Our workflow is divided in three phases:
We listen to those players with awareness of business needs, and then tell them what we’re going to collect using:
Not just cloud data engineers doing cloud connections via APIs, we can also do surveys, first party responses, focus group feedback, and other business needs we listen to from the company and from the marketplace.
We combine these two with our deep data analysis for two very deep approaches.
Now you have the data in one place. How to manipulate it to answer business questions? By determining which information is relevant and how to merge different sets of data by moving, cleaning up and classifying it, we identify that which will help you with your business needs and we stitch it back together in a single repository.
Next, we determine what kind of data analysis is appropriate to answer your business questions: statistics, regression analysis, clusters of data. The what way you analyze data tells you something about the information you seek. We visualiza and build models for this analysis with a data science and statistics approach.
The visualization of information is one of the outputs of our analysis. We achieve this thanks to advanced reporting and data visualization tools such as Data Studio. (Generic example).
As we organize and convert data into useful sets of information, we use it to take action and enable automation tools to better achieve your business goals, such as marketing campaign customization and website optimization models. The more data the models have to work with, the more accurate the product of this activation process.