How is data analytics transforming the telecom industry?

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My first professional experience as a fresh graduate was in an IT sector and my client was a telecom giant of UK. This was 15 years back and I, being an electronics and communication engineer, was very much fascinated by the kind of work I was doing. A part of this work was to monitor and report network traffic, find anomalies, monitor router performance, etc. Now as I am learning data analytics, I understand there is far more scope in utilising the telecom data to improve network efficiency, customer satisfaction, understand market trends and predict churn rates. Therefore, in this blog, I am trying to explain what each kind of data in the telecom industry is and how data analytics can be used to benefit the telecom sector.

The telecom industry plays a great role in the evolution of communication and with billions of connected devices; it generates a huge amount of data, which are analysed to answer crucial questions on network performance, customer experience, security, churn rates, cost reduction strategies, etc. Firstly, I will be discussing about the different kinds of telecom data, which on analysis can provide useful insights.

Categories of Telecom Data    

The various data available in telecom can be broadly classified into network data, customer data and market data.

Network Data

As a data analyst, studying network data using real time analytics would give insight into network traffic, accessibility, quality, anomalies, security. This helps in resolving network issues, effective distribution of network resources, improving quality of service in areas of concern and preventing fraud thus ensuring customer satisfaction.

  Customer Data

 An analysis of the customer data includes the study of user behaviour, location, churn and retention patterns, feedback, purchase history, etc. These insights help in understanding the target segment and enables telecom providers to offer personalised services and products thus improving consumer retention and profit.

Market Data

Telecom sector is a highly competitive area, so market analysis is imperative to remain relevant. It provides insights into competitor pricing, sales and market trends, which in lead helps companies benchmark their performance and plan effective marketing strategies.

Now let’s see how this huge amount raw data can be transformed to meaningful insights using data analytics.

 “Data is the new oil”- Clive Humby. This quote rightly captures the need for converting the raw, unrefined data into valuable information through processing, just like how oil needs processing before use in various daily necessities.

Data Analytics in Telecom 

Different types of data analysis are used in telecom to track network performance, customer satisfaction, fraud detection, growth of revenue etc.

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·       Descriptive analytics

 Descriptive analysis focuses on historical data to help understand network volume, peak hours, seasonal trends, call quality, latency, user behaviour, user location, preferred services, billing errors, churn rate, etc using key KPI’s.

For example, metrics such as ‘call set up success rate’ (CSSR), which is a fraction of total number of call attempts to the number of successful incoming and outgoing calls, and ‘call drop rate’(CDR) enable tracking of network performance, which in turn helps in decisions regarding network resource allocation for better customer satisfaction.

·       Diagnostic analytics

Diagnostic analysis investigates the various reasons for anomalies such as network outage, call drop, customer churn etc.

 An example is of this is monitoring the metric customer churn rate, which is the percentage of customers who leave during a certain period. This coupled with related metrics like customer satisfaction score (CSAT), which is the fraction of total positive scores by number of survey respondents, and customer effort score (CES), that measures how much effort a customer thinks it took to make a purchase or use a product/service. This helps to understand relationships and trends that explain the anomalies thus helping plan retention strategies.

·       Predictive analytics

Through deploying machine learning algorithm and statistical methods, predictive analysis can predict future service demand, customers likely to churn, potential network issue, etc.

For example, telecom companies can use analytics to understand which type of services have brought in the highest and lowest revenue using metrics like average revenue per user (ARPU) and Return on Investment (ROI). They can also predict when and where network congestion will occur using metrics like round-trip time (RTT) paired with server response time (SRT). Using these metrics company can identify the most cost-effective plan for infrastructure investment while proactively addressing potential network issues. Furthermore, by using statistical and machine learning technologies, we can find patterns of fraud in the network and can set an alert in real-time. 

·       Prescriptive analytics

Prescriptive analysis helps in deciding what actions should to be taken to improve efficiency at reduced operational costs.

Examples of this are decisions to offer personalized products and services to customers by studying metrics like CDR (caller data record), NPS (Net Promoter Score), and CSAT (Customer Satisfaction Score)

Though there are a lot of advantages of implementing data analytics in telecom, it has its own fair number of challenges.

 Challenges in Implementing Telecom Analytics

The various challenges that will need to be addressed while implementing data analytics in telecom are:

(a)   DATA QUALITY AND INTEGRATION: Inaccurate data can lead to errors and will impact the business decisions and so ensuring data accuracy, completeness and integration through advanced ETL engines are essential

(b)   DATA PRIVACY AND SECURITY: Telecom industry deals with sensitive customer information which needs to be protected. Understanding the legal and ethical frameworks are crucial for maintaining trust and protecting customer data.

(c)   DIRTH OF ANALYTICAL SKILLS: Since data analytics is a newly advancing technology, companies will need to invest on training programmes to upskill their employees’ handling data in data science, statistics and machine learning or work with external experts.

(d)   SCALING CHALLENGES: With rapidly increasing data, the data analysis process should support scalability to accommodate the growing volume of data and so it is advisable to select tools accordingly.

(e)   DATA ACCESS: The analyst needs to have proper access to the needed data source. Many businesses restrict access due to security reasons. Following security measures like data masking can enhance safer and unrestricted access.

(f)    BUDGET LIMITATIONS: For effective and safe use of data, the company will need to invest a lot in upskilling employees, buying various development platforms that are scalable and employing various risk-management steps to protect sensitive information.

It is abundantly clear that Data Analytics is proving to be a winning differentiator for the Telecom industry and enterprises being deliberate about investment behind this pursuit is seeing lot of success in uplifting their service quality, elevating consumer trust while delivering accelerated sustainable growth. What also stands out for these companies is the systematic approach they have in overcoming the various challenges.

Some examples of telecom companies who have already adopted data analytics are AT&T(AT&T Analytics), British Telecom( BT analytics), Vodafone(Vodafone analytics), Deutsche Telekom(Deutsche analytics and AI). According to industry insights(source:leadsinfra.com), industries using big data analytics have seen a 10% increase in customer satisfaction and 10% decrease in churn rate. Additionally, 72% consumers indicate they only engage with personalised marketing messages and 70% appreciate proactive customer service, such as alerts or bill reminders.

Data Analytics is here to stay, and it is redefining the telecom industry and it will be an ongoing evolution. A space to watch out for sure!