Understanding Telecom Analytics: What It Is and Why It Matters

With data being all around us in different formats and sizes, it has become much easier for organizations to gather insights and make timely decisions. This is accomplished through telecom analytics in the telecommunications industry.

The telecommunications network plays a vital role in keeping the world moving, allowing businesses to meet the quick demands of an evolving society in this digital landscape. The telecom analytics market was valued at $6.6 billion in 2024 and is expected to reach $19.0 billion by 2033, indicating a steady rise in the usage of telecom analytics for decision-making (Source: IMARC).

As the reliance on telecommunications grows, organizations increasingly focus on collecting data and meeting complex demands. 
This article explores telecom analytics, covering its definition, types of telecom analysis, associated challenges and benefits, key use cases, and best practices.

What is Telecom Analytics?

Telecom analytics refers to the process of evaluating extensive datasets and obtaining valuable insights. It plays a key role in improving customer experience, increasing sales, decreasing churn and fraud, and reducing operational costs. Telecom network analytics leverages advanced analytics to understand patterns.

Telecom analytics involves using Business Intelligence (BI) tools to address telecom organizations' specific needs and demands. Extending beyond the functionalities of BI solutions such as reporting and dashboarding, analytics solutions offer a suite of capabilities such as ad hoc querying, descriptive modeling, text analytics, data mining, and optimization and forecasting.

Analytics offers significant benefits in the telecommunications sector. From operations and internal processes to trend analysis, telecom companies can leverage these advanced techniques to reduce costs, improve customer experience and satisfaction, and make informed decisions to drive growth, innovation, and revenue.

Types of Telecom Analytics

Telecom analytics can be tailored to improve specific aspects of telecom operations and performance. Several types of telecom analytics focus on different outcomes, depending on the primary objective.

Different types of telecom analytics include:

• Advanced Analytics: Advanced analytics for telecom helps businesses facilitate: 

• Social network analysis allows operators to understand the relationships with their customer base and how they can improve their interactions with them.

• Geomarketing analysis that enables operators to use geographical information for marketing and sales activities.

• Deep packet analysis allows operators to understand network traffic better and troubleshoot network performance. 

• Descriptive Analytics: In descriptive analytics, historical data is collected and summarized, providing insights into trends, patterns, and even relationships in past events. 

Descriptive analytics lets stakeholders easily understand the data and identify the business’ strengths, vulnerabilities, and threats through reports, dashboards, and graphs. 

• Diagnostic Analytics: Diagnostic analytics helps you discover the root causes of a particular outcome, whether by identifying patterns or casual relationships in specific data or correlations. 

Diagnostic analytics is more potent than descriptive analytics, as it helps you understand the reason behind an event. For instance, diagnostic analytics can be used to analyze the quality of service, customer complaints, and performance degradation. 

• Predictive Analytics: Based on historical data, such as patterns and trends, predictive analytics uses machine learning algorithms and statistical models to predict future trends and events. It is yet another significant tool that facilitates decision-making among stakeholders. 

• Prescriptive Analytics: Prescriptive analytics combines descriptive, diagnostic, and predictive data to recommend specific actions. For example, it can advise telecom companies on optimizing their networks to improve operational efficiency. 

Employing the different types of telecom analytics based on your company’s goals can help you obtain valuable insights for better decision-making and overall operational efficiency.

Benefits of Telecom Analytics

Integrating telecom analytics into your system can help you obtain insights, streamline operations, and improve your business's growth through personalized strategies.

The key advantages of telecommunications analytics include:

• Enhancement of Customer Experience: Data analytics can be used to provide creative and easy-to-understand workflows, such as infographics and pie charts. This simplifies data comprehension for stakeholders, helping them focus on what truly matters and make informed decisions. 

• Effective Insights Gathering: Big data analytics can help identify patterns and trends in the telecommunications landscape. It enables businesses to discover findings that may not have been possible through simple analysis and helps them develop effective strategies. 

• Optimization of Operational Efficiency: Data analytics can help you obtain a bird' s-eye view of your enterprise. It enables you to leverage the extensive structured and unstructured data you have collected to develop efficient solutions and make informed decisions based on your insights. 

• Improvement in Network Usage: The telecom industry uses data analytics to automate operations and improve network coverage. Additionally, data analytics helps manage workloads for established policies and ensures system security.

