The value of Machine Learning and analytics for video

Summary

Content marketing is one of the most popular and widely used marketing approaches in recent years. Within content marketing, video is undoubtedly the main format to be used as it guarantees excellent results and aligns with users’ digital behaviors. However, video also has its flaws: it is not very engaging and is often “serial.” Machine learning, in this sense, is a valuable resource because it allows you to highly customize content by combining and learning from the analytics that the company has available. Among other things, machine learning can also go so far as to completely autonomously outline video content to use, which can maximize the effectiveness of your marketing strategy.

Content marketing for effective communications

Making content relevant is key when it comes to having to build effective communication on the web and social platforms.

This requires a great deal of attention and work, which is not taken for granted, but rather is built step by step through precise phases and with a strategic and rational approach.

From this point of view, it is no coincidence that we have been talking about content marketing for a long time. Content marketing “is the branch of marketing that includes the set of techniques for creating and sharing relevant and valuable textual and visual content around a brand or product/service, so as to create an engagement that can also guide the purchase process of users.” (insidemarketing.it).

We have realized that in order to communicate and be present on the web in a certain way, it is necessary to have a strategy and content is the perfect tool to reach a user, as long as it is “valuable,” which means that it’s relevant for those who use them and that they are used strategically.

This means that it makes no sense to produce videos, articles, images, posts if they are not included within an organic, rational, and well-constructed strategy.

Also because every piece of content is different and is effective for doing different things and pursuing different goals.

So, if you want to work on engagement, videos are certainly a good option For conversion, special written content such as whitepapers and ebooks can also work, allowing you to generate quality and well-selected leads; for awareness, even a single static image can be effective for getting noticed on websites and social networks.

One format above all: video

Net of that, though, there’s an acknowledgment that a certain type of content seems to work much more than others for achieving your strategy goals.

We’re talking about video.

It can be said that the web loves videos, especially because the users love them, as studies and reports on the topic have shown.

Even recently, the report by We Are Social in collaboration with Hootsuite concerning the trends and the evolution of the Italian and global digital landscape underlined this aspect, showing how a large part of the time on social media is spent watching video content.

Similarly, many predictions made about current and future trends confirm that the production and consumption of video content on and for the web will likely increase in 2021 as well as in the years to come.

Actually, this is not very surprising.

As a format, video has undoubtedly some very interesting strengths that fit well with the mechanisms of fruition imposed by the web and social networks.

Videos, for example, are able to quickly attract the user’s attention through graphics, impressive montages, and particular texts. Considering the speed with which an average person scrolls through their feed, the video is undoubtedly valuable because it is able to reach the audience.

Still on the subject of attention: a video can effectively convey a message, even in a few seconds, interrupting the user’s navigation; they may even stop to watch it until the end.

Moreover, a video can contain a lot of information and convey it in a relatively short period of time.

Not only that.

A video makes this information easy to use since it requires very little effort from any user who wants to watch it, since there is no need to read or pay excessive attention.

The action required is essentially passive; if anything, a specific call to action may be required at a later time.

The best format can still be improved

In light of this, it seems clear that video is the winning format for good content marketing – notwithstanding the strategic considerations that may make it more or less suitable for what you want to pursue and the results you want to achieve.

This, however, does not mean that the best format cannot be improved in some way, so as to further increase its benefits.

However, video does have some weak aspects that should be considered.

For example, it is passive content that keeps the user’s attention, but never actively involves him.

Second, by definition, video relies on a broadcasting distribution dynamic, a dynamic of one to many.

The same video, produced by a single company, is viewed the same, serially, by many, many people.

This means two things. First of all, there are no differentiating elements to attract the attention of the user, who (almost) never really feels called upon.

Second, not being able to differentiate the content, the same video must “fit” many categories of people that are very different from each other, by age, interests, cultural level, and so on.

But by doing so, the message will surely be dispersed, since not all users reached will be interested in the same way.

A waste of resources, if you look closely.

What would be needed, therefore, would be a technological solution capable of overcoming this problem, providing the necessary tools to personalize this content, perhaps even cross-referencing data and statistics related to the use of content.

Fortunately, from this point of view, there are some technological solutions that can help anyone who wants to improve the performance of their videos, making them more relevant, effective, and performing.

One of these solutions is machine learning.

What is machine learning?

What do we mean when we talk about machine learning?

