WORLDWIDE' TOP 10 DATA SCIENCE USE CASES BY TELECOM OPERATORS

In today’s scenario, we are constantly connected to the people and the world around us and telecommunication use has surged to a large extent. It is no surprise that telecom spends on technology and hence data science is no exception. Data scientists are now thinking of novel ways to implement data science in everyday life. The revolution in technology features introduced in devices, and the build and aesthetic look of our smartphones today is proof enough that the telecommunication industry uses a lot of science and technology to deliver enhanced services which are equipped with the capacity to take the world by storm.

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What is Data science?

As organizations are often faced with the trouble of compiling and understanding the large chunks of enterprise data, they needed a solution or a framework to store data. This is when data science came into the picture. So, what is data science?

It is one which uses various tools, algorithms, and machine learning principles to discover hidden patterns from raw data. It is used to take decisions or make predictions. It is a multidisciplinary field which uses the knowledge and insights from structured and unstructured data using scientific systems and algorithms. It helps facilitate and empower decision-making.

?Let us now consider some of the most specific, relevant, and efficient use cases of data science in the telecom industry.

1. Product innovation

Real-time data obtained from multiple resources can be used to improve the products offered by the telecom industry. Customer usage can be analyzed and this will help in coming up with new product bundles which help in saving money and identifying and serving customer needs. An example of an innovative service offered by telecom is the facility of using their Wi-Fi service from anywhere.

2. Contextualized location-based promotions

Customer locations can be detected in real-time by telecoms. This is possible by triangulating the location of their mobile device. This information is used to send contextual promotions by partnering with different merchants. These promotions come with a high conversion rate. This way, the telecom company gets a cut or a commission for each transaction and also helps generate more revenue. A simple example would be giving a discount of 20-30% at a restaurant which is in close proximity to the customer’s residence or place of work.

3. Reduced fraud losses

Fraud is a major issue in the world of telecommunications. Results from a recent survey revealed that global telecom fraud losses are valued at $40.1. Billion USD or approximately 1.88% of the revenue. The three common types of fraud are:

·???????Bypass fraud-This refers to unauthorized traffic within a telecom network. Companies prevent this by using big data to review the source of transactions, the cost of the call, and the destination number, in real-world situations.

·???????Toll number fraud-Real-time call analysis helps reduce this type of fraud as it is possible for telecom companies to lose thousands of dollars very quickly if someone calls toll numbers that cost $5 per minute or higher than that.

Credit card fraud-?This is the usual chargeback fraud which impacts the corresponding verticals and this can be minimized by correlating real-time transactions with historical activity.

4. Better customer service

Telecoms offer services for cable TV, internet, phone, etc. On one side, they need to ensure excellent customer service and on the other, avoid customer churn. Customer churn refers to a customer ceasing his/her relationship with a particular company. Big data proactively work on these issues. The customer service team is provided with 360-degree visibility of customer data for understanding customer history. Big data helps aggregate and analyze customer data thus enhancing customer experience and minimizing customer churn. This lowers operational costs and results in better decision-making.

5. Increased network security

Ensuring the security of telecommunication networks is a daunting task. This has been simplified by data science as it allows streaming events in real-time. It helps telecoms identify security issues, conduct predictive analysis, and use machine learning-based solutions to analyze any patterns of threats and automated escalations. This helps resolve issues before they cause serious damage.

6. Predictive analysis

Telecom has thousands of devices and these devices have to be up and running all the time. Sensors are used to gather data about the current state of a device. It helps telecom companies take faster, better, and data-driven decisions. It uses historical data to build forecasts. The better the quality of data, the higher the predictability.

7. Targeted campaigns

Big data solutions help in understanding the customer better through reviews of how they use the services. A customer who makes calls to a specific country more often or one who watches on-demand movies can be targeted for a campaign involving both these areas. This can also be done in real-time. For example, after a customer finishes a call, a real-time campaign asking them to sign up for a monthly plan and stating that they could have saved 40% on the last call results in better conversion and revenue for the telecom. It also increases customer satisfaction.

8.?Call Detail Record (CDR) analysis

CDR contains information about each call which can help improve customer experience. Before using big data solutions, telecom companies used to spend a lot of time on this. Now, telecoms can review metrics such as packet loss, call quality, and call latency in real-time and take appropriate actions. They can collaborate with third-parties to identify unusual call activity or suspect conversations.?This data can be combined and compared with historical data to understand customer behavior and define usage patterns.

9.?Optimized pricing

Big data solutions help in reviewing several metrics in real-time to set a price for each product offering. Prices are tested among different segments of customers from various regions to decide on the most optimal price. This is a win-win situation for both the customers and the telecom as customers get the price they want and steady revenue is generated for the telecom.

10.?Real-time network analytics

Telecoms need to continuously monitor their network to avoid any issues well in advance. Big data has made this possible. Implementation of data science allows getting alerts if a part of a network is experiencing unusual traffic as this can impact customer experience. It also suggests the right time to upgrade the network to add more capacity while onboarding more customers. This helps them focus on areas which will deliver positive results.

Conclusion

Applying data science to telecom services will help reap financial benefits and enhance market competitiveness. The telecommunications sector is leveraging data science in every possible way. Data science, machine learning, and artificial intelligence are inevitable when it comes to the future of the telecommunications industry. These will continue to grow in the telecom space as big data tools are becoming more readily available and sophisticated. In the present and in the future, businesses which identify the massive number of data points flowing through their network and around it will benefit from reduced labor costs, better technology developments, and will gain a better understanding of the things that more than seven billion potential customers of smartphones and computers around the globe plan to do with their gadgets.

Thanks and see you soon in the coming article,

Hassan QADI