Google Data Studio Charts to Create Stunning Reports

No business report is good without a presentation with shiny graphs and charts.

That’s where Google Data Studio comes in handy!

Google Data Studio charts help you create meaningful reports to visualize your data.

In this guide, we’ll show you all the charts that GDS has to offer and explain their purpose.

We’ll also explain how to customize chart settings data and chart properties.

An overview of what we’ll cover:

Google Data Studio Chart Properties 

It’s beneficial to learn about the properties of charts offered in the Google Data Studio account because it enables us to visualize the data in a much more customized manner. 

We can also customize the settings of that chart using various style properties. 

So let’s begin!

Depending on the type of the data and the way we intend to present it, we can choose from several types of charts and data studios. 

Moreover, we can also customize the data and install properties of the chart to visualize data in a better format. 

Data

Let’s use the table that’s already on the canvas and take a closer look at the data and its type properties in the data studio. 

We can customize the charts data from the Data tab.

Customizing the charts from the Data Tab of the Google Data Studio account

Let’s identify the Data source from which the chart is getting its data. 

The Table is getting its data from the GA Demo Account, but we can click on the Data source and select any other Data source to connect to the table. 

Selecting a data source to navigate the data from in a report on the Google Data Studio account 

Once we select the Data source, we can select which Dimension and Metric from that Data source we want to present in the chart. 

Selecting the dimension and metric for a chart in a report on data studio

Source/Medium and Sessions

The table shows the number of New Users for each Page Title. 

But what if we want to see the number of sessions per Source/Medium? 

In such a case, we can either add Dimensions and Metrics by clicking on the Add dimension, choosing the type source/medium, and selecting the field Source/Medium

Adding a new dimension of source/medium type to a chart on the report on data studio

We can also find a field on the right side of the chart on our screen and drop it on the left. 

Configuring the field charts of source/medium sessions on a new report in data studio

 

We can also replace a field by clicking on it and selecting another field or by dropping another field on it. 

Configuring chart fields of sessions in a new report on data studio

Moreover, we can also change the order of the fields that affect how they appear on the chart, or we can remove them. 

On the left side of the field name, we can see the type of field and if we hover over it, it changes to a small pencil icon which lets us change the properties of the field.

We can also rename this field. 

For example, we can call it Source & Medium, but it will only affect this table and if we are using the same source medium field in another chart on this report, it will be unaffected. 

The rest of the data properties depend on the type of chart that we’ve selected. But there are a few more at the end that are common across all charts in the data studio. 

Let’s take a look at them! 

Date Ranges

One of the most important data properties of any chart is its Date Range. This controls for which timeframe we see the data in the chart. 

By default, it’s set to auto, which means it lasts 28 days. But we can also set it to custom, once we select custom, we can choose our own date range for the chart.

Utilizing the date range feature for a chart on a report in data studio

We can choose a fixed date range by selecting the start and end dates.

Configuring the start and end date ranges for a chart on data studio 

We can also click on the drop-down menu and choose a dynamic Date range. 

Choosing a dynamic date range for a chart on data studio

The dynamic date range is calculated based on the actual day the report is being viewed. 

For example, if we set it on the Last 7 days. Every time someone views the report, the table will show the data for the last seven days relative to that day. 

Configuring the dynamic date range as last seven days for a chart on data studio

Optionally, we can compare the Metric with another Date range. 

For example, let’s consider the previous period or the same date range last year.

Let’s set it to the Previous period supply, so in the table, we can see how much each Metric has changed from the last 7 days to the 7 days before.

Adding a comparison date range to a chart on data studio

Filters

Another important property of any chart is the Filter. 

With the table Filter, we can limit the types of information that are presented on a chart. 

Utilizing the table filter for a new chart on the data studio account

For example, let’s say we want to limit the table to only show sessions from mobile devices. 

Click on Add a Filter, and add a name, Mobile Sessions, in our case. Set it to include Device Category Equal to (=) mobile, and click on Save

Configuring a mobile session filter with the device category equal to a mobile parameter

The table now only shows sessions from mobile devices.

