Defined as a form or sub-category of artificial intelligence, machine learning first emerged in 1950 when the Turing test was created by British mathematician Alan Turing, who wanted to see whether a machine could simulate human thought. In practice, in the context of specific tasks, algorithms are trained to discover recurring patterns in large sets of data, learning to perform the task autonomously as they go along, thereby improving their performance.
The first so-called intelligent programmes saw light in 1952. Later, in 1959, American computer scientist Arthur Samuel first used the term machine learning to describe the program he had created seven years earlier: a checkers game that learns as you play. Up until 1960, artificial intelligence and machine learning had garnered a great deal of interest. However, the results did not live up to the promise of these technologies, which would lead to a gradual decline in popularity. It was not until 30 years later that machine learning regained momentum, thanks in no small part to the rise of the internet and the considerable increase in computer power.
Areas of application for machine learning
Machine learning is gradually moving from the scientific to the public sphere. This transition is facilitated by the invention of Deep Blue, the computer created by IBM, which is also a master at chess. While machine learning was initially often applied within the games industry, it is now being applied to many different sectors. It is proving particularly useful in medicine (e.g. image analysis systems), autonomous transportation, search engines and spam recognition in mailboxes, chatbots and digital voice assistants and many other sectors. In data science, machine learning is being used to develop predictive analysis algorithms which, when tested and applied to specific types of data, can be used to predict the future.
Machine learning within the content marketing sector
Today, machine learning is also playing an important role in the field of content marketing. It is proving significant in helping businesses collect user data which enables algorithms to determine the interests, emotions and behaviours of target audiences. This is enabling companies within the field of content marketing to get closer to their customers, whether current or prospective. This allows them to stimulate conversions, increase sales and boost their return on investment.
Sentiment analysis, a preferred method to boost engagement
While there are various types of machine learning used within content marketing, sentiment analysis is one of the most popular approaches used by companies. This involves gaining insights into the mindset of the audience reading the content created by the company, whether through text, ads or social media posts. As a result, companies are able to identify which types of content generate the most engagement, and are therefore able to focus more narrowly on creating those.
Predictive analytics or how to anticipate engagement 24/7
Another approach widely used by companies is that of predictive analysis. This process involves drawing information from a large data set to understand past trends and thereby predict future outcomes. In the field of content marketing, predictive analytics makes it possible to collect data around the clock, analysing when certain types of content are more likely to trigger customer conversions. As such, it is not only a question of using machine learning to understand what content is most relevant to the target audience, but also of producing the right content at the right time. While not perfect, predictive analytics makes it easier for companies to boost their efforts in content creation.
Written by Andrea Tarantini
