adjusting the quality based on the user’s internet connection speed. Improving video content reduces buffering, makes videos more enjoyable, and increases the overall user experience score (MOS) for media-heavy users.
British Telecom (BT) Group
British Telecom Group is a UK telecommunications giant operating in approximately 180 countries. The company uses data analytics to enhance its operations, including predicting service issues, such as network outages, slow internet speed, and optimizing call center operations. BT has partnered with professional services and information technology company CACI to use its CACI Forecaster solution.
Using Forecaster, BT can better understand the factors influencing demand for call center services, including the impact of marketing campaigns and caller data. By automatically processing and visualising this data, BT delivers higher service levels, optimises planning, and reduces costs through accurate demand forecasting and more efficient resource allocation. As a result of the partnership, BT can now generate 66 forecasts in the same time it used to take to generate 11 forecasts manually. With Forecaster, BT can gain deeper insights with fewer call center resources.
AT&T
AT&T provides telecommunications, media, wireless networks, digital television, and technology services. It strategically invests in the development of AI-based network technologies (5G) that leverage large datasets. The company uses big data analytics to monitor its network in real time. By analyzing data from millions of devices, AT&T predicts congestion and reroutes traffic to ensure uninterrupted service. To achieve this, AT&T invested in the following two areas.
Edge computing solutions for IoT devices. The organization develops innovative solutions that bring computing power closer to the Internet of Things (IoT) devices. This approach allows real-time data processing of IoT apps to run quickly and efficiently, from autonomous vehicles to “smart” city solutions with reduced latency.
Intelligent Software-Defined Networking (SDN). It is a networking approach that uses software-based controllers or APIs to interact with the underlying hardware infrastructure and direct traffic on the network. With SDN, networks become more flexible, efficient, and responsive to changing demands, while companies can address network-related issues proactively.
Trends of data analytics in the telecom industry
Like many other industries, telecom is experiencing a dynamic shift driven by integrating big data with the following technologies.
- Edge computing is used to manage information at the edge and is essential to reduce latency and enhance real-time data processing;
- Data as a Service (DaaS) allows telecom operators to access and use information that is stored in the cloud without the need for local data storage or management;
- Advanced artificial intelligence and machine learning are used for real-time analysis and predictive decision-making.
- Hybrid clouds combine private and public cloud infrastructure to offer greater flexibility and scalability for app deployment without vendor lock-in;
- The 5G network generates even more data, and telecom companies can use network analytics to optimize its performance and ensure super fast services.
Conclusion
The question “What is the future of big data analytics in telecom?” often comes up. It is clear that telecom providers are unlikely to move away from big data and data science. Instead, they will continue to invest in it. The shift to a data-driven world is inevitable, as approximately 463 exabytes of data are generated globally every day.
For this reason, telecommunications analytics and big data are a powerful combination, and their widespread use is only a matter of time. The use cases of big data analytics mentioned above, from improved customer service and network optimization to predictive outcomes, have already shifted how telecom companies operate. Thus, the future of telecom companies lies in the deep integration of big data into telecom infrastructure, enabling more efficient, customer-centric service delivery. SoftTeco can help you achieve this and other benefits by offering end-to-end telecom software development services tailored to your business needs and budget.
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