Utilizing a mobile sessions filter for a chart on data studio 

Segments

Finally, if only we are using a Google Analytics data source, we can apply a Google Analytics segment. 

Adding a Google Analytics segment

For example, let’s say we only want to look at the returning users; we can click on add a segment, and from the System segment, we can select Returning Users.

Picking returning users from the system segments section of a report on GDS

Once we do that, the segment will be applied to the chart. 

Now we have a table that is showing the number of sessions from returning users on a mobile device Source/Medium and is comparing it from the last 7 days to the 7 days before.

Style

The rest of the settings depend on the type of chart that we select. 

Let’s take a closer look at the style tab, from where we can define the appearance of our charts once we configure the data properties of our chart. 

We can head over to the style tab to control the appearance of the chart and change its look and feel. 

Selecting a style type for a new report on GDS

While each chart type has some of its own specific edge style settings, there are some settings that are similar to all of them.

For almost every chart, we can change the fonts of different sections. You can change the font for the header and separately for the table labels. We can also adjust font sizes and colors.

Customizing size and colors for a new report on GDS

We can also change the text alignment. For example, we can set the metrics to be left-aligned. 

For a table, we can double-click on a column border to adjust the width of the columns. 

Then for almost every chart, we can set the background color, set a border, and adjust its weight, color, and its type. 

For example, it can be Solid, Dashed, or Dotted. We can also change the border radius, and apply border shadow. 

Customizing background and border styles for a new report on GDS

Visualizing Data: Learning and Using Chart Types

Let’s review different chart types in Data Studio and see how we can use them to tell different stories with data. 

We’re going to see what’s possible in Google Data Studio in terms of data visualization, what kind of charts and graphs to get access to and to communicate data exactly the way we want to the end-user of the report. 

We’re going to review each chart type with several examples, and we’ll learn how and when to use it and learn about data visualization best practices. 

Scorecards

The first and simplest chart type is a scorecard. Scorecards are good for displaying numbers for showing KPIs and by KPIs (key performance indicators). 

With a scorecard, you can show a number, a compact number, a ratio or percentage, and a currency amount. 

Analyzing the scorecard data from the scorecard chart type for a report on GDS

Also with these scorecards, we have the option to compare the values with the previous period. 

So we can see how much the number or the ratio has changed compared to the previous period of time. We can see the percentage of the change, or we can see the change in the absolute numbers. 

Analyzing the scorecard data from the scorecard chart type for a report on GDS

Another cool feature of the scorecard is the ability to apply conditional formatting. 

For instance, I’ve said that when the conversion rate is less than 2%, I want the background of my scorecard to be yellow. 

 Here also, we want the background to be green if the revenue is more than $2,000.

Analyzing the scorecard data from the scorecard chart type for a report on GDS

We can set multiple criteria for a singular scorecard. So we can set it to become green for more than $2,000 or yellow for less than $1,500, or become red for less than $400. 

That’s it for the scorecard. Let’s move on to the next chart!

Tables

As everyone knows, tables are good for summarizing data in rows and columns. Dimensions appear in rows and metrics appear in columns. 

We also can see the Grand total at the bottom of the table, and we have the option to paginate the table to see the next and previous pages of state. 

Accessing tables as a medium to analyze data in a report in GDS

But that’s not all. Tables in Data Studio have more styling features that help us visualize data differently and communicate different stories with our data. 

For example, instead of showing numbers in a simple way, we can apply a heat map to a column so we can easily respond to the largest and smallest numbers in that column.

Analyzing tables as a medium to access data in a report in GDS

We can also choose to show the numbers in a column with small bars. 

This not only helps us see the largest and smallest values, but it can also help us to compare the values more easily. 

So, for example, these three rows have about the same shade of green. So without looking at the numbers, we cannot be sure which one has the highest value. 

But, when we apply a small bar to this column, we can immediately see that this row has the largest value.

Analyzing tables as a medium to access data in a report in GDS

When we are using bars to visualize data, we can also apply a target line. Here, we’ve applied a target line at 5000. 

We can quickly see which rows are below the target and which rows are above the time. 

