Data Analytics in the Telecom Industry: Use Cases, Challenges, and Trends

As new technologies like 5G continue to roll out and the number of connected devices increases, the information generated by the telecom sector keeps growing. To thrive in the data-driven world, telecom providers must use big data in a way that extracts as much value for them as possible.  

Through big data analytics, organizations can not only elevate their services and generate more revenue but also create a more customer-centric approach to stay ahead of the competitors. Towards this, we will outline how companies leverage telecom analytics to take maximum advantage of its potential.

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 (structured, unstructured, and semi-structured) that is 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 just collect the information – it is important to understand all the insights that may be hidden within it. For this purpose, a particular field of research is used – big data analytics. We can define it as a form of advanced analytics that includes different analytical techniques and methods to deal with complex and large datasets. 

The main stages of big data analytics are:

• 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: includes cleaning, transformation, and integration to ensure that the information is in a suitable format for analysis;
• Analysis: the process involves various techniques and processes (machine learning, data mining) to explore and extract valuable insights from complex information.

With an understanding of the basics of data analytics, let’s look at what benefits and prospects it brings to the telco sector.

The role of telecom analytics

An average telecom company collects large volumes of information regularly. This data holds valuable insights into customer behavior, network operations, equipment status, and service quality. However, a significant portion of this information remains unused or inaccurate due to many issues. It makes it difficult for organizations to exploit its full potential. Thus, lack of access to effective analytics can result in poor customer service, mistrust, and dissatisfaction. As a result, this will harm the revenue and reputation of a company.

Telecom companies adopt big data analytics into their processes to make the most of their information. Analytics helps them collect, analyze, and interpret information more deeply to eliminate their pain points and predict future outcomes, improving decision-making both in real-time and in the future. According to Precedence Research, the telecom analytics market was $6.19 billion in 2022 and is expected to reach $23.66 billion by 2032.

The use of telecom analytics is widely acknowledged as one of the most important ways to improve the industry; however, it also presents a set of challenges for telecom operators to overcome.

Challenges of big data analytics in telecom

No doubt, telecom companies can benefit significantly from using large sets of information – however, companies may face some challenges when using data analytics. Before we get into the main challenges, let’s look at the factor that telecom companies should consider first.

In terms of how telecom service providers use big data, different companies are at various stages of their data maturity:

• Info-archive: companies that have not yet initiated efforts to leverage large information effectively, and they often face limitations in terms of analytical capabilities;
• Info-familiar: companies that have begun to implement big data into their activities, but they need to strengthen coordination and create a reliable and integrated data structure;
• Info-smart: companies that have successfully developed and integrated large data infrastructure and adopted a unified data strategy aligning with their business objectives and advanced analytics.

This classification gives companies an understanding of what stage of maturity they are at and, depending on the level of maturity, helps identify critical problems associated with large information. In light of this, companies are able to develop a customized strategy that will maximize the capabilities of large data. Now, let’s get back to the most common concerns to overcome when it comes to working with big data.

Diverse data sources

Telecom providers collect data from various sources, including call detail records, network logs, customer history, etc. Each source produces information in different formats, structures, and protocols. Thus, effective integration and analysis of this data becomes a complex task, requiring specific processing techniques and tools. Since telecom companies often work with legacy systems, they may be incompatible with some modern data formats or integration methods. It thus poses challenges regarding the maintenance of data quality, consistency, and management.

For this reason, companies need to invest in robust and scalable data integration solutions. It enables to standardize data and make it more accessible and analytics-friendly.

Disparate and siloed data sources

The telecom industry operates extensively, encompassing various regions and countries. As a result, data is distributed across diverse physical and digital locations and is most often not centralized. So, many telecom companies store their data in isolated silos or databases. These data silos emerge due to various factors, such as legacy systems, separate departmental use of the data, or the absence of integration strategies. As sets of information are collected in varying formats, structures, and quality levels, data consolidation and analysis become more complex and time-consuming.

Data inconsistencies

Telecom information often suffers from quality issues, such as missing values, inconsistencies, and inaccuracies. Data quality issues can arise for multiple reasons, including network errors or integration problems. For example, missing values in call records or incorrect customer details can hinder the accuracy and reliability of the analysis. Thus, incomplete or inaccurate information can lead to flawed insights and incorrect decision-making.

Data preparation

Much of the collected information needs preprocessing, cleaning, and transformation before it can be used for analytics. For this reason, data cleaning and preparation can be a labor-intensive, time-consuming, and costly process. Errors are often common, and the quality of the resulting data may not always meet expectations. Analytics must analyze extensive datasets due to an ever-increasing volume of information. This challenge becomes more acute as data expands. Effective data preparation is essential for successful analytics.

