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What is Machine Learning and why is it relevant in digital marketing

Machine Learning is the science that aims for computers to learn and act as humans do, and thus improve their learning over time autonomously, by providing them with real-world data and information.

For some authors, Machine Learning and Artificial Intelligence (AI) share the same concept, however, there are some differences that we must all recognize.

What is Machine Learning?

According to the University of Washington, “Machine Learning algorithms can figure out how to perform important tasks by generalizing from examples.”

And according to Forbes, machine learning is already revolutionizing the marketing landscape, with 84% of large marketing organizations starting to implement or expand the use of AI in 2019.

And these figures translate into results, as 3 out of 4 organizations that use Machine Learning in their operations see an increase in sales of more than 10%.

Thus, once its operation and main results have been identified, it is worth clarifying that Machine Learning is an AI system, only that this method is specifically focused on developing cognitive functions, emulating and taking advantage of human knowledge.

And in the Marketing sector, its results derive from using these systems to generate more “intelligent” tools that allow easy interaction with humans and foster customer-brand relationships.

Big Data and Machine Learning

Machine Learning or Automatic Learning is a term already common among technology enthusiasts, and in recent years it has begun to be related to Big Data, due to the factors that involve both systems.

Big data is known as the process in which we collect and analyze the large volume of data or information, which helps us discover hidden patterns, such as customer habits, market trends, or information from our leads, which It is really beneficial for organizations operated under the Inbound methodology and oriented towards customer satisfaction.

This is where Big data is responsible for providing information that feeds Machine Learning systems, processing the different types of data at the speed necessary for machines to predict future actions without the intervention of human beings.

Therefore, it could be said that Big Data helps Machine Learning applications improve their accuracy in predicting and analyzing results.

Machine learning application environment

An important area that is applying the new Machine Learning technologies is Digital Marketing. And it has become a trend to work with professionals who are experts in the latest AI tools and trends to implement programmatic advertising.

Programmatic advertising is the automated purchase of digital advertising space, using computer algorithms. In the past, buying media was a mostly manual process. Countless hours of bidding and sorting through media inventory were required to find the best advertising opportunities.

On the other hand, Content Marketing is another environment where Digital Marketing specialists can make use of Machine Learning to optimize their results, by producing more effective content aimed at your target audience.

A plethora of tools utilizing machine learning to varying degrees have made social media automation accessible to most organizations.

For example, automated tools with machine learning and some CRM systems can perform most social media activities, like following accounts and sharing content, using machine learning to automatically create shareable posts that feature your favorites. products or services in the various digital channels.

This is how this segment of Artificial Intelligence development works autonomously, and generates results from the information provided, while taking into account the behavior of your potential customers.

conclusion

In terms of purpose, machine learning is not an end or a solution in itself. Also, trying to use it as a general solution is not helpful at all.

When a learning algorithm fails, often the fastest path to success is to feed the machine more data, which will deepen the learning. However, this can lead to scalability issues, where we have more data, but without time to learn, data is still an issue.

To implement strategies driven by Machine Learning, we must from the beginning be aware of the objectives we want to achieve and how learning will be reinforced through information.

Having specialized AI tools in this sector is only the first step to generating aligned tactics over time that generate effective results.

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