How machine learning powers personalized video

Summary

Personalized videos are an essential tool for those who want to market effectively, as they allow you to establish a more direct and trusting relationship with the customer. After all, consumers are no longer the same as they used to be, but are looking for something different from companies. This is why it is so important to surprise them and above all why concrete actions are effective. In this sense, we understand the great investment in CSR and the need to imagine a new form of communication that exploits innovative tools such as personalized videos and machine learning.

The power of personalization

Personalization is one of the most important trends and one that has become even more important during this period of the pandemic that has forced us to rethink the relationships between people.

In fact, during the global crisis caused by COVID-19, companies played a fundamental role in making their support and presence felt by people who often found themselves isolated for long periods of time.

In addition to messages of closeness and motivation shared on social media, many companies have undertaken concrete operations to make their contribution in times of need. Armani was perhaps one of the first to make a concrete commitment to the cause.

With a gesture of great responsibility, the Milanese fashion designer converted part of his supply chain to produce disposable scrubs for workers on the front lines in the fight against the virus, giving an important message: Everyone is called upon to do their part, in every way (ilsole24ore.com).

It’s no coincidence that other companies – Gucci, Herno, Intesa Sanpaolo – have followed suit, contributing to the situation in their own way.

But what does this have to do with personalization?

Actually, it has a lot to do with it. The two are definitely connected, since they both fit into the same approach that has been demanded by consumers for some time now.

In fact, companies are no longer seen as simple suppliers of objects or services: they have their own identity, their own character, a tone of voice (this has almost always been the case) and, for some time now, they must also have a position.

This is not new. For some time now, we have been stressing the importance of brands to communicate their position on issues that are important to consumers (the environment, human rights, workers’ rights, etc.) (chiefmarketer.com).

Moreover, the best marketing and branding strategies increasingly include one or more Corporate Social Responsibility operations, “which translates into the adoption of a corporate policy that is able to harmonize economic objectives with the social and environmental objectives of the reference territory, with a view to sustainability, i.e. with the intention of preserving the environmental, social, and human heritage for current and future generations”.

And why is this so?

Because consumers want to live in a more equitable and sustainable world, they want to have a lifestyle that is consistent with these values and needs.

All of this is reflected in a field of choice even when buying: no one is willing to approach a company that goes in the opposite direction of how the individual behaves.

Acquiring something is no longer just about possessing it.

Buying has become, now more than ever, a social action, which can have a concrete impact on everything around us.

But if buying is no longer simply an exchange of objects or services for currency, it means that when each person buys, he or she is looking for something more than the trivial satisfaction of a functional need.

On the one hand, as we have seen, the reflection of one’s own “life choice,” on the other, a real experience.

The same is true when it comes to communication.

It is no longer functional messages, those that leverage immediate and basic advantages or benefits that attract consumers.

People are looking for something else, they are looking for something that strikes them, that surprises them, a type of story that is no longer product-centric but customer centric.

In this sense, there is a need for communication that is as personalized as possible, that is tailored exactly and perfectly to the needs and characteristics of consumers.

But what exactly does it mean to personalize?

Actually, it’s easy to talk about personalization, but what does it really mean?

The first thing you can customize is the content, which is the message you want to get across to the consumer.

This is undoubtedly the first hook to get people’s attention.

However, it’s not enough to just call them by name, as is done in email marketing, because gimmicks of this type no longer have the surprising power needed to really appeal to users.

That’s why personalization is increasingly being applied to videos, which are widely recognized as the most effective format when it comes to making a truly impactful communication, especially on the web and on social media.

It is no coincidence that platforms such as Babelee are becoming increasingly common, allowing companies to create perfectly personalized videos quickly (from the graphic component to the audio features and music) without the need to involve the IT department or without needing to develop graphics and editing skills in order to create high-impact content.

Another aspect that can (and must) be personalized is the moment of reception.

Each user, in fact, is not only sensitive to a certain theme rather than another, but has different interests and needs at different times, since each consumer goes through his own customer journey with specific timing (surveymonkey.com).

If a user is a loyal customer, he may need to receive constant stimuli to start buying again or to pay attention to the brand’s activities.

On the other hand, a newly acquired customer needs other types of content that will make him curious and that will take advantage of the classic “honeymoon” period that the customer has with the brand.

So, different users, different moments, and different messages to pursue different goals.

But even in this case, the answer lies in the use of technology and digital platforms such as Babelee, which makes it possible to cross-reference data and statistics in the company’s database relating to individual users in order to define the type of video to send and the best timing for its delivery.

