Examples of Data Science in the Telecom Industry
One of the most innovative aspects in the telecommunications industry today is data science. With greater volumes of data than ever before, telecom providers are increasingly leveraging data science tools and Artificial Intelligence to make sense of it all. Since the main activities of companies working in the telecommunication sector involve data transfer, exchange, and import, it is imperative that telecom providers invest in data data science tools that can manage and extract useful insights from the vast amount of data generated every day.
There is a wide variety of data science examples in the telecom industry. Below find a brief rundown of the top examples of data science in the telecom industry:
Within the telecommunications industry, one of the most significant challenges is being able to detect fraudulent activities. It is such a challenging endeavor because the telecom sector has such a high number of users with an even higher number of spots that are vulnerable to security breaches. Some examples of data science in telecom fraud detection include the application of unsupervised machine learning to customer and operator data, fraud detection systems, tools, and techniques can be used to detect unusual user activities and proactively prevent fraud from occurring altogether. Data science tools with visualization capabilities make it easy for telecom operators to perform network performance monitoring and visually detect the characteristics of normal traffic and patterns associated with abnormal traffic.
A popular data science case in many industries is price optimization. Great competition exists between telecommunications companies, and with so many available options, customers are inclined to use the services of establishments that are offering the best prices. With the help of advanced data science, pricing was developed as a way to simultaneously limit congestion while also increasing revenue. Pricing strategies are complex and involve a wide variety of factors, such as competitor pricing, time of year, operating costs, macroeconomic variables, sentiment analysis of customer reaction to different prices and perceived value of services, and more. Data science tools make it possible to ingest and consolidate all of this data, show how they impact and relate to one another, and inform an effective strategy that will maintain positive revenue while also retaining happy customers.
Network health, optimization, and profitability are essential for all telecom providers. Leveraging AI-powered technologies throughout the network lifecycle automates this complex and labor-intensive process and enables real-time access to internal and third-party data. This data can measure the network’s performance against assigned strategic objectives. Data science tools use real-time monitoring and forecasting to predict future network demands and determine where and when to expand capacity for maximum returns. This is particularly important for optimizing 5G infrastructure. With an increase in emerging 5G use cases, such as network anomaly detection and optimizing 5G network architecture, telecom companies must be ready to predict and adapt to constantly fluctuating demands.
Data science use cases in telecom involve a great deal of real-time data. The telecom industry has evolved, and in order to meet ever-changing customer requirements, providers are using analytical solutions to track data in real-time. Real-time streaming analytics gives providers a constant, 360-degree view of data related to customer profiles, network, location, traffic, and usage. Regular and frequent analysis of this data helps providers get a better understanding of customers’ reactions towards and usage of their products and services, and improve customer service. While subscribers are becoming more and more demanding and traffic becomes more active each day, real-time analytics helps providers meet these expectations with real-time analysis and real-time responses.
Preventing Customer Churn
Data science in telecommunications helps providers anticipate what their customers want, and also predict potential issues before they become real problems. This makes for happy customers, and happy customers don’t churn. Data analytics is incredibly valuable for customer church analysis in telecom because it provides accurate insights about customers’ feelings and behaviors. If a customer is unhappy with their service, the data will be able to provide insight and help the company efficiently build satisfaction. Data science tools combine data related to factors such as transactions, real-time communication streams, and social media sentiments to predict customer churn and develop proactive strategies for customer retention in the telecom industry.
Telecom data science tools help providers predict what customers might need in the future by looking at past trends. This technology is used primarily for customer segmentation and targeted marketing, as people are more likely to buy from a company when they're given offers based on their specific interests. For example, if you notice someone frequently calls one particular country, you might offer them a monthly service plan with some exciting add-ons. A recommendation engine uses Machine Learning algorithms and data analysis techniques to recommend the most relevant service or product to a particular user. This helps maximize customer satisfaction and generate revenue.
Customer Lifetime Value Prediction
A highly valuable use of data science in telecom is the prediction of Customer Lifetime Value. A Customer Lifetime Value (CLV) prediction model uses data related to customer purchasing behavior, activity, and services utilized in order to measure, manage and predict the CLV.
Telecom providers use CLV predictions to help inform activities such as: offering relevant services to specific segments of customers, targeting the ideal customers, identifying issues in order to boost customer loyalty and retention, and reducing costs associated with customer acquisition. The cost of retaining customers is huge, so not knowing your customers’ lifetime value is sure to negatively impact your profits. The process of creating a CLV model is five steps:
- Define the time frame for CLV calculation
- Identify the features to be used to predict the future and create them
- Calculate lifetime value (LTV) for training the ML model
- Build and run the machine learning model
- Check if the model is useful
Location Based Promotions
Another highly profitable telecom data science use case is the location-based services strategy. Location-based marketing matches opted-in, privacy-compliance location data transmitted from smartphones to points of interest. Being able to serve up promotions to people based on their geographic location at that moment involves a lot of real-time data. Telecom providers detect the real-time location of customers and may send promotional texts for a business they are near. This is accomplished by using data science to detect the customer's location and by partnering with different merchants. Most modern network operators have implemented analytic tools that are able to process customer geodata both internally and externally.
Data science helps telecommunications companies provide the products most suited to their customers needs. Using the customer data these companies collect, they can provide products that suit the needs of both their customers and themselves. Data related to customer usage and feedback is used heavily in the product development process, which results in cutting time, cost, and risk. Customer feedback and usage analysis is used before launching a new product as well as for improving an existing product/service.
Network security is an incredibly important issue in the telecom sector. With the use of data science tools, telecom companies can identify anomalous network activity and predict potential breaches of security before they happen. These predictions can be used to take proper actions before severe consequences occur. Data science in the telecommunications industry facilitates the analysis of real-time data, which helps with responding to issues as they occur, as well as historical data, which helps with predictions based on previous vs. current patterns of behavior.
Data science can help us predict the future and make better, data-driven decisions. With the use of real-time and predictive analytics, telecom companies can use data collected by their devices to build forecasts and gain insight into what they can do to improve operations. In leveraging predictive analytics, telecom companies can better understand their customers’ preferences and needs, which will ultimately improve the customer experience and give the company an advantage in a highly competitive industry.
The HEAVY.AI Advantage
Data science applications in the telecom industry are only as useful as our ability to interpret and leverage the data they generate. The telecom industry is filled with opportunities for growth and has enormous potential for optimization, and only the most advanced big data analytics tools can truly harness the vast data streams that telecom assets and customers generate. HEAVY.AI’s hardware accelerated analytics delivers greater speed and performance than any legacy system could handle. Instant interactivity coupled with dynamic visualizations of big data drives faster action and better decisions.
HEAVY.AI for telecom facilitates faster insights and improves our ability to ask the right questions and define objectives, fix inconsistencies and gaps in data, analyze data and form hypotheses about problems, conduct effective CLV predictions, perform fast data discovery, train accurate predictive models, monitor ML models, optimize networks topology and customer service strategies, and create interactive data visualizations that reveal hidden insights and clearly communicate findings to the right people – all at lightning speeds.
When HEAVY.AI gives data scientists and utility managers the power to explore big data at the speed of thought, the questions you can answer are limitless.