As I mentioned, you can bring data from Google-owned sources into Data Studio, including Search Console, Google Ads, YouTube, and Campaign Manager.

But that’s just the tip of the iceberg. There are also more than 120 partner connectors — essentially, third-party bridges between Data Studio and platforms like Adobe Analytics, AdRoll, Asana, Amazon Ads, and AdStage (and that’s just the As).

Whether your report is meant for internal stakeholders, like the leadership team, or external ones, like clients, it’ll be more effective if it looks good.

To adjust the report’s style and formatting, click the Layout and theme option in the toolbar.

Any changes here will apply across the report—meaning you only need to pick fonts, colors, etc., once versus every time, you add a new module to the report.

Data Studio comes with two built-in themes: simple and simple dark. But it’s easy to create your own — and the results are way more impressive.

Click on “Customize.”

Use your brand style guide to choose primary and secondary colors, fonts, and text color. You might need to get creative here; HubSpot uses Avenir Next, which Data Studio doesn’t offer, so I went with its cousin Raleway.

Just like you can bring your report to the wider world, you can also bring the wider world to your report.

You can insert Google Docs, Google Sheets, YouTube videos, and even live webpages with the URL embed feature. Embedded content is interactive, so it’s far more powerful than a screenshot.

Click “URL embed.” in the navigation bar to add content.

From there, simply paste the URL. Next, you may need to resize the box that appears to fit your content’s entire length and width.

The options here are pretty endless. One of my favorite ways to use this feature is to embed a Google Form gauging how helpful the report was for my audience:

If a section of the report needs extra context (or my viewers aren’t that technical), I’ll add a short video explaining what they’re looking at and how to interpret the results.

To personalize a report for a client, I’ll add the URL of their website, blog, and/or whatever pages they hired me to create or improve.

And for the HubSpot blogging team, I’ll add the latest version of the Search Insights Report so they can compare our progress to the results.

If you have a group of stakeholders that need to see your report regularly, consider using Data Studio’s “scheduled report” feature.

Click on the drop-down menu beside the “Share” button and select “Schedule email delivery.”

First, enter your recipients’ email addresses, then choose a schedule, whether daily, every Monday, or every month.

This is particularly handy when working with customers, since you may not want to give them access to the live report.

Alternatively, you can download your report as a PDF. This is helpful for one-off situations, like if your boss asks for a status report or your client wants to know how an ad has performed so far this month.

To download the file, click “download” on the drop-down menu.

Data Studio gives the option of downloading your current page or the entire report. You can even add a link back to the report so your audience can dig in deeper if they’d like and add password protection to ensure your data stays safe.

8. Embed reports.

You can even display your report on your company website or personal portfolio—which can be a great way to highlight the results you’ve gotten for a client or project.

Click the brackets icon in the upper navigation bar.

This box will pop up:

Adjust the width and the height as needed, and you’re good to go.

Give your viewers more freedom by letting them select which dates they’d like to see information for.

For example, my reports always default to the last 30 days, but if one of HubSpot’s blog editors wants to see how their property performed in the previous calendar month, the date range controls let them adjust the report.

They can choose from predefined options, like “yesterday,” “last seven days,” “year to date,” etc., or pick a custom period.

To enable this, first navigate to the page you want to give users date control. Next, click on the drop-down menu by “Add a control.” Next, click “Date range” from the toolbar.

A box will appear on your report. Drag it into the position you want — I recommend somewhere in the upper right or left corner, so your audience sees it first — and adjust the size if necessary.

Clicking this module will bring up a panel to the left of your report called Date Range Properties. Set the default date range to “Auto date range,” if it isn’t already.

If your viewers select a date range using the date range widget, every report on the page will automatically update to that period.

There are two ways to override this:

• Set a time period within a specific chart. That time period will always supersede the date range control.
• Group the charts you want to be affected by the date range control with the module. Select the chart(s) and the box, then choose Arrange > Group.

Now, only the chart(s) in this group will update when someone adjusts the date range.

Make sure this setting is clear to your viewers — otherwise, they’ll probably assume all the charts they’re looking at on their current page are using the same time period.

Give your audience even more flexibility with filter controls. Like the date range control, a filter applies its settings to every report on the page. So if, for example, someone filtered out everything besides organic traffic, all the reports on that page would show data for organic traffic specifically.

Add a filter control by clicking this icon in the toolbar.

The filter will appear on the report page. Resize it and drag it into the position you want. While it’s selected, you should see a panel on the left-hand side:

In the data tab, pick which dimension you want viewers to filter. These dimensions come from your data source — in this example, I’ve chosen Traffic Type.

The metric part is optional. If it’s checked, viewers will see the values for each dimension sub-category in the filter. (This will make more sense once you see the screenshot below.) They can sort by these values, but they can’t filter by a metric.

