Maximizing the Value of Telecom Data Analytics: Opportunities and Challenges

Telecom companies have to work with vast amounts of data from different sources, both internal and external. However, only a few are capitalizing on telecom data analysis to address complex issues and gain a competitive edge.

Stats prove the effectiveness of big data in telecom. Research by Analytics Insight reveals skyrocketing growth in big data in the telecom industry, with a 33% share of the overall market. By using the insights from data analysis, telecom enterprises can optimize network performance, enhance customer experiences, and drive business growth through predictive maintenance.

With more than a decade of developing custom solutions and automating business processes in the telecom industry, we at Flyaps helped big companies like Orange Group streamline their business operations and leverage the power of data analysis. Based on our expertise and real-life examples, in this article we’re going to discuss the opportunities big data brings to telcos, how telecom giants are using the power of data, and what data challenges businesses can address when implementing data analysis into their operations. And let’s start with opportunities.

Opportunities telecom companies can achieve with big data

Becoming a data-driven telecom organization surely does not happen overnight. The first thing you should do when starting your journey is to ask yourself, what is data analytics in telecom?  In particular, why does your company need to implement big data decisions?

Telecom data analytics involves extracting valuable insights from network usage patterns to optimize operations and enhance customer experiences. It enables direct analysis of customer behavior and preferences to develop targeted retention strategies that effectively reduce customer churn. The image below highlights some other use cases of big data in the telecom industry.

So, let’s look at the main big data benefits in telecom:

Retaining customers through predictive churn analysis

Churn analysis helps telecoms identify clients who are likely to leave the service provider. It’s all about analyzing client behavior and usage patterns to prevent undesirable outcomes and developing strategies to retain customers.

Imagine a telecom company that provides mobile phone services to its customers. They have noticed a significant increase in customer churn rate, where more and more customers are switching to competitors. The company wants to understand the root cause of this churn and take measures to retain its customers.

To conduct churn analysis, the telecom company collects various data points from their internal systems. This includes customer demographics, call records, data usage, billing information, customer service interactions, and more. They also gather external data, such as competitor pricing, network coverage information, and customer sentiment analysis from social media platforms.

Using the gathered data, the telecom company can apply analytics models to identify patterns and factors contributing to customer churn. For instance, they may discover that customers who frequently experience dropped calls or network coverage issues are more likely to churn. Additionally, they might find that customers who experienced poor customer service or faced billing discrepancies decided to switch to another provider.

With these insights, the telecom company can take actions to reduce churn. They might invest in improving their network infrastructure, enhancing customer service processes, offering personalized retention offers, or launching targeted marketing campaigns to address the identified pain points and retain valuable customers.

Using data to make smarter pricing decisions

Big data in telecom plays a significant role in price optimization by providing valuable insights and enabling data-driven decision-making.

Telecom companies can leverage big data to segment their customer base based on various attributes such as usage patterns, demographics, preferences, and behaviors. This segmentation helps in identifying different customer groups with distinct price sensitivities and needs.

What is more, big data allows telecom companies to conduct pricing experiments by analyzing vast amounts of historical customer data. By testing different price points and monitoring customer responses, companies can optimize their pricing strategies and identify the price elasticity of their services.

Last but not least, telecom companies can leverage big data to gather comprehensive information about their competitors' pricing strategies. Analyzing competitor pricing helps in understanding market dynamics, identifying pricing gaps, and making informed decisions on price adjustments to stay competitive.

Fraud detection

Telecom data analytics plays a crucial role in preventing fraud by providing Telecom data analytics plays a crucial role in preventing fraud by providing valuable insights and enabling proactive measures.

By analyzing large volumes of telecom data, including call records, network logs, and customer usage patterns, you can identify unusual or suspicious activities. Anomaly detection algorithms can flag potentially fraudulent behavior, such as abnormal data usage, SIM card swapping, unusual call destinations, or irregular network activity.

Let's delve into real-life examples to see how telecom data analytics is transforming the industry's well-known players.

How telecom giants are applying data

Below you can find some real-life examples of companies that have already made use of big data in their processes.

Vodafone: using big data for client's behavior analysis

Vodafone applies data analytics to understand customer preferences better and deliver more personalized customer service. This all is possible due to a special platform called Vodafone Analytics. Moreover, the service is available for other businesses.

For example, when Vodafone is planning to launch a new data plan targeting people under 18 years old in a specific area, they can forecast the number of subscribers they can expect in the coming months. This allows them to understand the demand for their services.

To analyze this information manually, the experts from Vodafone need days or weeks. But data analytics simplifies the process. Using predictive models and customer segmentation insights, Vodafone can perform demand forecasting for the new data plan. They can consider factors such as market size, competition, pricing, and customer preferences to estimate the number of subscribers they may expect over a specified period.

Interesting fact: Vodafone has also developed an in-house big data analytics platform to optimize the energy consumption of its 11,500 radio base stations. This platform incorporates artificial intelligence and machine learning capabilities to assist energy specialists in detecting and addressing "consumption anomalies" where network components consume more energy than anticipated. By leveraging targeted actions based on the data analysis, the company can optimize energy efficiency at these sites.

NeuString Analytics: improving roaming analytics with a cloud-based BI system

Outdated telecom desktop applications don't fit for today's digital landscape and require a lot of modernization. NeuString Analytics, a provider of telecom data analysis solutions, came to Flyaps with a similar problem. They needed a more advanced and user-friendly solution to meet their clients' evolving needs, and attract new big clients like Orange Group.  So, they asked us for help.

We had two main goals:

• Move the system with existing features to the cloud, so that NeuString clients could still use familiar services, but on a modern platform;
• Add new tools for calculating discounts and predicting data for users to create and compare budgets more effectively.