Analyzing tables as a medium to access data in a report in GDS

Another useful feature of tables in Data Studio is that we can apply conditional formatting. 

Let’s set the criteria that if a row is for a new visitor, the background color for the whole row would be light green. 

If the revenue is more than $300, the font color of the whole row becomes green. But if the revenue is less than $200, we want the font of just the revenue column to be red. 

Analyzing tables as a medium to access data in a report in GDS

As you can see, the conditional formatting for the tables in Data Studio is quite flexible. 

Time Series – 4:08

Using a Time Series chart, we can show the changes of the values of the metric over time. It helps us see the trends and patterns in the data. 

Here in this simple example of a Time Series chart, we are plotting users all the time.

Analyzing time series chart as a medium to access data in a report in GDS

We can also break down the metric over different categories, for instance, returning visitors versus new visitors. 

Analyzing time series chart as a medium to access data in a report in GDS

We can also compare the values to the previous period. We can see that during the last 28 days, we had both more new visitors and more returning visitors, than in the 28 days before that. 

Analyzing time series chart as a medium to access data in a report in GDS

Here’s another example of a time series chart. 

Again, we are plotting users over time, but this time, instead of breaking it down per new visitor versus returning visitor, we’re breaking it down per device category. So, we can now see the number of users from desktops, mobiles, and tablets. 

Instead of plotting it daily, you’re using the week of the year as the value for the date range. 

We can see the change in the numbers from the first week of the year to the 52nd week. 

Analyzing time series chart as a medium to access data in a report in GDS

You’re not limited to showing only one metric on a time series chart. 

For example, we can have three different metrics – Users, Sessions, and Pageviews. 

 We can show them per quarter of the year. 

If we move the cursor over any of these points, we can also see the actual values for that quarter. 

Analyzing time series chart as a medium to access data in a report in GDS

Here’s another example of the same chart, but this time, we’re using months as the dimension for the horizontal axis. 

Analyzing a time series chart as a medium to access data in a report in GDS

Moreover, a Time Series chart doesn’t have to be plotted using lines; we can use bars as well. 

We have users in each quarter of 2018 to compare it with the values for the same quarter in 2017, the year before. 

Analyzing time series chart as a medium to access data in a report in GDS

So that’s all about time series. Let’s move on to the next chart!

Area Chart

Area charts are very similar to time series charts, with the difference being that they apply a shade of color to the area underneath each series to show the volume of the data and that they can stack different series on top of each other. 

For example, when we see the value of returning visitors close to 2.5K, it doesn’t mean that we had more than 2000 visitors on that day.

But it means that the total of new visitors and returning visitors for that day was close to 2500 and that’s because the values are stacked on each other. 

Analyzing area chart as a medium to access data in a report in GDS

Let’s take another example of an area chart. 

We’re plotting revenue per month and breaking it down per device category, i.e., mobile, desktop, and tablet. 

The values are again stacked on top of each other. As a result, we can see the trend in revenue. So it started at $4,000. At the end of December, it was close to $8,000.

Analyzing area chart as a medium to access data in a report in GDS

But what if you’re not interested in seeing the trend, and instead, you’re more interested in seeing the distribution of revenue between these different categories. 

In other words, we don’t care about the total. But instead, we want to see which device category brings in more revenue compared to the others. 

To do that, instead of a stacking series on top of each other, we can use 100% of stalking, which is shown in this chart.

Here, we’re visualizing the same data set, but this time we are focusing on the distribution of the revenue. 

Analyzing area chart as a medium to access data in a report in GDS

For example, here we can see that in October, desktops were responsible for more than 70% of the revenue. 

However, in the months before and after, it was down to around 24% or 34%. We can also see the change in the distribution over time. 

Analyzing area chart as a medium to access data in a report in GDS

Next, we have line charts! 

Line Chart

The Line chart is very similar to the time series with the difference that in the Time series chart, we could only have a time dimension for the values on the horizontal x-axis. 

But with the line chart, we can have any dimension with any type of category. Here we have countries as the dimension for the x-axis and we are plotting users and revenue.

Analyzing line chart as a medium to access data in a report in GDS

This is not a good data visualization, because it suggests that there is continuity between these different endpoints. 

