Google Data Studio provides you with everything you need to turn the analytics data into informational, easy-to-understand-and-present reports through data visualization. And it's a tool not to overlook given that future business actions will be based on data in the reports.
Get creative with a guide we've prepared, packed with tips on creating beautiful and useful automated reports that help to make data-driven business decisions based on those.
1.1. Check the templates
Before building your data visualization hands-on, it is strongly suggested to check other Data Studio reports as well. Besides, Google has prepared a gallery with different templates that should speed up the reporting.
Templates can help you two ways.
1) You can find inspiration regarding what data to include in a dashboard, what data to combine in the charts, which colors to use to make the report easy to read, etc.
2) If a template is exactly what you were looking for, you can make a copy, change the data source to yours, and the report is ready to use.
1.2. Connect the data set with Data Studio
After checking examples and formulating the expected outcome, the next step is to open a new dashboard and connect it to the data set you want to use. A thing to keep in mind is that between your set and DS report should be a connector. In the majority of cases, Google has taken care of it by providing more than 180 connectors to choose from.
However, there might be specific cases with no connector. Alternative methods should be implemented, for example, data can be placed in Google Sheets or uploaded as a file.
1.3. Build your dashboard
If you didn't make a copy of an existing template, at this point, you should see a blank page which is your playground. Take a look around the interface and explore your options—Data Studio allows you to visualize the data using different charts in a variety of colors and sizes.
It allows operating with the visuals of the dashboard itself, e.g., choosing the color of the background, the size of the dashboard, and which elements are page-level, which—report-level.
The draft will help you understand if there is enough space for everything and if the overall impression of the report isn't that it's overstuffed.
As analysts, we believe that all data is important. Unfortunately, people who you share the report with might think differently. Therefore be sure to leave some blank spaces. Besides, this approach will allow you to revise if the dimension and metric combinations make sense.
When deciding on the data to include, remember about additional great features Data Studio provides:
If you choose the filter Source/Medium - Google/Organic, all charts in the dashboard will show the performance of this source/medium. If you have more
than one dashboard, add filters in the header and make them report level. Once you apply the filter, it'll change the data of every chart in the report.
When the draft looks good, you can proceed with formatting. Before working on it, remind yourself that one of the main reasons why Data Studio is used for reporting is that it allows easily spotting the most important details—whether it's a single data point, a specific insight, trend or correlation.
Use formatting to emphasize this effect:
Unless you have prepared the report for yourself, this is the time to share it with the readers. Google Data Studio has made sure that you can do it in multiple ways:
For the first couple of reports, it might be enough to use default data sets. However, later you might wish to add new data-related settings, e.g., implement custom formulas or rename your existing datasets (so that all would be lowercase or start with the same letter—facebook, m.facebook and l.facebook can be renamed as Facebook, etc.).
Google has taken care of that by offering to implement an array of functions to use in calculated field formulas.
Another advanced technique you should know about is data blending that allows operating with different data sets in the same chart.
To create such a table, you'll need to create two tables and two segments—each table will show the performance of the exact segment. Before blending the data, remember to rename dimensions accordingly, so it's easy to differentiate after data blending.
In the previous chapter, we explained the process of making a report. However, there is a difference between any DS report and a good one. In the next paragraphs, we'll share tips on how to make the report easy-to-read and find workable insights quickly.
When starting to work with Data Studio, you might be surprised by how many metrics and dimensions can be used and combined. The first instinct for an analyst might be to include as many data points as possible and then investigate each of them step-by-step.
Before you start to build the biggest report ever, imagine it—a report with dozens of pages, filled tables with small numbers, overloaded line-charts where lines are so small you can’t see the difference between them and dual-axis charts that have no sense. Doesn't sound that great, right?
To avoid ending up with a report like this, plan it by answering questions like:
Despite how impressive the data combination you create looks, it should give clear insights into what to do next. Otherwise, the beautiful traffic acquisition chart might be guilty of profit loss in the next quarter.
Initially, it might seem a good idea to combine the Page dimension with the Sessions metric to see in how many sessions a specific page was visited.
In this case, the data is misleading—session-level metrics take into account only the first hit. Therefore the Sessions metric will show only the number when the specific page has been a landing page.
When customizing the visuals of the report, keep in mind the overloaded version we warned about at the beginning of this chapter. Don't do this to yourself and other report readers! Instead, play with common behavioral principles that allow reading the report in the most efficient way.
Less is more
The main idea behind this statement is that dashboards shouldn't be overloaded with too much data. Remember, the reason for choosing Google Data Studio over a spreadsheet was that it's easier to perceive the data in GDS. If you fill the page with a lot of information, the reader won't understand where to look and what to pay attention to. It's better to create two pages with the same topic and put space around data.
Placement of elements
Some data points are more important than others and ask for a deeper investigation than just a quick look. However, the attention span, as well as the ability to remember information, is limited.
