How to use Google Data Studio to track your brand’s performance
Who knows your cookie brand the best? A 40-year-old man who lives in Paris and likes eating cookies, or a 23-year-old woman who lives in Berlin and does not like eating cookies?
Thanks to Google Data Studio (GDS), you can now combine the different data variables you have collected in your brand awareness survey, and discover who is most aware of your brand.
In this article, we will use GDS to unlock insights for a fictionary brand, Cookie Monster, and create visualizations that will help us better understand its brand performance.
Btw, GDS is free to use.
How is the data structured?
In Google Data Studio, you can tie your report to multiple types of data sources (BigQuery, Google Analytics, MySQL, YT Analytics, etc.). For simplicity, let’s stick with the basic Google Sheets.
The columns are for the following variables:
- Time: date
- Demographics: age, gender
- Location: country, city
- Preferences: loves eating cookies
- Company: Cookie Monster
- Metrics to measure: brand awareness %, average brand awareness in the cookies industry %
If you decide to use a similar template, do not forget to format your columns properly in both GDS (under Resource, Manage added data resources) and Google Sheets.
Visualize your performance in different countries or cities
In the data source, we have two countries: Germany and France.
The cool thing about GDS is that you can create maps to visualize your data (make sure you have formatted properly to a Geo type, e.g. “Country”, “Country”, “City” and/or others).
Here is an example with both France and Germany:
If you have launched marketing campaigns in specific cities, you can keep track of those and see the trend in brand awareness:
Have fun experimenting. If you do a multi-market marketing campaign across Europe, or any other continent, you can zoom into that part of the world to explore your results.
If you see that a city has a much larger brand awareness compared to another, you may check if your marketing efforts were adjusted with such intent.
Couple the Geo visualizations with Time Series to get the full picture of which cities perform the best.
See if your brand reached those you specifically targeted
What does Cookie Monster sell? Pleasure. I mean, cookies.
If in your survey you have segmented the audience between cookie lovers and non-cookie lovers, you could compare both audiences with a 100% stacked chart (in fact, who does not love cookies? anyhow, let’s stick to these variables for simplicity purposes).
Do you love cookies? Yes. 🤤
Do you love cookies? No (liar). 🤥
Do you know who is most aware of your brand, cookie lovers or non-lovers? I don’t know either. So let’s explore it together. 🤧
If your target audience occupies most of the graph, then you are doing a great job at targeting your audience. In this example, people who love cookies are more aware of your brand, than those who do not love cookies.
I do not think there is a benchmark % to decide if you are doing a good job at targeting, but if cookie lovers occupy 80% of the graph, then that shows that the targeting is effective.
You are not limited to two “types” of audiences and can plug in multiple ones on GDS.
The classic: Bar chart; The not so classic: mix up variables
Mix up different dimensions to understand how combinations in your variables perform.
For instance, you could understand the percentage of men between the age of 31 and 40 who know your brand, compared to women between the age of 41 and 50.
Or, you could see in which city cookie lovers know about your brand the most, and therefore adjust your marketing plan according to such results.
There is so much data we can extract from one simple table!
- Brand Awareness is higher for cookie lovers than non-lovers in all the cities (which means we are doing a good job of targeting our audience).
- Brand Awareness is very strong with women between the age of 41–50.
- Brand awareness with cookie lovers grows much more over the years than non-lovers.
You get the point, you can extract almost any type of information!
How does this compare to Google Presentation?
The reason I am testing Google Data Studio is because of the hassle I have been through trying to present data on Google Sheets (thank you for the recommendation Christoph).
The advantage of using Google Data Studio is that you are forced to compile your data in an organized fashion, in one single sheet. Instead of creating multiple sheets, you can create one, and plug in all your data in there. That way, you do not have to go back and forth between tabs and get lost.
Also, if you plan to input new data, you can activate real-time refreshing in your report, so you don’t have to bother about outdated data.
It is also possible to combine different variables, which would be a tedious manual task in Google Sheet.
This is how my Google Sheets looked before:
And now with Google Data Studio:
We have just covered the basics here, there are way more optionalities and formatting styles to play with.
But enough for today, we will cover advanced GDS functionalities in another article. 🏎️
Follow this Medium account, I post one article every 1st of the month. Consistently. 🦍
Thank you Google for the mention!