As they are connected to each other by the line, it suggests that there is a connection between the United States and Canada in the United Kingdom. 

Actually, this is not a continuous value like time, where there is the day before today, and the day after. These are different categories and they are not connected to each other in any way.

Combo Chart

A better way of visualizing the same data set would be using Combo Charts. 

With combo charts, we can have different series represented either as lines or bars. 

We’ve selected the users to be represented as bars and be plotted against the left axis and revenue to be represented as a blank line and be plotted against the right axis. 

Analyzing line chart as a medium to access data in a report in GDS

We’re not limited to using only two metrics on a combo chart either. We’ve added it to the metric sessions, and set it to be represented by bars and plotted against the left axis.

Analyzing line chart as a medium to access data in a report in GDS

So with the Combo chart, we can add multiple series and multiple metrics, and choose any of them to be represented at bars, or lines or be plotted against the right axis or left axis. 

Moreover, we can use any dimensions as our categories over the horizontal axis.

Bar Chart

Bar charts use bonds to plot the values of one metric or multiple metrics on the y axis, the vertical axis, and then we can have a category or a dimension applied to the horizontal axis, the x-axis. 

We have user sessions and pageviews by country. 

Analyzing bar chart as a medium to access data in a report in GDS

Accordingly, if we have just one metric instead of multiple metrics, we have just users per country. 

Further, we can apply a breakdown dimension. So, either with multiple metrics or if there is one metric, we can apply a breakdown dimension. 

Analyzing bar chart as a medium to access data in a report in GDS

We have users that are broken down by new visitors versus returning visitors. 

For example, for the United States, we can see that during the time from the report, we had this many new visitors, and this many returning visitors, but we can’t see the total of these two numbers. 

Analyzing bar chart as a medium to access data in a report in GDS

Instead of the values of a breakdown dimension, new visitors versus returning visitors are shown side by side, and they are stacked on top of each other. 

So we can see that again, we’ve had a number of new visitors during the timeframe, and this number of returning visitors during the same timeframe. 

We also have the total of these two values which is more than 300,000 users.

Analyzing bar chart as a medium to access data in a report in GDS

Whereas, here, we couldn’t see that at all. We could only see each value plotted against the y-axis. 

Analyzing bar chart as a medium to access data in a report in GDS

Also, there is another category of bar charts and data, a horizontal bar chart. In the example above, we’ve had only six categories.

But there are times when we would like to visualize more categories. And there’s just not enough room for all of them to be shown side by side. In those cases, we could use a horizontal bar chart. 

Analyzing bar chart as a medium to access data in a report in GDS

We have 16 categories. Since we’re just plotting one metric user, we can also apply a breakdown dimension to this chart.

Let’s see what it looks like. This one is the same chart, but now we have applied the age range of the user as the breakdown dimension. 

So, the length of the horizontal bars is the same, but we can see how many of those were between 35 and 44 years old, how many of those were between 25 and 34 years old, etc. 

It’s called a Horizontal Stacked Bar chart

Analyzing horizontal stacked bar chart as a medium to access data in a report in GDS

Now, just like in the example of the area chart, we are not interested in the totals. 

Instead, we want to know about the distribution of the age ranges for each of these states, and therefore, we can use a stacked bar chart with 100% stacking. 

Analyzing horizontal stacked bar chart as a medium to access data in a report in GDS

Such charts don’t show us the totals. Instead, they show percentages. 

We can easily see the distribution of change in the distribution of age ranges across different user states on this chart. 

We can see that states like California, New York, and D.C. have the oldest audience and that Florida has the youngest audience.

Map Chart

With a Map chart, we can visualize the difference in a metric across different geographical locations. 

Here we have users by country; the map will show that the darker the color shade, the more the number of users. 

Without looking at any other data within this dataset, we know that this is a website that gets the most of its audience from the United States.

Analyzing map chart as a medium to access data in a report in GDS

Now that we understand this, we can zoom into the United States and see the number of users per state, like this. 

Analyzing map chart as a medium to access data in a report in GDS

Here we can see California is the outlier, with the largest number of users, using the old legacy map chart in Google Data Studio. 