High costs

Although large and complex information offers enormous opportunities to improve the telecom sector, managing its benefits requires significant financial resources. Companies must carefully plan their budgets and effectively allocate resources to work with large information. Telecommunication companies must invest in maintaining and integrating a reliable infrastructure, repairing equipment, implementing advanced technologies, and hiring qualified specialists. 

Another important aspect is that telecom companies must invest in robust cybersecurity measures to comply with industry regulations and ensure data protection. This, in turn, leads to additional costs as well.

Poor customer service

Due to the ever-changing industry and customer needs, poor customer service is a persistent challenge in the telecom sector. Reasons for this can range from ineffective call center support, slow data transfers, and billing inaccuracies to technical problems. Inadequate analysis can negatively impact customer service too, leading to customer distrust, loyalty, and the company’s reputation. 

Companies should invest in analytics of large sets of information and its solutions to identify customer preferences, needs, and behavior. This can help telecom providers create better service strategies and ensure customer satisfaction. 

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. Therefore, if companies do not have reliable software with a stable network, it may lead to problems such as bad performance or security issues.

Use cases of telecom data analytics

As the telecom industry finds ways to tackle the abovementioned issues, it simultaneously uncovers remarkable opportunities to make the most of its information. Here are some of the most compelling use cases of telecom network analytics.

Improved customer experience 

A deep understanding of customer needs and behaviors is the key to keeping current customers and winning over new ones. Think about it – your telecom provider knows exactly what you’re looking for and provides you with that. But how exactly is it real? Telecom companies analyze information from call histories, Internet usage patterns, and location. They then create detailed customer profiles through analysis and provide personalized services and promotions.

Moreover, analytics is also beneficial for the prediction of future needs. With the help of predictive analytics, telecoms can forecast their customers’ behavior and deliver what they want in the right place. Due to this, data analytics allows for highly personalized customer service, helps companies keep pace with customers’ changing needs and helps build strong trust and loyalty among them.

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, predictive analytics allows companies to continuously monitor and manage any problems with their services and make strategic decisions to retain customers. 

Based on the collected information, telecom businesses can detect early signs that customers may consider switching to a competitor or abandoning their service. The ability to predict churn early and solve customer problems directly contributes to increased satisfaction and to establishing long-term customer relationships.

Network optimization 

Another example of effective use of analytics is optimization of network performance, network reliability, and efficiency. Telecom companies can significantly improve their network operations by analyzing information from network equipment, user devices, and other sources. One of the ways to achieve this is that telecom providers can identify network congestion and bottlenecks to reduce downtime and ensure a smooth data flow.

Moreover, analytics of data helps in load balancing, allowing telecom companies to distribute network traffic evenly. This proactive approach helps prevent overloads on specific network nodes, resulting in consistent service quality and fewer disruptions. By identifying and resolving network issues promptly and reducing downtime, telco companies can ensure that customers receive the level of services they expect.

Fraud detection 

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, putting its financial stability at risk. 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, leading telecom operators to incur termination fees and financial losses. 

Telecom providers use advanced analytics and system monitoring to combat any security risks, also relying on analytics for timely fraud detection. Analytics helps identify anomalies in call records, financial transactions, or customer behavior, promptly detecting fraudulent activities. For example, telecom providers can monitor network activity in real-time, identify suspicious events, such as multiple login attempts from different locations, and immediately alert security teams.

Predictive maintenance

Telecom companies must maintain network reliability and constantly monitor their equipment status to prevent downtime during service delivery. Analytics allows them to analyze network performance, equipment condition, and environmental factors. This approach enables telecom providers to identify anomalies and early warning signs that precede failures and helps determine why the failures occur. 

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. This reduces operational disruptions, minimizes equipment replacement costs, and optimizes resource allocation.

Cost 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 and identify opportunities for cost reduction. For example, analytics can monitor energy consumption across the network infrastructure. Telecom operators can optimize energy-intensive areas, resulting in substantial energy cost savings. 

Also, big data analytics is used for price optimization. Dynamic pricing algorithms leverage the power of analytics to set optimal prices for products and services. Telecom companies can maximize ROI and profitability by aligning these prices with factors like customer lifetime value, tariff plans, and distribution channels.

In light of these insights, it may be possible to determine the interdependencies between pricing, promotion, and future revenues. By optimizing pricing strategy based on profit and revenue generated, telecom providers will be able to increase sales, and, most importantly, retain customers.

