Telecom industry and data science

 

Telecom Industry and Data Science

The latest technological advancements have brought people much closer despite the distance between them. The higher level of connectivity has also increased data. Enormous information is produced through phone calls and text messages. In fact, it is true that telecom industries have left behind traditional techniques of handling data. New methodologies for handling large data have been introduced. This reflects the approaching era of big data and data science technologies.


The tremendous influx of data generated in the telecom industry resulted in increased demand for data science professionals. As a consequence, data science jobs and data science placements increased. In fact, data scientists are already employed because there is such a huge demand. Soon the telecom industries would desperately look for data scientists to join their teams.


But how has data science improved the telecom industry? How is the competition between so many rivals still running the industry? And how each one is using its own methods to be on top? All these questions would be answered in this blog.

Future of Data Science 

As per a research done by Analytics Insights, the rapid growth in big data is majorly due to the information technology and telecommunications industry. It holds a total of 33% share in the overall market. The telecom industry worth is expected to increase from $59 billion in 2019 to $105 billion in 2023. 


This clearly indicates that the demand for data science certification professionals will rise by 2023. The next two years are very critical for the telecom industry. The data science jobs will increase to over 4 million in the telecom industry itself.

Why Data Science in the Telecom Industry?

With the advent of internet of things and 5G mobile network, the consumer needs and demands have increased. The requirement of personalised services have tremendously risen up. The major cause for this is the demand for data science in the telecom industry. The present health situations turned many companies into full fledged Work from Home mode. Schools, colleges, and learning institutions turned to virtual and smart learning. This ultimately led to increased reliability for connectivity more than ever. Hence, digital communication such as work and study played a major role for integrating data science in telecom.


Data science has become critical in communication. With the reduction in workforce, limited physical access to data centres and call centres, data science integration in telecommunications is necessary. Remote working becomes smooth due to advanced telecom methodologies.


In this blog, we shall describe how IT and telecom companies integrate data science technologies in their work environment. How they are adapting to the changing system of the world and businesses. Also, in what ways they fulfil the requirements.

Big Data Management 

Earlier, data analysts and data scientists in the telecommunications department faced several problems. A lot of digital numbers, poor computation powers, and expensive costs were major hurdles.


In the present times, data science has eased the jobs:

  • Costs for data storage is reduced

  • Computation processing power rapidly increased

  • Affordable analytical tools and software available

Personalised Customer Services

Most industries today are focused on increasing comfort for the user. Improving user experience is one of the key objectives of data scientists. Data is collected from different resources for different purposes.


Customer demographics:

  • Gender, age, and address

  • Devices used for navigation

  • Services used

  • Physical geographical location


Customer behaviour:

  • Video watching choices

  • Social media activity

  • Customer care history

  • Previous purchase habits

  • Website visits

  • Duration of website visits

  • Browsing and searching patterns

  • Voice data usage patterns

  • SMS data usage patterns


Such data allows the telecom companies to provide personalised services and products to the customers. It eases the purchasing process in every step. Tailored messages can be provided for each specific customer using the right content and images.


Such tremendous efforts have given power to telecommunication to track customer experiences. It helps in establishing a cordial vendor and buyer relation. Even post purchase behaviour can be tracked.


Analysis of such data can help in identifying valuable aspects. Key performance index values or KPI are combined for:


  • Developing ideas for brand promotion

  • Avoiding customer churn

  • Determination of life time value of subscribers

  • Revelation of cross channel insights

Lifetime Value Prediction

Telecommunication companies are in deep competition with each other. There is a constant fight to survive. However, only those competitors can retain customers who serve better. There is an urgent need to grow and retain the huge customer base. The process of gaining customers however is very costly. New offers, discounts, and campaigns are given to the existing customers.


It is essential for the telecom industry to manage, measure, and predict the customer lifetime value or CLV. It is an essential aspect that predicts the value of the customers. It assesses profits and losses in the future. It represents the amount of money invested by customers in a business in their lifetime. Many important decisions can be made with such an essential insight in hand. Better and informed choices are made on investment of money in acquiring new clients and retaining the older ones.

Price Optimisation

The telecommunication industry has tremendously expanded its area. Many new firms and companies are coming up. Therefore, the competition is cutthroat in telecom. Each company wishes to have the largest number of subscribers. But that is not possible. In such a situation, the rate of the products and service charges play a critical role. To increase customer base and prevent churn rate, this aspect must be considered.