Currently, we have a Google Map, and we can see users per city. 

The first difference that we can notice on the Google Map is that instead of shaded regions, the numbers are plotted as dots and bubbles on the map. 

Analyzing map chart as a medium to access data in a report in GDS

Each bubble represents a city, and the size of the bubble represents the number of users that we had from that city. 

Another cool feature of a Google map is that we can pan around and zoom in to see more details about areas we’re interested in. 

Let’s take an example of a Google Map. We can see the users and revenue intensity worldwide. In this chart, the size of the bubble represents the number of users.

Analyzing map chart as a medium to access data in a report in GDS

The color of the bubble represents the amount of revenue. If it’s white, it means that we had basically no revenue for that city, such as in Florence, Italy. 

Analyzing map chart as a medium to access data in a report in GDS

If it’s orange, it means that we had a large amount of revenue from that city. 

For example, the bubble is both larger in size, meaning more users, and more orange in color, which means more revenue.

Analyzing map chart as a medium to access data in a report in GDS

Click on this table to filter to Google Map, and it automatically zooms on the United States to show the data only from the cities within the US. 

Analyzing map chart as a medium to access data in a report in GDS

Here is another example of the same map, and the only thing that has changed is the styling. So it looks much more like a Google Map. 

Analyzing map chart as a medium to access data in a report in GDS

If we click the table to filter the map only for New York City, it automatically zooms on the selected city. Much more interesting than a bar chart wasn’t it?

Analyzing map chart as a medium to access data in a report in GDS

Pie Chart

Pie charts are interesting, even though many every data visualization experts don’t like pie charts, and they have good reasons for that.

Not only does it takes up so much unnecessary space under reports, but also the human brain isn’t good at comparing the lens of curved lines, or comparing the angles with different angles. 

Analyzing pie chart as a medium to access data in a report in GDS

That’s why the viewers of this report will have a hard time to extract some insight and meanings from this chart just by taking a look at the visuals. 

If they want to see and compare the actual values, they have to move the mouse around all of these sections of the pie charts, one by one. 

If the report is printed, they just see a data connected to the Pie chart. 

Analyzing pie chart as a medium to access data in a report in GDS

But just like any other type of data visualization, pie charts can be used correctly or incorrectly. 

Our rule of thumb is that if we can visualize our data using any other chart type, it is not necessary to use pie charts. 

There are some guidelines that you can follow to produce a better pie chart that the viewers of your report can actually understand.

First of all, the fewer categories, the better. Here’s an example, in the first one we have 10 categories. But, the next one has 6 categories, and is a bit clearer. 

Analyzing pie chart as a medium to access data in a report in GDS

This one starts to become a good example. In just three categories, we can easily see the desktop has the largest number, followed by mobile and tablet. 

The best usage of a pie chart is when our dimension only has two categories, new visitor versus returning visitor, or male versus female. 

Analyzing pie chart as a medium to access data in a report in GDS

Some people just don’t like pies, so they created a blank circle within a pie to make a donut chart, but the same rules apply. 

Analyzing pie chart as a medium to access data in a report in GDS

But what if you have more than two or three categories? What are your alternatives? 

We have two alternatives for you. 

Instead of a pie chart, we can consider using a bar chart. The human brain can see the difference of the lens of straight lines much more easily, especially if they’re laid out side by side. 

If you want to save space, we can also use a 100% stacked bar chart with just one bar. 

Analyzing pie chart as a medium to access data in a report in GDS

Scatter & Bubble Chart

Scatter and bubble charts are usually a bit more complicated and harder to understand for the viewer.

Analyzing scatter and bubble charts as a medium to access data in a report in GDS

It usually takes more time to process the data presented this way. 

Unless you know that the viewers of your report are familiar with this type of data, or they’re ready to spend some more time to understand the data that is visualized this way, then stay away from using more complex charts such as the scatter and bubble chart.. 

Here are a few examples so you know what’s possible to do with this chart type. They’re good to show the relationship between two metrics with a dimension as they break down. 

For example, here I have transactions and the average order value. We’re using the US state as the breakdown dimension. 

