How big data helps telecom companies

How big data helps telecom companies


Ilya Semenov

Big Data is changing virtually every area of ​​our lives, and business first. But for some reason, not all companies, even large ones, have switched to using this technology.

As you know, Big Data allows you to analyze the behavior of shoppers or Internet users by tracking which sites they visit, where they are, and where they regularly visit, at which
hours and to whom they call, what programs they download and how they use them, etc.

Big Data and telecom operators

One of the biggest owners of big data in Russia is the telecom operators, through whose billing systems daily flow streams of information about many thousands of subscribers. As a result, they are
really know almost everything about us, down to passport data and phone models. And all this information can be monetized. As soon as the business understands this, Big Data is turning from a trend into a must
have, under which the whole structure of telecommunication operators begins to be rebuilt.

Using Big Data, Operators Increase Revenue: Flexibility of Offers and Context Retains Customers and Attracts New, and Big Data Analytics Enables Product Managers
it is directed to act in accordance with the product development strategy.

But to offer something, we need services that will provide the client with content, and the operator with data on which to analyze and generate new content. The more data, the more
the better you can build a portrait or customer avatar, the better you can analyze. The portrait of the client must be constantly enriched, as many data quickly become out of date on the axis of time.

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The world around is changing fast, and the model of work of companies should change thereafter. The modern subscriber is the consumer of many services, and the monomodels of work with the user are a thing of the past.
Providing only communication services is not enough – the world is becoming an ecosystem. A true customer-oriented operator, provides converged services, organizes its own
marketplace, interoperability interaction with external services and operators, and its data is syntactically and semantically homogeneous, which gives a marked field for learning models of algorithms
intellectual analysis. Customer-centricity is perceived by the individual: if previously relevant offers were targeted at Group customer segments, now –
to a specific customer. The interoperability of the interaction of services with each other leads to the fact that in a heterogeneous network of heterogeneous services, information becomes equally interpreted and
semantically consistent. Recommendation systems are starting to work more accurately, personalized, using predictive models.

Keeping up-to-date customers requires new technologies, new interfaces, new communications and new content. Subscribers can no longer offer static content – they need it
adjust. This leads to the transformation of the industry. Ecosystems entails the development of various services that are related to communication services. Not only operators, but also,
for example, financial and IT businesses, creating cross-ecosystem competition. New technologies, such as IoT, will help to associate additional activities with the client.
This is a new data source that will enhance the quality of the virtual client model in the carrier’s systems. The client’s transparency will increase, which will allow him to work with him more personalized.

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Now, carriers collect large amounts of data per subscriber – as structured (actions, events, completed forms, transactions, transactions, purchases, favorite restaurants, websites visited and
etc.) and unstructured (e.g., voice traffic). Any client events are recorded with respect to the time axis.

What Big Data Solve?

Of particular value are the specialized applications in which the data is marked. Their analysis becomes simpler and more accurate. Those operators whose ecosystems have financial and medical services,
you can say they know everything. Any human activity on the Web is a source of new information that participates in forecasting its needs at some point in time. Accumulated data and
environmental data contribute to the development of algorithms for quality content generation. For example, customer geolocation surrounding external geolocation services (such as movie theaters,
restaurants and stores), customer-driven applications on a smartphone, visited resources, spending time in relevant resources and services, the content of those resources are all data sources,
enter the decision support system.

So, the British company JJ Food Service, which supplies food to restaurants and cafes, fills its customers’ shopping carts online based on their shopping history and recommendations,
this takes into account the recipes of establishments, similar orders from other users, etc. About 80% of these goods are actually left in the shopping cart and paid for.

Large retailers, such as Target, identify customers by bank card, name, or email, track their shopping histories, preferences, and activity in
social networks and make appropriate proposals.

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Big Data is the key to an operator’s future success. Whoever learns to analyze them well will be the winner. Data analysis enables you to make the right decisions. One of the main goals
operators – retaining loyal customers and increasing profitability from one subscriber. We need to work on this. There are great examples of profitable businesses on the market that have focused on big ones
data: for example, Netflix earns up to 75% on purchases of goods offered by the recommendation system, which is based on both collaborative filtering algorithms and
personalized. Amazon is working in the same direction, increasing the profitability of its business.

Regardless of the methods used to handle big data, most tasks are a simple one: identify patterns of subscribers / users and provide appropriate
product or service on time. The operator knows his subscriber better, it is easier for the operator to build a strategy of its development, to estimate potential demand. Big data analytics helps you take it
optimal management decisions and reduce errors.

Big Data Tasks:

  • Customer segmentation by behavioral characteristics (descriptive analytics).

  • Development and launch of new products / services.

  • Formation of predicative proposals.

  • Product / service life cycle forecasting.

  • Speeding up customer service and deciding whether to provide / deny service.

  • Reduced support costs.

Big Data telecoms are used to evaluate the existing subscriber pool and to predict their behavior to adequately expand the list of services. Now subscribers are increasingly calling,
send SMS and use the Internet more. This allows Big Data telecom operators to analyze their customers’ preferences and then offer them
personalized sets, such as downloadable content. Another thing is, not everyone likes that his traffic is being analyzed by anyone, not even a human, but a robot. But this issue is already being addressed in
each country in its own way.

The future behind Big Data

Many telecom operators are already learning to work with big data and offer solutions based on it. But all the fun is ahead. Big Data and machine learning can be used to build intellectual ones
services of analysis of voice data, video stream. By understanding the meaning of language content, you can build online customer interaction. Scientists at the Massachusetts Institute of Technology have developed
neural network models that have learned to recognize human health by biomarkers in his voice. Video content analysis can be used to classify emotions with application
Deep Learning technologies. The combination of both technologies – audiovisual content recognition – will improve the accuracy and naturalness of communication with the client.

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The IT market is already offering systems for data analysis and decision making, and we anticipate a technological leap in consumers of these decisions in the coming years. Obviously, the future of the operator
is built on Big Data and AI technologies, including:

  • decision support systems (DSS);

  • predictive analytics and data mining systems;

  • natural language processing systems;

  • systems of language technologies;

  • computer vision systems;

  • process control systems, etc. technology.

Thus, it is safe to say that operators will transform, diversify, develop their services and infrastructure, train their data analysis systems to
fight for the customer and keep him in his ecosystem.

Big Data, Telecommunications

IT-Expert Magazine [№ 01/2020],


Ilya Semenov

Director of Development and Quality Management at Bercut.

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