Leveraging video automation, these processes of analytics, video creation, content definition, and delivery scheduling happen smoothly and automatically, simply based on the source data and any data that may be obtained from subsequent content consumption.

A new protagonist of personalization: Machine learning

From the above, one thing is clear: the role of video automation platforms like Babelee plays a fundamental role when it comes to creating personalized videos and, in general, impactful and highly targeted content.

Machine learning can greatly enhance the effectiveness of this type of content, intervening in some specific and delicate phases of the entire process of creation, distribution, and analysis of personalized videos.

Before delving into this, however, it will be useful to define “machine learning.”

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.”

Machine learning is not the same thing as Artificial Intelligence.

They are two very different things, as machine learning can be considered a subset of Artificial Intelligence.

We can even say that machine learning is an application of AI, which exploits specific algorithms to make it possible for machines to assimilate more or less complex information, decipher it making it intelligible, and then learn from it, thus progressively eliminating the need for direct instructions given by human operators.

This kind of technology is very versatile (as it has been widely explained in our previous post [insert link]) and is at the base of some digital platforms such as the already mentioned Babelee, which, because they exploit these solutions, are essential tools that companies can use for implementing a relevant marketing strategy that places the customer at the center of a memorable and high-quality experience.

How does machine learning make a personalized video more effective? 4 benefits

First benefit: More data, more accurate, more useful

It may seem trivial, but it’s worth clarifying: machine learning is based on data.

Regardless of the type of algorithm on which machine learning is built (there are, in fact, different types of machine learning depending on how they are “activated” and the role that human intervention has – ai4business.it), data is the fuel that makes the system work.

More: data is the raw material with which machines learn, work, and perfect.

From this point of view, therefore, implementing a machine learning system means making the most of your data, since it is systematized and made more intelligible.

Not only that.

Machine learning also allows you to enhance the collection of data, making it more systematic, organized, and, above all, useful for a subsequent phase: if the initial data is used to create the first content, those that are collected after the initial distribution will be used to refine the insights on your audience even more.

Depending on the results, the system will use analysis to learn which features of the personalized video are successful and which ones need to be modified, and it will act accordingly.

In a metaphor: if data is a company’s capital, machine learning is a way to use it and make it pay off as much as possible, setting in motion a true virtuous circle.

Second benefit: personalization rhymes with prediction

Learning from data is an aspect of machine learning that should not be underestimated at all because it allows companies to do something that, in strategic terms, is always a winner: to predict the effectiveness of your video content (cabotsolutions.com).

In this sense, employing machine learning in the production of personalized videos provides real assistance in building a good content marketing strategy.

In fact, by taking advantage of machine learning, you can identify the combinations of visual, textual, and audio elements that are most likely to attract the user’s attention and push them to perform certain actions.

Among other things, it is not only the capacity for combination that plays a role, but also the possibility that platforms of this type have of “fishing” quickly from a very large archive of materials, images, and frames that would sometimes be difficult if not impossible to retrieve by working manually.

In addition, each forecast can always be subsequently verified and, as mentioned above, improved in the light of data obtained from the analysis of user reactions.

Third benefit: not only effectiveness but also efficiency

This advantage was already anticipated.

The use of machine learning, and in general of all marketing automation systems, makes it possible to speed up and automate processes, thus saving a lot of time.

Indeed, time savings is possible because you no longer need to involve different teams in intermediate steps (e.g. with IT or the graphics team) before publishing the finished product.

This means that costs related to the use of human resources that are not necessary and can therefore be employed otherwise, are also significantly reduced.

For example, with Babelee, operators do not need technical skills to create highly personalized and impressive video content. This is why we can say that such a platform makes a communication vehicle that would otherwise require large investments and ad hoc investment accessible to every brand or company.

Fourth benefit: the level of customer experience quality

One aspect of machine learning that is often overlooked is that its use allows companies to increase the quality of the experience offered to the user.

If it is true that every system learns about users and their tastes, this means that as time passes, it is able to create content that is much more than just a personalized video: It is a true customer experience that “resembles” the customer more and more.

In this way, each person who receives the video has the impression that he is watching a handcrafted product, designed and made especially for him.

This, of course, not only makes the content more effective, but also improves how customers perceive the company, which is seen as attentive and receptive to the needs of individuals.

In essence, it becomes a subject that merits trust, both during its consumption and in the purchase phase: all of this is thanks to a personalized video through machine learning, who would have ever thought it?