Analyzing the scatter and bubble chart as a medium to access data in a report in GDS

So here we can see the outlier, California, which has the largest number of transactions and a fairly high average order value. 

Analyzing the scatter and bubble chart as a medium to access data in a report in GDS

 Four other states have higher average order value, but they have fewer transactions. 

We can make it even more complicated by introducing an additional set of dimensions. So, for example, here we have the same transaction metrics, average order value. 

But this time we’ve broken them down based on the browser. Also, we’re using bubble size to show the eCommerce conversion rate. 

Analyzing the scatter and bubble chart as a medium to access data in a report in GDS

Once again, we can immediately see the outliers. Chrome with the largest number of transactions, a fairly high average order value, and the largest e-commerce conversion rate. 

Analyzing the scatter and bubble chart as a medium to access data in a report in GDS

You can see that Firefox has the largest average order value with just a few transactions, and by few, I mean compared to the more than 16,000 transactions on Chrome. 

Analyzing the scatter and bubble chart as a medium to access data in a report in GDS

You can see the rest of the browsers with a low number of transactions and a lower average order value. 

So, if the owner of this business wants to optimize their website for different browsers, they can use this chart to quickly see where they should spend their resources. 

Let’s look at another example of transactions’ average order value, and eCommerce conversion rate as the bubble size. However, this time it is broken down by device category – desktop, mobile, and tablet. 

Again, we can see the desktop is responsible for the largest number of transactions and the highest average order value. Also, it has the largest eCommerce conversion rate. 

Analyzing the scatter and bubble chart as a medium to access data in a report in GDS

A different example to look at is which channel is bringing more traffic and more revenue.

Because the amount of revenue for this data set had a wide range of close to $1,000 to over a million dollars, we had to use the logarithmic scale for the x-axis with another scale for the horizontal axis. 

Each step here is 10 times more than the previous step, i.e., 100k, 10k, 1000, under a thousand, and one. 

Analyzing the scatter and bubble chart as a medium to access data in a report in GDS

Let’s read the data. Referral has about 100k users with more than a million in revenue. 

In summary, the organic search has about 350,000 users, but somewhere close to half a million dollars in revenue. 

Tree Map Chart

Let’s check out the Treemap. The Treemap lets us compare the value of one metric across the categories of one dimension. 

It shows the high values with a darker shade of color, as well as allocating a large area to that category. 

Analyzing treemap chart as a medium to access data in a report in GDS

Here we can see the nest Hello doorbell was responsible for the largest amount of revenue across all products. 

Analyzing treemap chart as a medium to access data in a report in GDS

Note that many of these are in the Nest category and they are nest products. 

Let’s take a look at a different way of using the Treemap. We have applied two dimensions to that product and product category. 

Instead of all nest products being scattered around different places on the chart, they’re all inside the rectangle which groups in Nest, Apparel, and office products.

Analyzing Treemap chart as a medium to access data in a report in GDS

Therefore, we can see the size of the revenue for each category. Within our category, we can see which one of the products had the largest revenue.

As you can see this is becoming complicated.

A good practice for using Treemaps is to use a dimension that has fewer categories in it.

If just applied to the product category, we can immediately see that Nest as a category has the largest amount of revenue across all the different categories for this shop.

Analyzing Treemap chart as a medium to access data in a report in GDS

Below is another example of using a dimension with a few categories and age ranges, and note which category has the largest amount of revenue. 

Analyzing Treemap chart as a medium to access data in a report in GDS

Pivot Table 

Pivot tables are good for summarizing large tables with several dimensions and metrics in a cleaner, simpler way. 

Here, we see product revenue and quantity per product category. We’re using a bar so we can quickly compare the numbers.

Analyzing pivot table as a medium to access data in a report in GDS

The first difference between a Pivot table and the Table is that it highlights the rows as we move our cursor across different categories. 

Analyzing the Pivot table as a medium to access data in a report in GDS

You might have noticed this Plus Sign and that in the title, we see Product Revenue and Quantity, which we can see per Product and Product Category. 