Listed below are real-world companies that have used telecom analytics for a successful strategy:

• AT&T: The US-based multinational telecommunications company implements analytics to analyze customer behavior and improve services accordingly (Source: CustomerThink).

• Vodafone: Vodafone uses analytics to improve its network and energy usage with real-time data. Additionally, leveraging insights from real-time analytics, Vodafone offers personalized offers. This helps the company reduce customer churn and retain customers (Source: IjonaServices).

Telecom analytics can help you improve data monetization and automate operations. By creating a strong and data-driven strategy derived from analytics, telecom companies can optimize their network infrastructure and build a stronger and loyal customer base.

Challenges of Telecom Analytics

Telecom companies are embracing big data analytics to process the vast amounts of information they have stored. While telecom analytics offers significant benefits, it also has its own set of challenges.

• Varied Data Sources: Telecom companies gather data types, such as call detail records, customer history, and network logs. Each type of information is available in a different format and structure. Analyzing the various data types becomes challenging as it requires a particular processing technique. 

• Disparate Data Sources: Telecom companies work across different regions and countries. Therefore, some of the data is not centralized, resulting in the creation of data silos due to lack of integration or outdated systems. Several companies store all their sensitive information in isolated databases. 

• Inconsistent Data: Most of the data is available in unstructured format, and data quality needs to be improved. These errors may occur due to network issues or problems within the database. Inaccurate or inconsistent information may impact a company’s decision-making process. 

• Data Preparation: Before data analysts begin their analysis, the data undergoes preprocessing, which includes handling missing values, scaling features, cleaning, and transforming the data. 

However, when dealing with an extensive dataset and a limited number of people to manage it, much of the data needs to be more consistent and relevant. 

• Expensive Process: The more data you have, the better equipped to make informed decisions that drive improvement and growth. However, telecommunication companies may lack the financial resources to maintain and integrate a robust infrastructure or implement sophisticated tools and technologies.

• Inadequate Customer Service: According to a report, 96 percent of customers switch telecom operators due to insufficient customer support (Source: TrueList). Companies can lose loyal customers, adversely affecting their reputation and credibility. 

Telecom analytics can address the issue of inadequate customer service by identifying recurring problems and assessing customer needs and preferences. This allows companies to develop effective strategies for long-term success. 

Addressing the above mentioned challenges can help companies leverage the full benefits of telecom analytics. Companies can then focus on enhancing customer experience, reducing churn rate, and optimizing network performance.

Top Use Cases of Telecom Analytics

As the world continues to evolve digitally, telecom providers are shifting their focus to big data to extract valuable insights, elevate their services, boost revenue, and maintain a competitive edge. 

Here are some use cases where companies can leverage the benefits of telecom analytics: 

• Improvement in user experience: It is critical to deeply understand how each customer behaves to bag new customers and keep the existing ones. Telecom companies use data analytics to examine customer information from past calls and identify patterns. As a result, they can create customer profiles to offer personalized services or promotions to new and existing users.

• Customer Churn Prediction: Predictive analytics enables telecom companies to monitor customer attrition regularly and make adjustments where necessary. Big data analytics can reduce customer attrition by 15 percent (Source: McKinsey).  

It also allows companies to make important decisions such as detecting whether or not a customer is considering switching to another telecom provider. 

• Targeted Marketing: Telecom marketing campaigns can leverage telecom analytics to monitor their marketing efforts. Companies can strategize and improve their marketing campaigns by gathering customer data such as purchase history, feedback, and preferences. 

• Price Optimization: Telecom operators must price their products and services judiciously, considering the intense competition in the market. 

Companies can use historical data such as past purchases, competitor pricing, and customer responses to discover insights on consumer behavior and subsequently develop optimized pricing plans. 

These use cases highlight the impact of telecom analytics. Telecom companies that can easily adapt to new methodologies can obtain a competitive edge and thrive in the long run.

Best Practices for Telecom Analytics

Following the best practices of telecom analytics is essential to ensuring quality data that supports effective evaluation and decision-making. 

Some of the telecom analytics best practices that you should follow are: 

• Define Your Objectives: You must establish a clearly defined goal. It could range from predicting a certain market, optimizing your network performance, or enhancing customer experience. 

