Machines think and learn in a remarkable way. Though it may intimidate us sometimes, artificial intelligence offers plenty of scope for improving the efficiency of all business departments, none more so than marketing.
In this article you will discover why - particularly when faced with lots of content - machine learning is so valuable for content marketing. But not before our usual little historical titbits, of course.
Contents:
- Machine learning in less than 100 words
- Alan Turing and the intuition that introduced us to “machines that think”
- Data is to machine learning as experience is to people
- Beyond data: machine learning applied to content
- Why does content marketing need machine learning?
- Automatic learning applied to content marketing (using a DAM Platform)
Machine learning in less than 100 words
Machine learning is a branch of artificial intelligence.
If we had to explain machine learning to a child we could define it as “a computer that learns on its own”. A slightly simplistic definition perhaps but one which conveys the idea quite well.
In fact, this technology, enables machines to learn even if they haven’t been programmed to do so. On the basis of data supplied by humans, they are able to learn computer algorithms on their own.
There are lots of different applications of machine learning. For example, understanding the meaning of a text, providing personalised recommendations to internet users or classifying a set of content.
Alan Turing and the intuition that introduced us to “machines that think”
“Can machines think?”
This is the question with which Alan Turing begins “Computing Machinery and Intelligence”, one of his most famous scientific papers and, key foundational work on Artificial Intelligence as we know it today.
An incredibly important figure, both historically and in the present day, Turing used this document to share his visionary approach.
One of the key challenges facing the first computers of this era was that of superseding the purely mechanical approach to replicate human - and therefore very complex - patterns and ways of thinking.
However, thanks to the parallel progress also being made in neurology and cybernetics, Turing understood that the adult human mind develops through evolution and constant learning.
For this reason, a computer should emulate the brain of a child which, after learning something, replicates the reasoning of an adult.
In his paper, the academic also introduces “the imitation game”, a method of establishing just how intelligent a machine is.
Known as the Turing Test, the game involves a human evaluator who dialogues with a human and a machine via written messages and has to decide which is the computer and which the human. If the evaluator is unable to tell the machine from the human then the machine has passed the test.
Data is to machine learning as experience is to people
We can define the information made available to the algorithms as the experience that enables people to grow. In fact, machines can only learn if they have lots of data to work with, enabling IT systems to develop abilities similar to the cognitive capacities of humans.
At the same time, the presence of a multitude of data also necessitates the recourse to this kind of solution.
For example, machine learning is used by search engines to interpret the searches of users and propose results in line with their expectations. The omnipresent chatboxes which many organisations use to provide an automated first level of support to their customers are based on similar logic.
Beyond data: machine learning applied to content
However, machine learning doesn’t only feed on data.
The continuous growth of digital communications, of all kinds, also contributes to generating a huge volume of content. Photos, videos, audio, documents: it is impossible to put a number on the amount of digital files with which we come into contact on a daily basis.
The homepages of websites, e-commerce sites and social media platforms are packed with content designed to inform, to entertain or to sell.
The volume of content in circulation – managed in the most organised companies using Digital Asset Management solutions - extends the applicability of machine learning. In fact, these technologies have a vast base of files from which to learn, for example, how to recognise and classify content.
So, just like data, content is increasingly grist for the machine learning mill.
With great benefits for marketing.
Why does content marketing need machine learning?
Every day, marketing departments, and digital marketing departments in particular, produce enormous amounts of content. This is part of daily life in big businesses, like those in the fashion or design sectors for example.
In these organisations, in addition to creative briefs and the definition of concepts, before publishing on channels like websites and e-commerce platforms marketers must also manage related product information.
Some do so manually, recovering this information from the PIM or ERP system and loading it onto the CMS, e-commerce platform or marketplace. Others integrate these systems.
In both cases, it is evident that product information and content are not correlated but simply share a common space on the webpage or the e-shop page on which they will appear.
Yet the association between content and product information is a crucial part of any quality content marketing strategy. Why?
Because only content accompanied by a suitable amount of product information can ensure the best possible user experience on communication channels. With positive consequences in terms of sales volumes, the e-commerce shopping cart abandonment rate, and customer loyalty.
Thanks to content-product association, as well as identifying the user’s favourite content, the content analytics on the DAM Platform, make it possible to trace and monitor the actions of every user in terms of the products with which they have interacted.
In this way it is possible to construct an interest profile and propose users content and products tailored to their own personal preferences.
Simple, right?
Actually no.
Most companies still separate the management of content, usually entrusted to a DAM solution, from that of product information, typically managed with a PIM system.
Getting different systems to interact with each other isn’t always easy and can result in not inconsiderable integration and silo costs. For this reason, the best DAM Platforms make it possible to natively manage the information related to the product that every piece of content represents.
However, manually associating product information with content is a very time-consuming activity.
When there is a lot of content to manage, too much, it is therefore important to go the extra mile.
And this is where machine learning comes in.
Automatic learning applied to content marketing (using a DAM Platform)
Machine learning is a valuable source of support for those who manage large volumes of content with a DAM Platform. Especially those that want to manage it effectively.
In fact, automatic learning technologies, on a DAM Platform, make it possible to classify files as images, videos and audio, streamlining the activities of content marketers.
In particular, thanks to semantic analysis, they are able to recognise and interpret the characteristics of all types of content, automatically associating it with tags. For example, they make it possible to tag all content on the basis of the elements that it represents if it is an image or a video, about which it talks if it is audio content or which it mentions in the case of documents.
In addition, in DAM Platforms that support the integrated management of content and product information, the potential efficiency of machine learning is amplified because content and product are associated automatically.
In these cases, machine learning generates time savings as well as other major business advantages.
- Firstly because the automatic tagging of all content with associated marketing characteristics and product information enables marketers to save time and energy.
- This also results in major advantages for customers who, as mentioned, can enjoy an optimum user experience on the digital channels.
- And that’s not all. As well as streamlining the process of associating content with product, marketers can also save time when searching for the content they need on their platform.
In short, the application of machine learning to content marketing can bring operational benefits while also improving the buyer journey of your customers. To the joy of your ROI… and your business in general.
While machine learning applied to content marketing may be regarded as an interesting option for small and medium-sized enterprises, it is absolutely essential in larger, more structured organisations.
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