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How To Manage Data Analytics in the Telecom Industry

Picture of Alen Muslić

Alen Muslić

Chief Innovation Officer

Discover how CSPs can enhance their data analytics capabilities to keep pace with the complexity of telecoms.

In the data-heavy telecommunications industry, analytics present a huge opportunity for improvement. So much so that the telecom analytics market is expected to reach $23.66 billion by 2032, and the number of CSPs investing in analytics use cases has increased steadily since 2019, according to a 2023 report by Analysys Mason.

But getting data analytics right is about more than just making investments. To harness the power of data analytics and unlock valuable insights, CSPs need well-defined use cases, an integrated approach to data, and cutting-edge technology. In this blog, Alen Muslic, Chief Innovation Officer at ZIRA looks at the diverse use cases for data analytics and how it can help CSPs drive profitability and customer retention.

Understanding the landscape of big data analytics in telecom

In recent years, CSPs have been laser-focused on optimizing performance and cutting costs. As they face an increasingly competitive landscape, keep up with technological breakthroughs, and adhere to ever-changing regulations, it can be difficult to locate opportunities for improvement across their business.

In this context, data analytics has proven a powerful tool, allowing CSPs to turn data into insight. With the considered approach to data analytics, telecom companies can find and address inefficiencies, such as analyzing customer data to understand more about customer behavior and find actionable insights for improving their customer service.

Data analytics is also an increasingly important factor as CSPs face growing competition from technology companies and hyperscalers. As a recent report by TM Forum explains, one of the biggest differentiators that tech companies have over telcos is their ability to “leverage the potential of advanced analytics and machine learning” and increase efficiency using data-enabled insights. As the telecommunications industry becomes more complex, CSPs will have to get more sophisticated data analytics capabilities to keep pace with technology companies.

Traditional use cases for data analytics in telecom

CSPs are already using data analytics in some areas of their business, such as:

Customer and partner behavior analysis

The provision of reliably positive customer and partner experiences is key for CSPs. For this, the first step is having a detailed understanding of what products and services partners and end-users need at what times. This then allows CSPs to tailor their solutions specifically to their partners and greatly increase customer satisfaction.

Data analytics can help CSPs analyze customer and partner behavior and locate opportunities for up- and cross-selling or providing customized services. For example, BSS products enhanced with data analytics, like ZIRA’s Revenue Management, can identify personalized promotions and corridor discounts for partners and customers, which invariably leads to higher levels of customer satisfaction.

ZIRA Revenue Management

Network optimization

Network performance is of course a vital metric for CSPs, as it correlates directly with customer experience. Network users expect uninterrupted service at all times, so minimizing drops in service quality and issues with network congestion remains an important differentiator.

Using data analytics, CSPs can measure critical metrics that link to overall performance, such as network latency or data packet loss. Predictive analytics can even help network optimization teams anticipate future network problems and resolve them before a larger issue presents itself.

Fraud detection

Fraud continues to be a critical issue for the industry, with the CFCA estimating that almost $39 billion of telecoms revenues were lost due to fraud in 2023.

With advanced data analytics capabilities, CSPs can process their large volumes of network usage data, such as Call Detail Records (CDRs) and billing usage information, and identify any anomalies that may be caused by fraudsters. As a result, they can stop fraud-related revenue leakage much faster and more easily than without the help of data analytics.

The future of data analytics

These use cases, while helpful, don’t show the full spectrum of possibilities that data analytics represents in the complex domain of telecommunications today. To get the most value out of their data, CSPs will need more sophisticated use cases that recruit cutting-edge technology to analyze and forecast data.

One key example of this is using AI and ML to help trading and routing processes. These rely heavily on time-series data, from supplier prices to traffic patterns. Understanding changes in the data can greatly enhance a CSP’s ability to forecast fluctuations and prepare ahead of time. For example, using the machine learning capabilities of ZIRA’s Telco AI Platform, CSPs can predict supplier prices up to six months in advance and benefit from improved margins.

Having increased visibility over supplier prices also leads to profitable new deal opportunities, which can help CSPs form new partnerships in their ecosystem.

ZIRA's Telco AI Platform

Overcoming challenges in data analytics management

Whether it is more traditional or future-looking, CSPs have to approach data analytics with the right mindset. Here are some considerations to bear in mind.

Data quality and consistency

You’ve heard it before, but your data analytics capabilities are only as good as your data. It is critical to have a constantly updated and reliable data source that your analytical tools can feed into.

To that end, CSPs should ditch uploading data manually and use automation whenever possible. For example, with ZIRA’s Trading and Routing Management, it’s possible to automatically upload and verify supplier prices from emails, ensuring accuracy throughout the process.

How trading and routing management can benefit from AI-enhanced data analytics

Scalability and flexibility

As your business scales, your data grows with it. To ensure your analytical processes remain reliable, you need flexible and scalable tools that can expand along with your business. Our lead-to-cash BSS suite supports business growth with an agile Revenue Management system that can keep your billing data safe as you onboard more partners and customers without losing any existing functionality.

Security and privacy concerns

Data analytics and data security should be synonymous in every telecommunications business. CSPs have vast amounts of sensitive data at their disposal, from customer information and billing data to service agreements and partner records. To guarantee regulatory compliance and prevent the risk of a data breach, CSPs should have robust data security measures in place. A few important elements to consider:

  • Implementing a multi-layered security strategy incorporating firewalls, intrusion detection systems, data encryption, and regular security audits to detect and respond to security breaches in good time.
  • Educating employees on the latest security threats and ensuring they understand and adhere to the company’s security protocols.
  • Managing third-party risks by establishing protocols to ensure that external parties with access maintain data confidentiality.
  • Establishing robust procedures for backup and recovery to minimize the fallout from security incidents.

Conclusion

The use of data analytics in telecommunications can bring significant benefits to CSPs, from increasing profitability to improving customer satisfaction. To get the most benefit out of it, CSPs should invest in modern BSS tools that use the power of AI and ML and can analyze complex time series data with much higher accuracy. That way, CSPs can make well-informed decisions and prepare for the unexpected for guaranteed business success.

Ready to discover what data analytics-enabled BSS can do for your business?

Read more ZIRA blogs

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