Al & Machine learningBig DataPredictive AnalyticsRobotics

Role of Data Science in the Telecom Industry

The current advancements in technology have brought the world closer as people are able to connect with each other despite the distance between them in just a few seconds. This rise in connectivity has resulted in an increase in data that is being generated through calls and texts. It is evident that Telecom Industries can no longer continue using the traditional techniques and methodologies for handling the large amount of data that is increasing with each passing second. Hence, they are approaching Big Data and Advanced Data Science Technologies for handling and managing this data.

The immense influx of data generated in this industry means that the demand for Data Science Professionals is rapidly increasing. According to research done by Analytics Insight, skyrocketing growth in big data is dominated by the telecommunications and Information Technology Industry, with a 33 percent share of the overall market. The organization predicts that spending on big data in telecom will grow from $59 billion in 2019 to over $105 billion in 2023. This means that by 2023 the demand for certified data science professionals in the Telecom industry will increase with over four million Data Science Jobs being advertised.

Whereas the rollout of the 5G Network, the Internet of Things, and increasing consumer pressure for personalized services have been the driving forces behind the demand for data science in the Telecom Industry, the COVID-19 pandemic has put it into hyperdrive. Many companies are advising their staff to work from home while schools have turned to virtual learning so as to reduce the spread of the virus. The need for reliable connectivity is needed more than ever as almost all industries rely on digital communications to work and study virtually.

With a reduced workforce, limited access to crucial departments like data centers and call centers, the telecom industry is increasingly adopting data science to ensure that critical communications in this new remote working world remain smooth during this period. In this article, we examine how telecom companies are using Data Science Technologies to respond faster to the changing environments and requirements.

Real-Time Analytics

Due to the speedy advance of the internet and the evolving of 3G, 4G, and even 5G connections, telecommunication companies face the challenge of the regularly changing customer requirements-we saw Safaricom launch the 5G Network in Kenya in 2021. The subscribers are becoming more and more demanding, and the traffic gets more active every day. To help cope up with this, the Telecom industry is using up-to-date analytical solutions for performing regular analysis of data collected from diverse resources.

Data science through Real-time streaming analytics can handle this task – the streaming analytic solutions are tailored to continuously ingest, analyze and correlate data gained from multiple sources and generate response action in real-time mode. Real-time analytics combines the data related to customer profiles, network, location, traffic, and usage to create a 360-degree user-centric view of the product or service. It also captures and analyzes the interaction and communication between the customers.

Product Optimization

In recent years product optimization has become an increasingly valuable tool for the development of any business because it cuts time, cost, and risk in the development process. Providing the best-suited products and services that meet the customers’ needs is a crucial factor in the telecom industry. The Industry uses Data Science to perform the real-time analysis of customer data to improve their products and services. Factors like customers’ usage and customer feedback are analyzed before a final decision is made for launching a new product or improving an existing product/service that will benefit the customers as well as the industry.

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Prediction and Prevention of Customer Churn

The Telecom industry offers several services like phones, TV, internet among others. Making the customers believe that you are worth their time and money is a challenging task while keeping them engaged for a longer time is even more challenging. It’s therefore important for the companies to apply proper and accurate analytics that will enable them to understand their customer behavior.

The Telecommunication sector performs predictive analytics on the customer transaction data collected by their devices for gaining valuable insights about their feelings. This helps the management in building satisfactory solutions to customer issues resulting in better services and customer churns prevention. A good example is Cox Communications, a leading player in the telecom industry, which had built predictive models that enabled them to quickly and precisely poll millions of customer observations and hundreds of variables to identify issues including the likelihood of churn. They then personalized offers across 28 regions. This move enabled them to act upon the insights and recommendations, resulting in a reduction in customer churn.

Trainer at Predictive Analytics Lab shares his screen during a virtual class on Credit score

Fraud Mitigation

Telecom networks and their customers are vulnerable to cybercrime, which has become worse during this Covid- 19 pandemic period. The detection of fraudulent activities has proven to be one of the biggest challenges for this industry. According to a recent survey, the value of fraud losses faced by the Telecom industry globally is around $40.1 billion which is around 1.88% of the total revenue. Data Science enables these companies to analyze real-time data and be able to identify the source of fraudulent transactions and correlate those with historical activity to prevent future counterfeit actions. Again, the most common fraudulent activities in the Telecom world are misuse of credit/debit card information, unauthorized access, fake profiles, among others. Thus, the industries are using unsupervised machine learning algorithms to detect unusual user activities and prevent fraud. For instance, Vodafone works with Argyle data to detect and prevent frauds with the help of fraud analytics.