You can add an additional filter to your filter control. For example, if you’ve added a filter for Source / Medium, you may want to exclude the “Baidu /organic” filter, so your viewers don’t see that as an option.

Customize your filter control’s formatting and appearance in the style tab. You have a few options: list/check all that apply filters, like this one:

Or “search all” filters, which allow your viewers to search by numeric and text terms using operators like >=, and <, or “equals,” “contains,” etc., respectively.

This can be a hassle for the people reading the report—plus, they need to be somewhat comfortable with search operators. So, unless your filter dimensions have 10,000 values (unlikely), stick with the list filter.

11. Create interactive chart filters.

Want to make it even easier for your audience to filter the charts in your report? Create responsive chart filters.

This sounds fancy, but it simply means selecting a dimension in a chart will filter all the charts on that page for that dimension.

For instance, if you click on “organic” in this chart, the other charts on the page will update to show data for organic traffic only — just like you’d applied a traditional filter control.

You can also create chart controls for time, line, and area charts. For example, if a user highlights say, January through March on a time chart, the other charts on the page will show data for January through March as well — just like date range control.

And also, just like filter controls, you can group chart controls.

To enable chart control, select the appropriate chart. In the right-hand panel, scroll to the bottom and check the box labeled “Apply filter.”

Add a caption next to charts that support interactive filtering, so your viewers know it’s an option:

Data controls may just be one of the coolest Data Studio features, full-stop. Place one of these bad boys on your report, and you’ll give viewers the ability to choose the source of the data being piped into your charts.

This is a game-changer for anyone managing a complex property or working with multiple stakeholders.

For instance, imagine you’re the admin of HubSpot’s Google Analytics account. You create a Data Studio report monitoring key website performance indicators, like average page speed, number of non-200 response codes, number of redirect chains, and so on.

13. Add a dimension breakdown.

Instead of telling you what a dimension breakdown is, it’s easier to show you how it works.

Suppose we want to see users by source. To find out, we create a simple bar chart.

This is interesting — yet there’s some context missing. For example, is all of that organic traffic coming from Google? (Since this is U.S. data, probably, but imagine creating the same chart for China or Japan, where Baidu and Yahoo have a far greater presence.)

What about referral traffic? Clearly, we’re getting a significant number of users from referral links; is a single source driving most of them, or is it distributed fairly equally across a wide variety of sources?

We could create separate bar charts for each source — first filtering by medium and then making the dimension “Source” and the metric “Users.”

Or we could click a single button and have Data Studio do it for us.

Under Breakdown Dimension, click “Add dimension.”

Add “Source.”

Here’s what you should see:

Pretty sure my former Data Analytics professor would cry if he saw this. But don’t worry, we’re not done yet.

Jump over to the “Style” tab and check the box “Stacked bars” to turn your regular bar chart into a stacked bar chart (you should see the chart type update accordingly).

Data Studio will automatically make your bar charts “100% stacking,” meaning that every bar will go to the top of the chart. However, this style is misleading — for example, here, it suggests every medium drove the same number of users.

Uncheck this box.

Now check it out:

To bring any chart into Explorer, mouse over the space next to its top-right corner. You’ll see three vertically-stacked dots appear; click them.

Select “Explore (Labs).”

You’ll see something like this:

You can toggle between different visualizations; add and remove dimensions and metrics; change the date range, and apply segments.

Note: Unlike every other Google tool out there, Explorer does not automatically save your work.

To preserve your chart, click the “Save” button on the top nav bar (to the left of your profile icon). Once you do that, your Explorer “report” will be saved in the Explorer section of your dashboard. In addition, every change you make will be saved by default.

15. Create report-level filters.

By default, a filter applies to every chart on that page. But what if the viewer goes to the next page? The filter won’t go with them.

This is confusing for non-technical folks and inconvenient for data-savvy ones. To bring a filter up from page-level to report-level, simply right-click on it and select “Make report-level.”

Data Studio is powerful because you can bring in 400+ sources of data into a single report. But, thanks to a new feature, blended sources, it just got even mightier.

Heads up: this will get a little technical. Stay with me, and I promise it’ll be worth it.

If you’re familiar with JOIN clauses in SQL, you’ll understand data blending right away. No idea what SQL is? Not a problem.

The best way to think about blending data is with a Venn Diagram. You have two data sets. Each data set has unique information — e.g., such as the data living in the green and blue areas.

But they have (at least) one data point in common: the information in the blue-green overlap section.

This shared data point is known as a key. If your data sets do not have a key, they’re not blendable.

For example, suppose you want to compare how users behave on your website versus your app. The key is the user ID, a custom dimension you’ve created in Google Analytics that your app analytics software also uses. (Note: The key doesn’t need to have the same name in both data sources; it just needs to have identical values.)