For instance, telecommunication companies providing instant free calls or free text messages for the next 30 days is a result of such analysis. Critical decisions arrive after getting insights from the different types of data. Employing advanced big data technologies is a part of every telecom industry now. Indeed, data science solutions such as real-time analysis and predictive analytics play a vital role. As a result, optimal prices of products as per the different segments of customers can be settled.

Network Optimisation

It becomes expensive when the network is down. Also, underutilisation or over taxation can cause problems. In fact, reaching maximum capacity can also be troublesome.


This problem was resolved in the past by putting caps on data. Also developing tiered pricing models helped the telecom companies to handle this problem.


Real-time analytics and predictive analytics can put a stop to these problems in the future. It will allow the companies to analyse the behaviour of subscribers. Thereby, creating individual network usage policies and systems.


This way, maximum customer satisfaction can be ensured. Also it improves revenues and efficiency. 


Real-time analysis can prevent damage to a great extent. Let’s consider these instances:

  • If a network goes down, every department is affected and customers are concerned too. By using real-time analysis, immediate steps can be adopted to address the issue.


  • If a customer abandons the shopping cart, the customer care department can communicate it quickly. A call, email, or a text message can be delivered for further conversation.

Network Security

An important aspect which is the source of high concern is the network security. While working over the internet, or connectivity such concerns should be addressed. There is no network that can escape cyber attacks. Therefore, an efficient and stable network security system is critical for protecting private data. An excellent security system aids the telecom industry in reducing the risk of data breach and sabotage.


Data science integration in network security is very vital. It works efficiently in ensuring that network security is checked in real-time. It analyses previous data and predicts upcoming problems and security threats.


Before a severe problem, this analysis suggests suitable actions.

Sentiment Analysis and Social Media

The past 10 years have been incredible for social media platforms. Its evolution has been tremendous. The way companies work today have been transformed completely. Every day data is produced and data scientists harvest this data. It is thoroughly checked for any reviews, feedback, rants, or any such information. This is followed by a detailed sentiment analysis.


The objective attend while doing this are:

  • Tracking usage patterns

  • Improving brand image

  • Defending brand image

  • Monitoring reaction to released products

  • Responsiveness to offers and campaigns

  • Identifying new revenue streams

  • Handling problems efficiently

  • Easing the concerns of customers

Prevention of Churn

The biggest challenge faced by industries is the instability of customers. Customers jump from one network to another in search of cheap services. This is called customer churn. The basic reasons behind customer churn are:

  • High service prices

  • Average services

  • Low connection quality

  • Better competitors

  • Traditional/old technology


Data science has utilised some effective technology to prevent churn. It utilises real-time and predictive analytics for the same:


  • Tracks customers website movement or swapping devices or SIM

  • Combining variables such as calls, number of texts, average bill, and more to predict customer behaviour

  • Applying sentiment analysis on social media to detect opinions

  • Segmenting customers based on target

  • Providing personalised and promotional products

  • Responsiveness to customers when change occurs


Many vendors partner with telecommunications operator to understand the following:

  • Using in-depth analysis to optimise features such as automated calls

  • Improving the completion rate by 30%

  • Identifying potential channels by a higher probability, by a factor of eight

  • Running targeted churn prevention campaigns

Targeted Marketing

Of course, this new trend set by data science is ruling over all the industries. Targeted marketing is an essential phenomenon that helps in retaining the customers interest. Telecom industries predict the needs of the customers in the future. It is based on the recommendation engines. This technology displays attractive and cheaper services to the ones looking for it. 


In simple words, if a client frequently visits a platform and views products, then this data would be stored. On analysing this data, better exciting and attractive offers for the same products can be displayed next time on their visit. Such smart moves have helped in improving customer satisfaction and maximising the revenue stream.  

Conclusion

It is crystal clear that data science is supporting the telecommunication industry immensely. Countless domain or specialised industries are restructuring their business models using data science solutions. Such heavy inclusion of data science has led to generation of data science job opportunities. 


It is evident that the upcoming years are very promising in the telecommunications industry. Since the future of data science is very bright. With the arrival of newer and better technologies, every industry is expected to grow. It could be a wonderful opportunity to pursue a career in data science and understand the roles and responsibilities of a data scientist. 


Professionals and non-professionals from any domain can learn data science skills. One of the best data analyst course in delhi & data science institute in delhi are provided by learnbay. Learnbay offers a wide range of domain specialisation courses in AI, ML, and data science. For more details on data science jobs and data science courses, visit their website.

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