• Evaluate Data Landscape: Before you begin analyzing, you must understand what kind of data sources you have. Are they all structured, unstructured, or a combination of both? Are they all call records or customer feedback? Understanding the type of data you have to work with can help you streamline analysis. 

• Choose the Right Platform: Choosing an analytics platform that aligns with your goals is important, as all platforms vary in their features and capabilities. 

Seek platforms that offer real-time processing, predictive analytics, scalability, and easy integration with diverse systems to ensure smooth compatibility and optimal performance within your current setup.

• Leverage Machine Learning: Telecom analytics provides excellent results combined with machine learning. Ensure your platform adapts to newer technologies, such as machine learning, to continuously learn from emerging patterns and trends. 

• Establish Robust Security Measures: Dealing with sensitive information adds extra risk to your company’s credibility. Therefore, it is critical to establish robust cybersecurity measures. This will help you ensure data compliance and safeguard data from threats. 

• Monitor, Improve, and Expand: After implementing the best practices, it is important to allocate time and resources to hiring experts who can continuously monitor and enhance your platform's performance. These experts can also help improve and implement initiatives for a seamless expansion of your infrastructure. 

How are Tredence's Telecom Analytics Solutions transforming the TMT industry?

The Telecommunications, Media, and Technology (TMT) industry is evolving rapidly, fueled by innovation, operational efficiency, and customer-centric strategies. The TMT industry acts as the pillar for companies across different sectors. 

Whether you are a startup entering the TMT industry or an established company with limited resources and expertise, scaling to the top will be a significant challenge. Transforming your business can be daunting due to the intense competition in the market. 

While the TMT industry has the potential to transform companies and help them stand out, it also presents unique challenges, such as disparate data sources, market complexities, and lack of trust, that must be addressed promptly. 

This is where Tredence acts as a game-changer. Leveraging AI and ML capabilities, Tredence’s telecom analytics solutions encourage businesses to bridge the gap between insightful data and effective strategies. 

Here is how Tredence can help you support your TMT Infrastructure: 

• Enhance Operational Efficiency: Tredence helps businesses optimize their operations across different sectors, such as supply chain, customer management, and retail planning, through its advanced AI solutions. Using predictive forecasting models, companies can make informed decisions, thus improving their overall business efficiency. 

• Monetization Across Franchises: Tredence’s advanced data engineering and business intelligence practices provide a comprehensive view of customer activity, offering an effective approach to uncover monetization opportunities across franchises. 

• Transform Business Models: Companies need to transition to modern business models for increased efficiency and scalability. Businesses with a modern business model can harness new growth opportunities and better adapt to market conditions. 

Tredence’s personalized solutions and advanced tools and technologies, such as AI and ML, can help you stay competitive.

Conclusion

Data analytics in the telecommunication field has grown significantly. Telecom analytics is more than just a powerful tool; it is a game-changer that enables companies to gather important data about their customers, networks, and the latest market trends. 

With a wide range of analytics platforms available, companies can now confidently focus on expanding their customer base and driving growth while relying on data-driven insights to guide their decision-making. 

Tredence helps companies build customized solutions, enabling the last-mile adoption of AI and ML. Its services enable businesses to manage technology disruptions by combining the potency of AI-led telecom data analytics to deliver sustained growth and trust. 

With its telecom analytics solution and 1300 expert data scientists and engineers, Tredence helps you produce measurable results in customer experience, personalization, supply chain improvement, and machine learning implementations.

Contact Tredence today to transform your telecom business with its advanced AI and ML solutions. 

FAQs

• What is telecom analytics?

Telecom analytics is the process wherein telecom operators collect, analyze, sort, process, and understand extensive data. Using AI and ML's predictive features, companies can use the findings to draw conclusions and enhance their customer experience.

• How has analytics changed the telecom industry?

Telecom companies leverage their data to gain insights that drive informed decision-making. Analytics can help telecom companies optimize network performance, improve customer experience, and improve strategic efforts.

• What are the benefits of telecom analytics?

Telecom analytics can help companies improve customer satisfaction and promote operational efficiency. Additionally, telecom analytics can minimize issues such as customer churn, downtime, manual errors, and losses. 

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Editorial Team
Tredence