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 also consider call records, browsing history, and social media activity to gain more sophisticated insights. And with deep learning, companies can fully understand customer needs.

Depending on the segment, companies can create detailed profiles of their customers and offer them targeted promotions, customized service plans, or specific content. This level of segmentation allows telecom providers to offer a more personalized experience, which can lead to increased customer satisfaction and loyalty.

Targeted marketing

Among other telecom network solutions is the improved effectiveness of marketing campaigns. Telecom providers harness big data’s power to monitor real-time marketing efforts. By segmenting customers based on their purchase history, service preferences, and feedback, analytics can develop a comprehensive strategy to improve marketing campaigns accordingly. When a specific campaign underperforms, they can promptly make adjustments to optimize it, resulting in improved conversion rates and reduced costs.

Customer lifetime value (CLV) prediction

In today’s competitive market, customers are constantly seeking the best value, making it easy for them to switch 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. In these models, a variety of factors are considered, such as customer longevity, service usage, average revenue per user (ARPU), and churn likelihood.

These predictions enable telecom providers to create precise strategies to improve customer retention, boost revenue, and gain an edge over competitors.

Trends in data analytics in the telecom industry

Like many other industries, telecom is experiencing a dynamic shift driven by integrating big data and emerging technologies. Let’s explore some of the main trends for the telecom sector.

• Edge computing: edge computing in telecom 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): a cloud computing service that allows telecom operators to access and use information that is stored in the cloud without the need for local data storage or management;
• Advanced AI and ML: big data will call for the use of sophisticated machine learning and artificial intelligence algorithms;
• Hybrid clouds: hybrid clouds combine private and public cloud infrastructure to offer greater flexibility and scalability for app deployment without vendor lock-in;
• 5G network: the rollout of 5G technology will generate even more data, and telecom companies will use analytics to optimize network performance and ensure superior services.

Real-life examples of using telecom network analytics

There are many telecommunications companies that have already used analytics in their operations and have benefited from the results. Below are some of them.

Vodafone

Vodafone is a multinational telecom company in 21 operating countries that provides connectivity for individual customers and businesses. It offers an array of advanced technologies and digital services, including smartphones, broadband, IoT services for businesses, etc. Vodafone has launched its Vodafone Analytics platform in 2016. It was designed to collect, process, and analyze large volumes of information generated by Vodafone’s operations and customer interactions. 

By using visualization tools, such as Citilogic and Carto, this platform offers its business users an easy way to access, understand, and act upon the information generated by millions of subscribers, particularly regarding location. These tools help optimize business operations, improve accuracy, and enhance return on investment (ROI) without requiring extensive in-house research.

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 harness the large sets of information. The critical areas of focus for AT&T include:

Edge computing solutions for IoT devices. The company 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. Using it, networks become more flexible, efficient, and responsive to changing demands, while companies can address network-related issues proactively.

Deloitte

Deloitte is a globally recognized consulting firm that offers various services such as audit, risk and finance advisory, consulting, etc. Deloitte created a cutting-edge solution using the SAP HANA platform with the telecommunications giant SAP. SAP HANA is an in-memory computing platform and database management system. The primary features of this platform include high-speed data processing, advanced analytics, and the ability to handle both transactional and analytical workloads on a single platform.

This makes it suitable for various applications, from business intelligence and data warehousing to real-time processing and predictive analytics.

British Telecom (BT) Group

BT Group is a UK telecommunications giant operating in approximately 180 countries. It offers fixed-line, broadband, mobile, subscription TV, and IT services. The company uses data analytics to enhance its operations, including predicting service issues and optimizing call center operations. Their focus on Internet of Things (IoT) services led them to create a real-time data processing system. This platform was developed with the following models: 

• SIM card management: it enables BT Group to register and assign unique numbers to SIM cards, making customer distribution easier;
• Rate plans: through a custom rating engine, this module matches customers with the right rate plan for them;
• Billing: provides automated invoicing, real-time data collection, and precise billing calculations;
• Account management: making it easy for clients to register, view invoices, and invite other companies.

By offering this versatile platform to IoT businesses, BT Group is able to make informed decisions, optimize customer offerings, and generate revenue.

Conclusion

The question “What is the future of big data analytics in telecom?” often comes up and here is what we can say. It is unlikely that telecom providers will move away from data science; instead, they will continue to invest in it. The shift to a data-driven world is inevitable, as the world generates approximately 500 exabytes of data daily. 

For this reason, telecom analytics is an indispensable tool, and its widespread is only a matter of time. The use cases of 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 focuses on the deep integration of big data, which will lead to a more efficient and customer-centric industry.