You blend your website behavior report from GA with your app usage report. This gives you all the records from the first report along with any matching ones from the second; in other words, if a user has visited the site and used the app, they’ll be included.

However, if they only used the app but didn’t visit the site, they will not be included in the new blended data.

Try this workaround if you’re bumping into limitations with your data source connectors: blend a data source with itself.

To give you an idea, the GA data connector only lets you add one “active user” metric to a chart, so there’s no way to see 1 Day Active Users, 7 Day Active Users, and 28 Day Active Users on the same chart… unless you blend your Google Analytics data source with itself.

Follow the same instructions as above, but instead of picking a new source for your second data source, just select the first one again.

And since all of the fields are identical, you can pick whichever join key you’d like.

This option is also perfect when comparing trends across two-plus subdomains or segments.

For instance, I wanted to look at organic users for the HubSpot Blog (blog.hubspot.com) and primary site (www.hubspot.com) at the same time.

This helps me figure out if we’re growing search traffic across the board. It’s also helpful when traffic decreases — have rankings dropped site-wide, or just for the blog (or the site)?

However, you can’t add two separate “user” metrics to a chart at once… unless, of course, you’re blending data.

Create a new blended data source (following the same process as above) to set this up.

Add your first view to the left-most column, your second view to the following column, and so on.

Note: Make sure you’re choosing views with mutually exclusive data. In other words, I wouldn’t want to use “blog.hubspot.com” as my first source and “blog.hubspot.com/marketing” as my second source because all the data for the blog.hubspot.com/marketing view is included in the blog.hubspot.com one.

Because of that overlap, we wouldn’t be able to spot trends clearly.

Use “Date” as the join key.

I added the organic traffic segment to both sources, but you can choose whichever segment you’re interested in (paid traffic, social traffic, etc.) Or leave it off entirely! Tons of possibilities here.

In fact, here are some additional ideas for blending a source with itself:

• Compare two-plus custom segments
• Compare two-plus landing pages
• Compare two-plus goal completions

When your existing data doesn’t give you enough information, it’s time to create a calculated field.

Calculated fields take your data and, as their name suggests, makes calculations.

It’s probably easiest to explain with an example.

Let’s say you want to look at the average number of transactions per user. You can create a calculated field that takes the metric “Transactions” and divides it by the metric “Users.”

Once this field has been created, it’ll be updated automatically — so you can change the chart’s time range, dimensions, etc., and the average transactions per user data will update accordingly.

There are two ways to create a calculated field.

Create a data-source calculated field

This option makes the field available in any report that uses that data source.

It’ll also be available as a filter control or in new calculated fields (like calculated field inception).

Obviously, this is a good option if you plan on using this custom metric more than once. The only caveat — you must have edit rights to the original data source. You also can’t use a data source calculated field with blended data.

To create a data-source calculated field, add a chart to your Data Studio dashboard, then choose the data source you want to derive your new field from.

Click “Add a new field” in the lower left-hand corner.

(You can also do this by clicking the pencil next to the data source and then selecting “Add a field” in the upper right corner of your field menu.)

Use the left menu to search for the metrics you need; click one to add it to the formula.

If the formula has an error, a notification will appear in red underneath the editor explaining where you went wrong.

If your formula works, you’ll get a green checkmark.

Click “Save” to add your new field to the data source.

And don’t forget to name yours — which I forgot to do. 🙂

Now you can add this calculated field to any chart just like a regular field.

Create a chart-level calculated field

For this option, you’ll only be able to use the field for that specific report.

This option is a little easier because all the limitations of the other type are reversed.

While you can’t use a chart-level calculated field in another chart, filter control, or additional calculated field, you don’t need edit rights to the original data.

You can also use a chart-specific calculated field for data blending, which we’ll cover in the next step.

To create a chart-level calculated field, simply click “Add a field” underneath the existing dimension(s) and metric(s) you’ve selected.

When you choose to add a new field, this pane will pop up:

From here, enter the formula for your new field — simply typing in the name of your desired metric will trigger a menu of options — and click “Apply.”

Your new field will be added to the chart.

Okay, so there’s a lot you can do with simple algebraic calculated fields. But there’s even more you can do once you introduce functions and RegEx.

Don’t be scared off! We’ll walk through these step-by-step.

If you’re comfortable with Google Sheets and/or Excel functions, you already know how to use functions in Data Studio.

For instance, let’s say that you majored in English, and it’s always bothered you that “Source” in Google Analytics is lower-case.

You can use the UPPER function to transform Source into all upper-case.

Simply click “Add dimension” > “Create new field.”

Then enter the UPPER formula:

This is the pinnacle of Data Studio mastery, requiring all the skills you’ve already learned and a hefty dose of luck — just kidding, it’s super easy.

Create a blended data source per usual.