I'm passionate about discovering new insights, this is my main motivation. 

I work with data analysis, speciali any source for getting insights about the human behavior. Also a data scientist and machine learning researcher in training from FIA Business School. In the web, I'm at Github, Slideshare and LinkedIn and I also have blog (in portuguese) about data. Contact me to analyse quantitative and qualitative data.



Insight's projects



Dashboard Earned Media

Get Insights for real-time conversations about your brand universe and the competition.

Data Science reports

Machine learning algorithms for decision making with your internal database with information security. 

Brand competitive analysis

Get the best scenario for your company with external and internal data. 

Earned Media reports

In weekly and monthly formats, find the best ones for your strategic planning.

Survey Research

Collected from surveys and then statistically analyzed to draw meaningful research conclusions.

Social listening

Monitoring social media allows researchers to find insights into a brand's overall visibility on the internet.

Machine learning methods from Github (under construction)


Linear regression

In statistics, linear regression is a linear approach to modeling the relationship between a scalar response (or dependent variable) and one or more explanatory variables (or independent variables).  From Wikipedia.

Logistic regression

Logistic regression is a statistical model that in its basic form uses a logistic function to model a binary dependent variable, although many more complex extensions exist. In regression analysis, logistic regression (or logit regression) is estimating the parameters of a logistic model (a form of binaryregression).  From Wikipedia.

K-means clustering

k-means clustering is a method of vector quantization, originally from signal processing, that is popular for cluster analysis in data mining. k-means clustering aims to partition n observations into k clusters in which each observation belongs to the cluster with the nearest mean, serving as a prototype of the cluster. This results in a partitioning of the data space into Voronoi cells. From Wikipedia.

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A few insights

2015: More diversity in beauty brands

I discovered and pointed out the requests for more "diversity" in beauty brands in the most diverse social networks, before even the buzz demand and burst in 2016.

2016: Dona Benta

I pointed out and discovered the "boleiras", enterprising mothers or fathers who used the Instagram to sell cakes, this at the height of the recession in 1TRI 2016

2016: Acer Brasil

I discovered and pointed out the need for notebook brands to talk to "casual gamers" and "mobility", people who wanted laptops with dedicated graphic cards, using the notebook as a multimedia device. This guided new ways of generating products that cater to the gamer;

2018: L'Oréal Brasil

In a lecture published the study and data pointing to the k-pop universe associated with beauty brands, this at an international level, with high dialogue with adolescents. In this same, the strand of women who do not use shampoo to wash hair and its behaviors and data indicating solutions of new productions.

Check my last post

My blog (in portuguese) about data


TedxSão Paulo

Theme: communication with purpose

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LinkedIn Top Voices

LinkedIn highlight in 2016

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Market research, business intelligence and content about consumer behavior

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