Interest analysis from Digital Media: how does it work?

Interests are fundamental components of any audience, whatever is the characteristic of the identified segment: stakeholders, brand lovers, or detractors.

They tell us how to engage people, how to talk to them and the topics we can use to get their attention. And every marketer knows how precious and scarce this resource can be nowadays.

As a qualitative, non-discrete object, interests used to be difficult information to obtain; fortunately, thanks to social media and the web, boasting a huge variety of data that can be used to infer interests.

Digital Consumer Intelligence tools such as KPI6 can do the trick for you – which means, they can extract interests from your user base and bring them to you in an easy, ready-to-read and customizable dashboard.

Let’s learn together how that can be accomplished.

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AI technologies offer a large suite of models we can use to improve our way of searching for a particular trend. Having a visual understanding of what a person talks about is a great advantage that can save us an enormous amount of time, especially if we are about to analyze not a single person, but a large group of people – an audience.

For example, we may want to understand what kind of people buy a particular product, or the followers of a particular influencer, or again we may want to launch a digital advertising campaign targeting users who see the contents of our Facebook page.

To help our customers, KPI6’s AI Researchers have developed an intelligent system that quickly eases the pain of navigating through more than one thousand interests in a smart way. 

We follow the standard set by IAB (Interactive Advertising Bureau), where interests are grouped in more than 20 main categories, each containing up to four levels of precision. We can show if an audience talks more about rock music – and so becoming a part of the class named | Art and Entertainment | Music | Rock Music.

Or maybe they prefer to travel, as we can suppose if they write about Disneyland very often.

We commonly make the assumption that if a person writes frequently about an entity such as “Cristiano Ronaldo”, we can very likely say that he often talks about soccer.

In other words, we can infer that an entity (Cristiano Ronaldo) is linked to an interest (Soccer). But how can we do that on an analytics platform? Manually writing all possible couples among entities and interests is impossible, since it would require too much effort.

How can we know that “Cristiano Ronaldo” is a football player, then?

We can, thanks to brand-new technologies. Think about what Google did with online researches: if we type “Cristiano Ronaldo” the research engine shows us other information outside of the usual web pages. A table appears, showing information like age, partner, children, and personal statistics, as well as salary and how many goals he has scored during the current season.

Google is not doing it by hand, and – you are going to believe that – nor even with a magic wand. The answer is actually pretty simple: the word “Cristiano Ronaldo” is matched to a single entry in a private big archive of data that stores all the information about the popular football player – the so-called “Knowledge Graph”.

We won’t bore you with technical details and we’ll try instead to keep things as simple as they should be. Imagine we have entities like cities, foods, people and so on. We also have qualities like dates, professions, ages.

A single entity contains links to some of those traits because a relation exists that links the entity and the quality. Relations can also link entities with other entities, but not qualities on other qualities.

Now we have some basic rules that will tell us that, if we find the entity “Cristiano Ronaldo” with the relation “profession” and quality “soccer”, we will know that the aforementioned entity is a football player.

But there’s a missing piece in the puzzle: we need a way to know if we are still talking about the “Cristiano Ronaldo” knowledge graph entity if we encounter words like “Cristiano Ronaldo”, “Ronaldo” or “CR7”.

When the user mentions an entity, we first recognize it in the text; only then we link it to an entity of the knowledge graph without NLP (Natural Language Processing) technology. When the prediction has been made, we extract all the relevant information we want to show in the chart. We organize all the information hierarchically, showing only the general interest classes like “Art and Entertainment” and “Automotive”.

It is the customer, according to her own needs, the one who will expand a category.

For example, in the Automotive category, we can see sub-interests like “Sports Car” on 80%, “Utility Cars” 15% and “SUV” 5%. Clearly, this particular audience likes to talk about luxury items; people belonging to it will probably have a high income, too.

When people ask us to enlist all the interests we can predict, we feel a bit troubled. We really don’t know! What we can do is generating a subset of the interest hierarchy. 

For example, imagine how many genres of music exist in the world. Imagine that a big database that stores all of them also exists – The Knowledge Graph – and that we can access that information with much less effort.

To do that, we match the name of the song to the related entity in the database, and look for the relation “genre”. If a hit is found, we stick to the genre “Rock Music”, and attach it to the prediction as a specific interest of “Music”.

We can predict more than one thousand interests this way, but we cannot predict them in advantage. Nevertheless, we do think that we have gained much more than we have lost.

Interest Detection works in a complex way, but it has been developed to make things simpler for our users and to turn that complexity into powerful, understandable insights to get the best profile of your audience.

Any company can make good use of well-classified interests, to know one or two key topics that can quickly and surely engage its audience.

Sometimes, interests are surprising and unpredictable: without a tool, you always risk your money in possibly unprofitable activities – you might be convinced that your customers love football, while they spend their Sundays playing golf… and your budget for TV spots or Digital Campaigns is gone!

As already explained in another article, the Digital Consumer Intelligence tools can provide different outputs from the classification of interests: one of these is Polygons, the first tool that compares clusters, transforming the interests of the audience into a single two-dimensional points, providing an index for their similarity.

An additional tool to better use the classification and detection of the interests of your audience, to reach your target and get social and digital performing campaigns.

Do you want to know why large enterprises prefer Digital Consumer Intelligence? 

Watch our Digital Consumer Adventures webinars