We should not think of futuristic technologies, since their use is more widespread than we think, to the point that we often encounter and interact with them without even realizing it.

Let’s start with a definition.

Machine learning refers to “a subset of artificial intelligence (AI) that deals with creating systems that learn or improve performance based on the data they use.” (oracle.com).

On closer inspection, then, machine learning is not really a synonym of artificial intelligence, since everything related to machine learning is part of artificial intelligence, while artificial intelligence does not only include machine learning.

The fascinating thing about machine learning is that it somehow mimics human intelligence or at least a part of it, the part related to learning, which is made possible by specific algorithms.

There are two main types of machine learning algorithms currently used: supervised and unsupervised machine learning algorithms. Depending on which of the two algorithms is exploited, the machine learns the data differently to make predictions.

Specifically, with a supervised machine learning algorithm, a data scientist must act as a guide and teach the algorithm which results to generate.

Learning, therefore, has a human starting component, which initiates the recognition and systematic collection of data: in supervised learning, the algorithm is trained from a dataset that has already been previously labeled and has a predefined output.

In the context of unsupervised machine learning, however, the approach that is used is more “independent” since the computer learns to identify complex processes and patterns without the guidance of a technician directing the learning.

This means that in unsupervised machine learning, the source data on which the training is based does not have labels or a specific and defined output.

Obviously, the two approaches lend themselves to being used in different situations, depending on how much data you need to process and what kind of tasks you want the machine to perform.

How can machine learning be used?

Machine learning is an extremely versatile solution that can be used to cover a wide variety of applications.

For example, it is very useful when you need to provide a customer with personalized service: here, machine learning is leveraged to ensure voice recognition of digital assistants or to enable autonomous driving of vehicles or more simply to formulate search suggestions on Google, Amazon, or Netflix.

Obviously, machine learning is also used to bring added value to the business, particularly in the area of marketing since it is useful for profiling the customer base, personalizing the offer, and decreasing the abandonment rate.

Through machine learning, in fact, it is possible to manage and organize a large amount of data and statistics, making them intelligible, but also exploiting them to profile the customer base and organize it into small, precise, and dynamic groups, depending on certain factors (for example, personal and demographic characteristics, preferences, interactions with the brand, and so on).

Starting from this profiling, machine learning can then be used to hyper-personalized one’s marketing strategy through the launch of targeted promotional campaigns, built according to the characteristics of the consumer one wants to approach.

For example, you can differentiate the timing of content delivery according to the position of the subject within the customer journey, or you can modify the content to be sent.

Video and machine learning: A combination to improve customer satisfaction

This last aspect is undoubtedly relevant when it comes to video: Big Data in combination with machine learning becomes a fully exploitable resource to build your content in a personalized way, so that it is as relevant as possible for those who receive it.

In this sense, machine learning and Big Data analytics are a key resource. By knowing and learning more and more about your target customer base, you can create videos that are more likely not to be skipped and instead, watched all the way through.

This is already very common in other areas. For example, think about email marketing platforms where messages and newsletters are programmed with specific targets, and user preferences are also tracked so as to make recipient segmentation increasingly precise.

Or to the personalization of websites: very often when a user visits a site, through machine learning it is possible to record movements in such a way as to be able to present him, at a second access, a better and more functional navigation path regarding his behaviors.

Applying the same technology to videos, the result can be really surprising.

Starting from the analytics present in your databases, you can create highly personalized videos with the help of machine learning, varying the message and the type of content according to the personal characteristics of those to whom the content is addressed.

Not only that.

Machine learning also allows you to learn from these videos by analyzing user performance and reactions: did they watch it? For how long? How many reached the end of the video?

Depending on the “user response,” machine learning systems can learn what parts of the content need to be improved by analyzing the very analytics data that the content itself has generated.

Again, leveraging machine learning, you can use the mechanics of AB testing to verify the effectiveness of a video by personalizing it differently for two groups of users with similar characteristics.

This is not to mention the contributions that machine learning can provide in the post-production phase, identifying, for example, the best combinations of images or the most effective durations to achieve a given result.

All of this is aimed at creating a memorable video for the user, with a high added value and providing the company with an effective and highly traceable marketing tool that can be realized in a simple way, without the need for excessive technical skills or a great expenditure of resources both in terms of manpower and time.

From analytics, to personalized video to new, even better-personalized video that collects new, more accurate data every time: this is the virtuous circle that machine learning sets in motion.