Data Science
Session 1
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How Big Data Analytics can optimize the
telecom Sector?
We are in the era of Big data. Big data is presenting excellent optimizing
opportunities to the companies in various sectors. Big data when analyzed well,
can provide actionable insights that aids in devising strategies to optimize multiple
business processes.
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Big data has become an essential part of the telecom industry due to the
enormous amount of data being generated by the telecom world. Telecom
operators are sitting on gold mines as the smart devices have enabled the telecom
operators to gain access to specific information about their customers’ behavior,
preferences, movement, etc.
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Today’s telecom industry is facing many challenges. Telecom operators have
been using Big data analytics to evaluate and formalize strategies to enhance
customer satisfaction, preparing the network for future demands and to make
data-driven decisions to optimize the processes.
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Telecom companies have realized that analyzing its data while combining the data
of different sources is the key gain a more profound understanding of their
customer behavior, preferences, product performance. Through Big data analytics,
telecom companies can battle challenges they face today.
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A recent study suggested that 47% of the telecom operators are investing in Big
data analytics and 19% are expected to implement a Big data strategy soon.
Telecom companies are pressed hard against Hyper-competition, slow revenue
growth, and increasing network costs that compel telecom companies to deploy
advanced analytics solutions.
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Big data analytics will help to improve their profitability and gain a competitive
advantage by enhancing customer experience and optimizing network usage.
Telecommunication companies are using Big data to gain insights about the
potential of new product offerings, enhance customer experiences, reduce service
truck rolls while improving customer service, forecast network capacity and
demand faster and more accurately, optimizing value-based network capacity
planning.
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Analyzing Big data is a challenge in itself for telecom providers as certain
characteristic of Big data such as the variety, velocity, and complexity are
challenging to deal with. The data that telecom companies gather is from various
sources such as social media networks, connected devices, government portals,
call data records, billing information. Thus, it adds to the complexity of Big data.
The fact that the data is mostly unstructured adds more to the complexity as its
analysis is out of the capacity of traditional databases.
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A recent survey reports that every minute, Indians spend ₹ 1.85 million on
shopping online. More than 100 hours of video is shared on YouTube every
minute. The average time spent by a social media user is 2.5 hours a day. The
data generation speed is extremely high and to gain value from this data; it needs
to be analyzed in a particular time frame. Real-time processing of Big data is
becoming crucial for many applications.
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Retaining existing customers along with attracting new ones is one of the most
critical challenges in the maturing mobile telecommunications service industry. By
deploying predictive models and machine learning algorithms, it is possible to
identify customers who are likely to leave the network, accurately. Bringing
together data collected on customer usage, complaints, transactions, social
media, they can formulate factors which can identify customers at risk of moving
out.
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In the end, it is safe to conclude that Big data analytics is the key for growing
revenue for telecom companies while understand and serve their customers
better.
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