Five reasons to use Machine Learning

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As many readers would already know, Machine Learning means the ability of a machine to learn from data. In other terms, the use of Machine Learning algorithms represents a complete revolution in the area of statistical modeling. Data can be analyzed, thanks to Cloud Computing and high computational resources, without explicitly writing a model. The machine itself, normally using a set of labeled data, learns the relationship between input and output.

This approach is not only incredibly effective using structured data but, even more important, it allows to analyze and extract valuable insights from unstructured data, such as text and images. 

Machine Learning algorithms are in constant evolution and likely in five years we will see more and more automated systems, able to interact with us in a more and more natural way. Meanwhile, they are already part of our daily life. Have you ever talked with a smart speaker? The ability of the machine to listen and answer to your questions is not magic, it is machine learning!

  1. Why to use Machine Learning: 5 good reasons
  2. How PaperLit uses Machine Learning

Why to use Machine Learning: 5 good reasons

Nowadays, Machine Learning is a great buzzword. It seems that everything could be solved by this “black-box” where machines learn by themselves. Even though this idea is absolutely not true (or, we could say, it is not so simple!), it is true that Machine Learning can be extremely useful in many contexts and it has opened many opportunities for big and small companies.

Enhance predictive ability

A big use of data is related to the ability to predict what will happen in the future. In companies, as well, managers are more and more moving to a data-driven approach and they call for predictive models that can help to identify the right strategy and to make the right decisions.

In this context, Machine Learning can help to build more effective predictive models. Machine Learning, indeed, can be identified with a set of algorithms, from simple ones (as decision trees) to very complex ones (as neural networks), able to identify relationships within variables and, through this, predict the next value of the output given a specific input. A subset of Machine Learning, Deep Learning, allows to extract incredible insight from images, for instance.

Improve efficiency with intelligent processes 

Intelligent processes can enhance the company efficiency and reduce time spent by workers in boring and repetitive activities. Machine Learning allows companies to extract and analyze information from unstructured data. One example could be the information retrieval from documents and texts, another possibility could be related to image analysis. In the manufacturing world, for instance, one of the main applications of image analysis is quality control. Another important use case is fraud prevention, where the use of specific algorithms can be very effective.

Improve customer experience

Machine Learning enables high personalization in the customer relationship. Integrating data from different sources – both online and offline channels – the company can have all the information related to the customer history and, applying Machine Learning algorithms, it is possible to predict customer behaviour and customer sentiment. Using these methods, your company can – for instance – increase cross/up-selling abilities through recommendation systems or improve customer care activities through data-driven customer service.

Development of new products and services

Data Analysis in general, and Machine Learning even more, can have an important role in suggesting the right decisions to take. Right decisions do not mean just how to act in a specific situation or which part of the machines you should substitute before the breakdown. Right decisions could mean much more! Analyzing data related to your customers and prospects, you could extract relevant insights about your reference market. 

If you understand what your customers want, you can anticipate their needs, creating a dedicated offer. This could impact on the new product development process or on the creation of additional services for your product.

Digital assistant and chatbots

One of the main applications of Machine Learning nowadays is the development of digital assistants, called chatbots. In terms of business use, chatbots can be used to interact with your customers or employees, they can be used both in Marketing and Human Resources processes.

How PaperLit uses Machine Learning

PaperLit, tech company part of Datrix group, uses Machine Learning to help publishers to create content and reach new readers. PaperLit offers brands and publishers apps and solutions that integrate Artificial Intelligence proprietary algorithms. Some of the solutions are:

  • VoiceLit: distribution of content through smart speakers, that uses Machine Learning in the development of Natural Language Processing algorithms.
  • DataLit.AI, a platform that turns data intelligence into additional revenues. The platform uses Machine Learning in hyper-profiling and hyper-targeting customers, in order to reach the right customers in the right time and place.
  • MagPedia is a platform for the monetization of content. In this case, Machine Learning is used for breaking down and re-aggregating content, in order to capture attention of different reader clusters.

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Marco Belmondo

Marco Belmondo

Chief Marketing Officer at Datrix group (including PaperLit). Born in 1969 in Ivrea. Worked in Milan, Turin, Bologna, Rome and London. Debut in advertising (Saatchi & Saatchi, Italia Brand Group), followed by finance at TradingLab (UniCredit Group) as Head of Marketing Communication and Customer Service, then retail banking at UniCredit Banca and Banca di Roma as Director of Marketing Communication and e-Banking Services. He returned to investment banking at ABN Amro and RBS, then in fintech at Epic SIM.

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