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Data Science to businesses is quickly becoming like oxygen to humans. Data Science is inevitable for industries to flourish. 

Telecom sector is no exception to this fact.

Telecom is also adopting Data Science not only to maximize profits but also to increase operational efficiency and customer experience.

Why is Data Science Required in Telecom Sector?

Analyzing data to increase profits, create effective business and marketing strategies, visualize data etc. is now a common practice for the Telecom sector. 

They have realized what Data Science can do.

Telecom sector is completely dependent on data import, exchange and transfer. Traditional techniques are now obsolete. 

Why? Data moving through multiple communication channels, is increasing with every passing moment.

Modern data analytics techniques will work wonders, in order to realize the true worth of data generated in the telecom sector.

How Telecom Sector is Moving Forward with Data Science?

Telecom industry makes the most of Data Science. It uses data to solve pertinent issues, ensures no more issues crop up, eliminates the chances of deceitful practices and many such points.

It becomes imperative for the Telecom sector to adopt Data Science as one of the main focus points is customer satisfaction. 

Let us look through 5 such applications of Data Science in the Telecom Sector. 

1. Customer Experience

There is no dearth of data in the Telecom organizations. Millions of customer complaints are analyzed to know common complaints and types of solutions needed. The right solution at the right time for the right problem will increase customer satisfaction.

Satisfied customers are loyal customers.

On the internal front, how did a technician deal with a customer complaint, can also be a source for analyzing his performance.

Customer calls are more likely to generate a lot of unstructured data. Analyzing it will mean reduced truck rolls costs. This will come to face more when the types of problems that lead to increased truck rolls are identified.

This is why deep learning will work wonders in this area.

2. Customer Segmentation

The number of predictive analytics applications in the Telecom sector is probably more than in any other industry. The aim of the Telecom industry is to predict on the basis of historical data. With the application of predictive analytics to the core, the industry can know their customers well.

Customer segmentation is one such application of the Telecom industry. If the telecommunication companies can divide their market and then target the content, success is not far away. 

Customer behavior segmentation, customer migration segmentation, customer value segmentation and customer lifecycle segmentation are the four segmentation areas.

You can predict customer’s reactions, preferences and needs. Whether your customer like the offered products and the services offered? Everything can be analyzed easily.

3. Product Development

Vague product development, without any strategies and planning, will definitely incur huge losses. 
Careful and strategic planning, continued throughout the product development lifecycle will increase a loyal customer base.

With the use of insights collected from data analysis and predictive analytics, targeted product development will be feasible; your customers will get what they want, with top quality.

A top quality product is the one which takes into consideration market intelligence, digital analytics implementation and internal feedback, along with being customer-oriented. 

Predictive analytics comes to use in this sphere as well. Predicting which product will be accepted well in the current market scenario and competition, is an important point for the telecom sector.

4. Real-time Analytics

If there is one industry that isn’t bothered much by the colossal amount of data in recent years, it is the Telecom industry. This
is because the Telecom industry has been dealing with mammoth data for many years; even before data’s importance was undetermined.

Internet has rapidly transformed and so did the network technologies- 3G, 4G and 5G. This means rapidly changing customer demands.

With a rapid increase in the customer base, the load is on the network. Telecom companies are now bound to maintain the network properly, avoiding glitches, so as to increase customer base and retain existing ones.

Real-time data is used to collect real-time issues, solve them and provide responses. 

Real-time analytics collates data related to location, usage, customer profiles, traffic and network. It then builds a comprehensive view of product or service.

When the response is real-time, changes happen within a blink of an eye and telecom sector is able to flourish.

5. Customer Sentiment Analysis

Telecom sector has witnessed change as no other industry did. This was because of the increasing influence of the internet services. As a result, the field is too vast and understanding the customers becomes challenging.

A collection of methods used to process information is known as customer sentiment analysis. This process assesses the positive and negative feedback of the customers for a product.

When the collected data is analyzed properly, major insights and latest trends are unearthed. A real-time solution to the issues faced by customers is also possible.

Text analysis techniques enable customer sentiment analysis. 

It becomes easy for latest tools to gather data from social media channels as customers are more active on this medium. Data thus collected, is analyzed to gather sentiments and targeted and useful response is directed towards the customers.

Prompt and directed use of Data Science and machine learning has nurtured the telecom sector. 

With huge amount of data rolling in every moment, it makes sense for the Telecom sector to extensively use data and algorithms to keep up with the competition.

Gone are the days when technical glitches or network failures took days to be fixed.

Everything is done in real-time now. Faults are anticipated to a greater extent and then preventive measures are applied.

Author

Senior Content Writer @INSAID. Data Science and AI enthusiast who loves to read, write and converse.

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