Use these user experience patterns when planning the placement of charts—add the elements that give immediate answers at the top of the dashboard (the attention span is high) and complex or less important ones at the bottom.
Size of elements
All elements should be readable without extra effort, therefore a font size below 12 is not suggested. More importantly, font size is the key to emphasize specific information.
If the goal was to increase the conversion rate, and by implementing the changes this goal was completed, make sure the scorecard with these changes is the biggest element on the dashboard.
Create groups of elements
Another principle that defines a good user experience is grouping related elements. That way readers can find common patterns easily.
Continuing the increased conversion rate example, we suggest adding additional information near the main scorecard element. The user will not only remember the increase but quickly find detailed insights on what has helped achieve the goal
What's more, we advise placing similar elements in similar positions. For example, if the first dashboard has a table at the bottom, place it there on other dashboards as well. The reader will look for it there instinctively, as our brain tends to look for patterns everywhere.
At this point, you have added the most appropriate data, placed it according to principles of good user experience/visual hierarchy, and the job might seem done. But you have only reached the middle because making reports visually appealing is as important as adding correct data.
Look at this step as your chance to create a story based on data. You can structurize elements in an order that shows surprising details, emphasize specific elements (e.g., only 2 bars from the whole 10-piece bar chart), you can add explanatory colors and so on.
Remember that reports in Google Data Studio are easier to understand than spreadsheets or Google Analytics Custom reports because you can change data format from boring tables to tables with different color heat maps in them. Think creative and play with—change colors, sizes, fonts, add pictures, use conditional formatting.
At first, this might not make sense. If the main goal is to allow readers to find insights with a quick look, then why should the report contain any text? It takes more time to perceive text than pictures. From our experience with different kinds of reports, we have concluded that some dashboards are more beneficial when a few clearly described insights are shared.
Example: table—performance of landing pages
To understand how the performance differs between landing pages, the reader needs to analyze the table. Given that analysis is time-consuming, the author can do it before and share the main insights under the table in the text format. In case a reader wants to know crucial details, he can read the summary. If he has more time, he can research the table.
Data visualizations should motivate the report’s reader to listen to you more. However, you risk losing someone’s trust without checking added data.
To avoid sharing false information, compare the data you've added to the report with GA data. Some metrics will be easier to compare (e.g., sessions, users, transactions), some might need a custom report. Even though it takes time, it is necessary to make trustworthy statements and suggestions.
In addition, share the report with a colleague and ask them to evaluate the report. They will look at the report with a fresh eye and might point to combinations that don't make sense or are not necessary.
No description is more helpful than real-life examples. In this chapter, we have shared 4 different reports used by our team, for you to find answers to specific questions, and inspiration to build your report.
This is the simplest report, but it shares the most crucial information and allows us to follow changes in metrics by time. It shows the general picture, whether it's a client website or your team's performance.
The dashboard does not have many elements, and with a quick look, you can easily see if you are closer to the goal or if something needs to be changed. In this kind of report, the main question is 'what?' while the 'why?' part is not included.
The following dashboard is an example of how to track the main KPIs of an eCommerce store in the current month.
This is the type of GDS report used the most often in our team regardless of the further steps, which could be:
As analysts, we love to search for insights and we can't find them just by looking at the main KPIs. Therefore a detailed report to overview the eCommerce store from a different perspective is the key to success.
The example shared below is a part of our report of overall eCommerce store performance. It contains the following dashboards:
Seeing how simple it is to work with GDS, you might want to create all your reports there. At this point, you should remember the magical chain of how the tool works—there is a data source connected with GDS by a connector. Although GDS has a lot of connectors, some are still on their way. In that case, you can add everything to a spreadsheet and connect it to GDS.
The next example we would like to share is a report used to track A/B test results while the test is still going. You can later use this report to perform analysis when the test is completed. It's a two dashboard report containing the most important information for you to share it with the client.
The dashboards in the report:
Initially, we made the report to track the performance of a specific A/B test. However, it had a more complex set-up than usual, so the standard Google Optimize report didn't match our needs. By knowing that it is easy to collect the data in spreadsheets with
Google Add-ons and that we have the needed statistical formulas to calculate the winner of the test, we decided to create a Data Studio report.
Now we are so used to the format and benefits of working with Add-ons that we use the same report as a template for other test evaluations. To read a full case study and learn how to make this type of report check out this blog article.
When we started to work with GDS, the tool was not advanced but it didn't mean that our requirements were low. E.g., we used spreadsheets and Add-ons to create a shopping behavior funnel in GDS. The chart, however, was not fully automated and didn't change the data after updating the data period filter in the GDS report. It was time-consuming and not effective for our clients.
Then Google implemented data blending functionality and everything changed. Once you blend the data and add new formulas within the same tool, the chart is ready and the client doesn't need any spreadsheets to update the data.
The Shopping behavior funnel is a milestone in eCommerce analysis. It’s one of the first charts to view when starting to work with a new client and should be periodically checked to see how improvements change the drop-off rates.