KDnuggets Home » News » 2019 » Feb » Opinions » Top 10 Data Science Use Cases in Telecom ( 19:n08 )

Top 10 Data Science Use Cases in Telecom


In this article, we attempt to present the most relevant and efficient data science use cases in the field of telecommunication.



By ActiveWizards

Header image

In the course of time, data science has proved its high value and efficiency. Data scientists find more and more new ways to implement big data solutions in daily life. Nowadays data is a fuel needed for a successful company.

Telecommunication companies are not an exception. Due to these circumstances, they cannot afford not to use data science. Within the telecom industry data science applications are widely used to streamline the operations, to maximize profits, to build effective marketing and business strategies, to visualize data, to perform data transfer and for many other cases. Key activities of the companies working in the telecommunication sector are strongly related to data transfer, exchange, and import. The amounts of data passing through various communication channels are getting larger every minute. Therefore, old techniques and methods are no longer relevant.

In this article, we attempt to present the most relevant and efficient data science use cases in the field of telecommunication.

 

Fraud detection

 
Telecommunication industry being the one attracting almost the most significant number of users every day is a vast field for fraudulent activity. The most widespread cases of fraud in the telecom area are illegal access, authorization, theft or fake profiles, cloning, behavioral fraud, etc. Fraud has a direct influence on the relationship established between the company and the user.

Therefore, fraud detection systems, tools, and techniques found wide usage. By applying unsupervised machine learning algorithms to an immense amount of customer and operator data to spot the characteristics of normal traffic you can prevent fraud. The algorithms define the anomalies and with the help of data visualization techniques present them as alerts to the analysts in real time. The efficiency of this technique is very high because it allows to provide an almost real-time response to the suspicious activity.

 

Predictive analytics

 
Predictive analytics is applied by the telecommunication companies to get valuable insights to become faster, better and make data-driven decisions. Knowledge of customer preferences and needs gives a better understanding of the customer. Predictive analytics uses historical data to build forecasts. The better is the quality of the data and the longer they are historically, the better is predictability.

Let us take into consideration several use cases of predictive analytics in the telecommunication industry.

 

Customer segmentation

 
The key to success for the telecommunication companies is to segment their market and target the content according to each group. This golden rule is relevant to the various areas of business. Speaking about telecommunication, there are four segmentation schemes of primary importance: customer value segmentation, customer behaviour segmentation, customer lifecycle segmentation, and customer migration segmentation.

Advanced targeting allows predicting needs, preferences and customer’s reaction to the telecommunication services and products on offer. It enables enhanced business planning and targeting.

 

Customer churn prevention

 
Acquiring a customer is a challenging task. Keeping the customer engaged requires a lot of effort as well. Accurate diagnosis of the customer's behavior and enabling alerts highlight the customers at a risk defecting. Smart data platforms can bring together customer transactions data and data from real-time communication streams to disclose the insights concerning customers feelings about the services. This allows immediate addressing the satisfaction-related issues and churn prevention.

 

Lifetime value prediction

 
Customers tend to search for better and cheaper services, therefore, it is important for the telecommunication companies to measure, manage and predict the customer lifetime value (CLV). Failing to predict this value may result in profit loss.

Customer lifetime value is a discounted value of all the future profits and revenues generated by the customer. The CLV model is concentrated on 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.

 

Network management and optimization

 
Telecommunication companies tend to regard the customer's engagement process and internal channels as a guarantee of smooth functioning of the operations. Network management and optimization gives an opportunity to define the score points in operations to identify the root causes of these complications. Looking into historical data and predicting possible future problems or, on the contrary, beneficial scenarios is a great benefit for the telecom providers.

 

Product development

 

Product development is a complex process that needs control and thoughtful management starting from the stage of concept development till ongoing lifecycle management and maintenance. Ensuring the high-quality performance of the product according to the customer’s requirements is impossible without applying smart data solutions. Data-driven product development process should take into consideration not only customer needs but the results of digital analytics implementation, internal feedbacks, and marketing intelligence.

 

Recommendation engines

 
Recommendation engines are present in all the spheres of our digital life. Telecommunication sphere is among these aspects. Ignoring the enormous data sets concerning customers preferences would become a significant loss for the telecom. Prediction of the future needs becomes possible to the availability of data.

The recommendation engine is a set of smart algorithms depicting customers behavior and making a prediction about possible future needs of the product or service. Most popular approaches here are collaborative filtering and content-based filtering.

Collaborative filtering relies on the analysis of data on user's behavior or preferences and predicting what they will like by their similarity to others. The critical assumption of the model is that people with similar profiles may have similar needs and make similar choices.

Content-based filtering approach utilizes the attributes concerning relations between the customer profile and the items the customer chooses. Thus, the algorithm recommends the items and services similar to those previously purchased.

 

Customer sentiment analysis

 
The telecommunication sphere is under constant change due to the increasing role of the Internet services. For each telecommunication company, this may be regarded as a vast field to learn and understand the customers.

Customer sentiment analysis is a set of methods applied for information processing. This analysis allows assessment of the customer positive or negative reaction to the service or product. Analysis of the aggregated data also allows revealing recent trends and reacting to the customers’ problematic issues in real-time. Customer sentiments analysis largely relies on text analysis techniques. Modern tools collect feedback from various social media sources conduct analysis and provide an opportunity of utilizing mechanisms for direct responding.

 

Real-time analytics

 
The telecommunication industry is famous for its long-term experience in dealing with significant data streams for years. Due to rapid development of the internet and the evolving of 3G, 4G, and even 5G connections, telecommunication companies face the challenge of the constantly changing customer requirements. The subscribers are becoming more and more demanding, and the traffic gets more active every day.

Real-time streaming analytics can deal with this task. Modern streaming analytic solutions are specially 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.

 

Price optimization

 

The telecommunication sphere belongs to highly competitive industries. Acquiring as many subscribers as possible remains a critical goal, anyway. Due to the fact that in recent years the number of users has been growing extremely fast, pricing emerged as a tool to limit congestion and increase revenue at the same time.

Dynamic pricing approach strives to map lifetime values, tariffs, channels to calculate price elasticity at the intersection of device, channels, and pricing plan and to combine this data. Basing on these insights the interdependencies between pricing, promotion, and future revenues may be defined.

 

Conclusion

 
Telecommunication industry has been boosted by the active use of machine learning and data science. This step was made only for the better. A great many of aspects and issues became much easier to resolve, control or even prevent from happening.

The telecommunication sphere had to adopt modern technologies and techniques to stay relevant and not to lose positions under severe circumstances of the global market. Telecom companies operate with vast communication networks and infrastructures with the intense data flow. Processing and analyzing this data with the help of data science algorithms, methodologies and tools find practical application. Therefore, we attempted to specify several of these use cases and to demonstrate real benefits one can get.

 
ActiveWizards is a team of data scientists and engineers, focused exclusively on data projects (big data, data science, machine learning, data visualizations). Areas of core expertise include data science (research, machine learning algorithms, visualizations and engineering), data visualizations ( d3.js, Tableau and other), big data engineering (Hadoop, Spark, Kafka, Cassandra, HBase, MongoDB and other), and data intensive web applications development (RESTful APIs, Flask, Django, Meteor).

Original. Reposted with permission.

Related:


Sign Up

By subscribing you accept KDnuggets Privacy Policy