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Increased Network Security

Network security is one of the most important aspects to consider when working over the internet, LAN, or another method, in the telecom industry.

Predictive Lab trainer shares her presentation with the trainees during a class on Cyber Security

While there is no network that is immune to attacks, a stable and efficient network security system is essential to protecting client data. A good network security system helps telecom industries to reduce the risk of falling victim to data theft and sabotage. Data Science helps in ensuring network security by viewing events in real-time, analyzing the previous data, and making predictions about any problem or complications that might appear in the near future -This analysis helps in taking suitable actions for any problem before its severe consequences. For instance, Brightlink communications which provide voice, messaging, analytics, and cloud solutions use Net Optics Director Pro (a network controller switch) for monitoring their calls. watch training on Network threats here

Price Optimization

The growth in technology has resulted in increased competition amongst Telecom industries as each company wants to have the largest number of subscribers. Pricing of products plays a very important role whenever it comes to increasing subscribers or users. For instance, in December 2020 Airtel Kenya announced that their users would get 100 minutes every day for Airtel-to-Airtel calls, under the “Tubonge Voice Bundle”. Such decisions are arrived at, after analyzing different types of data then driving insight out of them. Telecom industries are using advanced big data technologies and Data Science solutions for the real-time analysis of various aspects. This is helping them in setting the optimal price of products according to customers of different segments.

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Lifetime Value Prediction

In this era of telecom companies fighting against each other to better serve and seize customers from their competitors, the need for them to grow and retain their existing customer base is very important. But similar to the process of acquiring customers, there is a huge cost associated with the process of retaining existing customers too – by giving discounts, targeted offers, etc. It is therefore important for the telecom companies to measure, manage and predict the customer lifetime value (CLV) as failing to predict this value may result in profit loss.

CLV represents the total amount of money a customer is expected to spend in a business during their lifetime. This is an important metric to monitor because it helps to make decisions about how much money to invest in acquiring new customers and retaining existing ones. Data Science Methodologies enable the telecom industry to analyze and predict customer purchasing behavior, activity, services utilized, and average customer value. Smart solutions process real-time insights distinguishing between profitable, nearly profitable, and unprofitable segments of customers predicting future cash flows. It also helps the industry to provide relevant services to different segments of customers based on these predictions.

Predicting the CLV for each customer for the next 30 days.

Targeted Marketing

Data Science helps the Telecom Industry to predict what customers might need in the future based on their usage of different services with an e example of target marketing being the recommendation engines. It’s obvious that customers are usually attracted to better and cheaper services. For example, if a customer frequently visits a particular social media platform, you can offer them a monthly plan with some exciting and attractive offers – like what Safaricom in Kenya has done with a free WhatsApp offer where you can communicate via the platform even when you don’t have internet bundles. Such moves help in maximizing customer satisfaction and revenue generation. Another example is Globe Telecom in the Philippines, who collaborated with IBM and Nokia to develop a platform for targeted marketing.

Summing Up!

It’s evident that Data Science provides numerous opportunities for the Telecommunication Industries to smartly utilize the vast amount of data that is being generated daily. Countless Data Science solutions will help the industry to restructure their business tactics in the best conceivable and lucrative mode while focusing on satisfying their clients.

Time is ripe for the Telecom industry to embrace the new winning science by investing in data science competencies within their enterprise. The ideal group to drive this revolution are the data scientists whose role will be to empower the management and the rest of the staff to make informed decisions based on trends, transform the managerial capability into a quantifiable data-driven procedure, and deploy analytics models within the organization’s pipeline.

 “There are very few data scientists out there passing out their resumes. Data scientists are almost all already employed, because they’re so much in demand.”

LinkedIn co-founder Allen Blue

It is clear that soon telecom industries will be desperately sourcing for data scientists to join their team. Don’t let this opportunity pass you by!

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