Google Analytics Data Dashboard on Google Merchandise Store
Hi! I’m Douglas and I’m a Data Science enthusiast trying to learn everything I think I need to become a Data Scientist.
As I’ve explained in my first Power BI post, First Steps on Power BI and Car Sales Dashboard, I’m doing a course on Power BI basics by a Brazilian (and thus Portuguese speaking) company Data Science Academy. The ninth chapter of the course gives a brief introduction to Google Analytics. Since the course at no point teaches how to build a site and most students (like myself) don’t own one, it doesn’t go too much into enabling Google Analytics on the site or the native Dashboard it creates. Rather it gives us a dataset provided as an example by Google itself about their Merchandise Store to play and experiment with. The data is of March, April, and May of 2017, but I don’t know if this was a selection made by the course or by Google. Nevertheless, it has 39 columns of information.
As has been for a while, the course gave us a list of questions that must be answered in the creation of the dashboard. They were:
- Do the clients access the portal mostly through Organic Search or Paid Search?
- How much time, on average, does a visitor stay on the portal for each day of the month?
- What is the main source of access to the portal?
- What operating system is mostly used to access the portal?
- What device is mostly used to access the portal?
- What is the total revenue by day?
- How is the Average Pageviews doing as a KPI?
The Dashboard I used to answer these questions was:
I will show and explain how each and all of those questions has been answered in these charts.
(1) Do the clients access the portal mostly through Organic Search or Paid Search?
Yes, I know a pie or donut char would have been perfect to answer this question, but at the time I created this 100% Stacked Bar Chart I was thinking of using those for another question (what I didn’t) and I always wanted to build a chart like this. Furthermore, the course made a chart with every form of access, but I think as the question only mentions Organic and Paid searches it would be more fitting to only show those two.
(2) How much time, on average, does a visitor stay on the portal for each day of the month? and (6) What is the total revenue by day?
I always like to try and merge every chart with time as the X-Axis into a single one (if it doesn’t get too polluted). I did it here as it is still pretty clear what each part of the chart represents.
(3) What is the main source of access to the portal?
The only work I had when doing this was choosing how to color every column so that it looked good in the final Dashboard.
(4) What operating system is mostly used to access the portal? and (5) What device is mostly used to access the portal?
I wanted to also join these two questions in a single chart because I believe seeing which OS is being used in which device, even if not profitable, is interesting information to have. For example, we can have an idea here of how little people used Windows on their phones in 2017.
(7) How is the Average Pageviews doing as a KPI?
The teacher actually gave a bit more information on this and said that the KPI should be the Average Pageviews and the Goal should be the maximum Pageviews in a Day on the dataset.
Mobile Layout
This was not requested by the course, but I believe it is and will always be good practice for me to create the mobile layout of every dashboard I make. It doesn't take more than 5 minutes and, in the future, maybe a fantastic feature to have on my dashboards. This is how the Dashboard has fared in Mobile Layout :
In any case, this is my Dashboard with Google Analytics Data from Google Merchandise Store as guided by Data Science Academy’s Power BI free introduction course. I can’t publish the dashboard (at least just yet) but the dashboard file will be available for download on my GitHub here alongside the dataset provided by the course I used. I hope this helped you in any way but really, as I always say, even if it didn’t help you, it helped me tremendously so it’s worth it. See you soon!