Despite a tight schedule, we’ve managed to achieve it and launch the first version in just four months.

We ensured that the new cloud-based solution seamlessly integrated with the existing system, allowing for efficient data reporting and presenting it on dynamic dashboards. These dashboards (including an interactive world map) provide insights categorized by country, period, service types, and showcase volume, charges, and other valuable information. We paid much attention to designing a user-friendly interface, prioritizing ease of use without compromising visual appeal.

One of the vital improvements for more accurate analysis capabilities was to develop several modules with different dashboards that would display key metrics crucial for telcos, including traffic volume, inter-operator tariff rates, statistics by countries, discount agreements, and so on. For example, there is a budget module, where we added the possibility to create several budgets with different parameters and compare them to identify the most profitable option.

As a result, NeuString Analytics now has a powerful and unique system that doesn’t have any alternatives. Even big companies like Orange Group and Hutchison 3G UK Ltd find it essential for forecasting, budgeting, and analyzing data. The partnership with Flyaps has successfully transformed NeuStrings' telecom data analytics capabilities, enabling them to better serve their clients and remain competitive in the market.

Now that we’ve talked about companies and the opportunities data analytics provides to them, let’s move to challenges.

Main data challenges faced by telecom companies

The most common challenges in telecom big data analytics include:

Data quality and complexity

Telecom data often suffer from quality issues, such as missing values, inconsistencies, and inaccuracies. Data quality problems can arise due to factors like network errors, data integration challenges, or human error during data collection.

For example, missing values in call records or incorrect customer details can hinder the accuracy and reliability of the analysis. Incomplete or inaccurate data can lead to flawed insights and incorrect decision-making.

What’s more, Telecom companies collect data from various sources, such as call detail records (CDRs), network logs, customer interactions, billing systems, and IoT devices. Each data source may have different data formats, structures, and quality standards. Integrating and consolidating data from diverse sources while maintaining data quality is a complex task prone to errors.

Privacy and Security

Telecom companies handle sensitive customer information, including personal details, call records, and location data. Analyzing this data while ensuring privacy and security poses a significant challenge. Compliance with regulations like the General Data Protection Regulation (GDPR) adds complexity to data analytics processes.

These regulations impose strict requirements on the collection, storage, processing, and sharing of personal data. Ensuring compliance with these regulations while performing data analysis can be complex and requires careful handling of data.

Correlations among variables

Identifying correlations among variables and understanding the relationship between variables and predicted metrics (labels) is a crucial task in data analytics projects.

For example, variables such as age, gender, income level, education level, and occupation can be used to analyze their impact on customer behavior, preferences, and purchasing patterns.

With the vast availability of diverse information sources today, the number of variables can reach hundreds or even thousands, making this task more challenging. Still, uncovering these correlations is essential for making informed business decisions.

Data integration and reconciliation

Data integration and reconciliation refers to resolving conflicts that arise from multiple data sources at the schematic, modeling, and semantic levels. In simple terms, data integration and reconciliation is about dealing with conflicts that occur when you have data coming from different sources. These conflicts can happen at different levels.

First, there's the schematic level, which means that data may be organized differently in each source. For example, one source may use different names or formats for the same type of data compared to another source. Data integration and reconciliation involve finding a way to bring all the data together and make it consistent across different sources, even if they have different structures.

Next, there's the modeling level. This refers to the way data is represented and the relationships between different data elements. When integrating data from multiple sources, it's important to ensure that the models used to represent the data are compatible and can work together. This may involve mapping or transforming data so that it fits into a common model that can be used for analysis.

Lastly, there's the semantic level. This involves resolving conflicts in the meaning or interpretation of data. Different sources may use different terminology or definitions for the same data. Data integration and reconciliation involve aligning the semantics of the data so that everyone understands and agrees on what the data represents.

Telecom data analytics can be used to map and transform data from different sources into a common format or structure. Advanced analytics techniques can identify patterns, relationships, and similarities in data, enabling efficient mapping and conversion. This helps ensure that data from various sources can be integrated seamlessly.

Telecom big data analytics solutions from Flyaps

At Flyaps, we work with enterprises in the telecommunications industry. We have a deep understanding of how telecom networks operate and helped unlock numerous opportunities for our clients by leveraging the vast amount of data they generate and turning it into valuable insights.

Here's what we can offer to help you maximize your growth potential using data analytics:

• Predictive analytics to examine current data and historical events to predict possible future scenarios;
• Hybrid clouds to provide the needed flexibility and data deployment options by moving processes between private and public clouds;
• Data integration and consolidation. We can organize all structured and unstructured data in one place, making it ready for further processing and analysis;
• AI and machine learning models. We build intelligent solutions that can offer valuable insights and projections to make positive changes in your business;
• Telecom business intelligence systems or self-service analytics that make drawing actionable insights much easier;
• Data visualization, which includes creating interactive and customizable dashboards and reports to make data more understandable.

Check out our telecom case studies to learn more.

Final thoughts

To sum up, there are many ways data analytics can help telcos. Analyzing and collecting all important information takes a lot of time and resources, but data analytics solutions can become a secret weapon to help you reduce your time on manual tasks, identify problem areas, improve customer experience, and lift revenue. And this is just the tip of the iceberg of what data analytics can do.

With a profound understanding of the telecom industry, our team excels in applying technologies to extract valuable insights from vast amounts of telecom data. We, as a custom software development company, can help you transform complex data into practical insights, ensuring well-informed decision-making and driving your business growth. Reach out to us, and we’ll help you to reach a new level in your telecom journey!