Top 10 Data Science Use Cases in Telecom Industry
Today, advancements in technology have brought the world closer. People are able to connect with their loved ones and other people sitting far away from them in just a few seconds. With the increasing connectivity, the data is also increasing. With our daily calls, messages, etc, we are generating a huge amount of data. So it is no surprise to know that Data Science in Telecom Industry is helping to handle such a large amount of data.
The Telecom Industries now can not employ the traditional techniques and methodologies for handling the data which is increasing with each passing minute. Thus they are approaching advanced Data Science tools and Big Data technologies for utilizing this data.
However, the Telecom Industry uses the insights gained with the help of Data Science for various purposes such as:
- For maximizing profit
- Planning efficient business and market strategies
- For data visualization
- For performing data transmission, etc
In this tutorial, we will go through some of the most important use cases that will explain the role of Data Science in Telecom Industry.
Before we proceed ahead with data science use cases in telecom, let’s firstly revise what is data science.
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Top 10 Use Cases of Data Science in Telecom Industry
The top use cases of Data Science in Telecom Industry are:
1. Product Optimization
Providing the best-suited products according to the needs of the customers is a very important concern for any industry. The Telecom Industry is using Data Science to perform the real-time analysis of customer data for improving their products. Various factors like the customers’ usage, feedback, etc are taken into consideration for coming up with new products that will benefit the customers as well as the industry.
2. Increased Network Security
One of the biggest concerns of the Telecom Industry is to ensure the security of the networks. Data Science helps them to identify the problems. It also helps them to analyze the previous data and make predictions about any problem or complications that might appear in the near future. This analysis helps them to take suitable actions for any problem before it’s severe consequences.
For example, Brightlink communications which provide voice, messaging, analytics, and cloud solutions had stated in 2013 that they are using Net Optics Director Pro (a network controller switch) for monitoring their calls.
3. Predictive Analytics
The Telecom industry has to manage and maintain a large number of devices that are continuously running all the time. The Telecommunication sector performs predictive analytics on the data collected by their devices for gaining valuable insights. These insights help them in making some smarter data-driven decisions for becoming faster and better.
4. Fraud Detection
The detection of fraudulent activities is one of the biggest challenges for the Telecom industry. The Telecom industry, along with having the most number of users also witnesses a large number of cases of fraud. 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.
The most common fraudulent activities in the Telecom world are unauthorized access, fake profiles, misuse of credit/debit card information, etc. Thus the Telecom industries are using various unsupervised machine learning algorithms for detecting unusual user activities and preventing frauds.
For example, a very well-known Telecom Enterprise Vodafone is working with Argyle data for detecting and preventing frauds with the help of fraud analytics.
5. Price Optimization
The competition between the industries in the Telecom sector is increasing day-by-day. Everyone over there is aiming to have the largest number of subscribers. Pricing of products plays a very important role whenever it comes to increasing subscribers or users. The Telecom industry is using advanced Big data and Data Science solutions for the real-time analysis of various aspects. This will help them in setting the optimal price of products according to customers of different segments.
6. Real-time Analytics
With the advancements in the telecom industry such as 2G, 3G, 4G, etc, the customers’ needs and expectations are changing. To cope up with this, the Telecom industry is using modern analytical solutions for performing regular analysis of data collected from the diverse range of resources. This real-time analysis helps them to keep an eye on data related to network, traffic, customers, etc. This helps them in understanding the users’ reactions towards their products and services.
7. Preventing Customer Churn
The various services offered by the Telecom industry are TV, internet, phone, etc. Making the customers believe that you are worth their time and money is a challenging task. Keeping them engaged for a longer time is even more challenging. Thus you need to apply proper and accurate analytics for understanding customer’s behavior. They extract valuable insights about customers’ feelings from the customer transaction data and analyze them. This helps the Telecom industry in building satisfactory solutions to customer issues. This helps them in ensuring better services and avoiding customer churns.
For example, Analyx is a leading IT service provider in Europe known for applying Data Science solutions to marketing. The European telecommunications operators have teamed up with the Analyx to help them in identifying the potential churners. This helps them in taking effective measures for the prevention of customer churn.
8. Targeted Marketing
Data Science is helping the Telecom Industry to predict what customers might need in the future based on their usage of different services. Recommendation Engines are the biggest example of targeted marketing. The customers are always attracted to better and cheaper services. For example, if a customer makes frequent calls to some particular country, you can offer him a monthly plan with some exciting and attractive offers. It helps in maximizing customer satisfaction and revenue generation.
For example, Globe Telecom, which is one of the popular telecom service providers in the Philippines, collaborated with Nokia and IBM for developing a platform for targeted marketing.
9. Customer Lifetime Value Prediction
Customer Lifetime Value is a measure of the overall profit or revenue that can be generated by a customer throughout his relationship with the industry. Predicting the CLV of any customer is very important for every industry. Data Science solutions help the Telecom industry to provide relevant services to different segments of customers based on these predictions.
10. Location-Based Promotions
You all might have observed that whenever you are around some restaurant, you start receiving promotional text messages. This is done with the help of Data Science. The Telecom industry detects the real-time location of the customers and sends promotional texts by partnering with different merchants. These location-based promotions help the Telecom industry to increase their revenue.
Along with telecom industry, learn other Real time applications of data science.
Finally, after going through this tutorial, we can conclude that Data Science provides a number of opportunities for the Telecom industry to smartly utilize the huge amount of available data. Various Data Science and Big data solutions are helping the Telecom Industry to reshape their business strategies in the best possible and profitable way. This also helps them to always keep the customers in the center.
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