Experiment #206 Brand Presence – The analysis


The sources from which we are going to extract the information are totally heterogeneous, which forces us to carry out a set of techniques and scripts to capture the data that we need. In this case, we decided to use Python programming language, which offers a suite of tools capable of executing this heavy task.

For different social networks we have at our disposal the corresponding API to obtain the desired information (publications, likes, visits, etc.). And, both from Twitch and the different television channels, we have direct access to the signal.

Later and thanks to artificial intelligence, we will be able to detect if the brand is present, like in the next images:

using AI to detect brands from a tennis match

using AI to detect brands in a football match


Once the information has been collected, different services will be in charge of detecting the presence of the brands in each image. Apart from customized models that will improve and enrich our system, we will use Computer Vision, which is one of Azure’s Cognitive Services:


How the computer service works


Analysis of the information

Once we have the architecture assembled and the data has been processed by the Cognitive Services, the next step is extracting information from the pre-processed data to make it available to the analysis departments, which will use it for decision making.

To tackle this last part, we have decided to make use of a great ally such as Power BI.

Understanding the information

Next we will explain how the Power BI’s information is structured.

At the top we find the sources:

And within each channel we can find different categories, like:

In Social networks:


“Impact” is the effect caused by each of the elements. For example, the impact on a football broadcast would be calculated as the product between the seconds that a brand appears times the number of spectators. However, in a social media post the impact will be equal to its reached viewers.

The “impact” metric is something totally customizable, and must be calculated as the experts in the
business dictates, because each company behaves in a different way.

Let’s start with the report. In it, the different elements will appear numbered to facilitate their
reading and monitoring.

Global metrics – tab

Global metrics in PowerBI

  1. The percentage of impact of each social network. We can see how Instagram and Facebook predominate, the first being the winner.
  2. Percentage of impact by subtype. In this case, we can see how eSports and entertainment stand out from the rest.
  3. Regarding television channels, Cuatro and La Sexta are the superiors.
  4. There is no doubt about the impact that Twitch is gaining. That is why we thought it would be interesting to compare it with the impact of both social networks and the TV. In this case, we can see how Twitch could practically be put at the same level than the other two, so the investment made in this platform is being truly effective.
  5. Finally, in terms of general impact by source (without breaking down by subtype), Twitch would be the one performing most satisfactorily.

Social media – tab

In this tab we are going to enter into a study between the different social networks:

Social Media tab in Power BI

  1. Number of total publications vs the target set. We set a goal of 1000 and we are 67 publications above.
  2. Although the number of publications has been exceeded, we have not received as many “likes” as expected.
  3. However, our custom metric that relates both “likes” and visits to publications tell us that we have reached a lot more people than expected, so the objectives are being achieved.
  4. In this chart we can see the impact of our publications by date. February was clearly the busiest month.
  5. One of the studies to be carried out is when (time) our publications have the greatest impact. Thanks to this visual we can see how from 8 to 9 and from 13 to 17 is when we have a greater impact.
  6. Here we can see the impact of each of the social networks. In addition, it also works as a filter.
  7. If we want to know which person in the publication has the greatest impact, this chart will tell it. Here Cristiano Ronaldo and Lionel Messi meet tied at the top of the list. However, Iker Casillas seems to be the one with that least impact creates.
  8. We also have at our disposal a couple of filters to filter by “Service”, this means, by a social network account in particular or by a particular person.
  9. Finally, we can see the number of publications by type (image vs video).

If we want to filter by a specific account (FC Barcelona, for example) this would be what we would see:

Filtered metrics in PowerBI FC Barcelona


And here an example of Real Madrid CF:

Filtered metrics in Power BI real madrid

As the analysis shows us that the posts with Cristiano Ronaldo or Lionel Messi have most impact, the decision-makers interest would be in having these two appearing in most of the posts in order to generate even more impact. And as the publications on the FC Barcelona account work very well between 8 and 9, the account should try to publish at that specific time, or between 1 and 3 o’clock, which is a good time to publish for both accounts.

Twitch – tab

As we have seen before, Twitch is one of the leading platforms at the moment. And to many, the new television.

metrics for Twitch in Power BI


  1. Personalized impact vs the objective set.
  2. Seconds that appear on the screen vs the target.
  3. Number of people we have reached vs the target.
  4. When the post has had most impoct. In social networks, it is crucial to know the best time slot to publish, so this chart is very important.
  5. Impact per person. In addition, it also works as a filter. We can see how Ibai is the leader, followed by AuronPlay.
  6. The impact by date.

“TV Channel” tab

This tab shows the analysis of different television channels and programs:

TV channel example in PowerBI

  1. As in the previous tabs, here we can see the customized impact vs the objective.
  2. Total seconds vs the goal.
  3. People we have reached vs the objective.
  4. Time period in which the most impact has been achieved, this being mainly between 6:00 p.m. and 3:00 a.m.
  5. Impact by service. As we can see, the ads have greater impact than a football game. But, we have to keep in mind, that the costs for appearing in an add are different for the costs to appear in a stadium or on the players’ shirt, and that, in this case, we should also elaborate metrics such as impact vs. invested money.
  6. The impact in different TV channels.
  7. Impact per person who appears on the screen with the brand. This info can be used in future ads and choose one person over another to appear in the ads, in order to achieve a greater impact.


Read more of Experiment #206 here.

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