But the only thing that we can see here is the product category. Where is the product? If we click on the Plus Sign, we can expand the Pivot table. 

Analyzing Pivot table as a medium to access data in a report in GDS

For each category, it can list all the products within that category, and we can see line items and the revenue for that product only. 

If we scroll down slowly to reach the end of the next category, we can see that the subtotal for the next category for all products was close to $2 million dollars in revenue and close to 15,000 in quantity sold.

Analyzing the Pivot table as a medium to access data in a report in GDS

This is the same amount that we can see for the whole category. If you collapse the columns, at the bottom, we can see the Grand total for all categories.

Analyzing the Pivot table as a medium to access data in a report in GDS

Let’s take a look at another example of a Pivot table. The previous one was similar to a normal table, but with an expandable column of products. However, this one is completely different.

Instead of having dimensions represented as rows and metrics represented in columns, we have a dimension product category in rows. 

We also have another dimension, the channel, which is the default channel grouping and is represented in columns. 

For each product category, we can see how much revenue we make from referrals, how much from directs, and how much from organic searches. 

For each of these categories, we can see how much we make in total, and over the right, we can see how much in total we make from each product category, regardless of the channel.

Analyzing the Pivot table as a medium to access data in a report in GDS

By clicking on the plus sign, you can expand the product category to see the product within each category. 

For each product, you can see how much we made from referrals, how much from directs, how much from organic searches, and then how much in total. 

Just like the previous example, if we scroll down to the end of the next category, we can see the subtotal for that category.

Analyzing the Pivot table as a medium to access data in a report in GDS

Bullet Chart

Bullet charts are simple. They show the performance of one metric compared to the target. We can also set three different ranges – the low range, the mid-range, and the high range using shades of color. 

Analyzing bullet chart as a medium to access data in a report in GDS

Let’s take a look at the eCommerce conversion rate. It shows 2%, as it’s rounded to a compact number with no decimals. 

We can see that it is actually somewhere between 1.5 and 2%. The target is set to 2%, and we can set the target on whichever number that we want. It is part of the chart settings. 

This is usually something that the business owner should decide – do they want a 2% eCommerce conversion rate? 

Is this something that they’re happy with? Is this something that they are trying to achieve? 

Analyzing bullet chart as a medium to access data in a report in GDS

Also, we can set three ranges, the low range is usually considered bad. If the conversion rate is less than 1%, in this case, it’s a really bad performance. 

If it’s somewhere between 1% and 2%, its mid-range and acceptable; 2% and above is considered good. 

We can choose to show just one bar for this time period, or we can choose to compare it with the previous time period. 

For example, in this case, we can see that in the previous period, the commerce conversion rate was in the good range of 2.5%. 

In this time period, it is less than the target, but it’s still in the acceptable range. 

Analyzing bullet chart as a medium to access data in a report in GDS

Let’s look at another example. We can do the same for revenue. If they have a target of 2 million, anything less than one and a half million is considered bad. 

Anything between one and a half million and two and a half million is considered acceptable. Then anything above that is good. 

Analyzing bullet chart as a medium to access data in a report in GDS

In this period, they hit the target and they overperformed; they made close to $3 million in revenue, which is good.

If we make a comparison, we notice that in the previous period, they had made close to $5 million in revenue.

So, despite hitting the target, despite being in a good range, they’re not making as much money as they used to make in the previous time period.

Analyzing bullet chart as a medium to access data in a report in GDS

Based on this data, the owners of this business might want to actually move the target for the next period from 2 million to somewhere closer to $3 or $4 million.

Note that we didn’t show you how to set up and configure all of these charts, because that would have taken so much time. However, at least you know what’s possible. 

Also, you know what kind of options you have when you want to visualize data and communicate insights to the viewers of your reports. 

These are your tools for when you want to tell a story with your data set. 

Summary

There you have it, we’ve covered what charts are there in the GDS and what are their use cases.

Pivot tables are also a great way of presenting data. Check out our handy guide on pivot tables in Google Data Studio.

What are your favorite chart types? Do you use charts often in your job as well? Let us know in the comments below!

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Mike Espie
Mike Espie
3 years ago

Video doesn’t exist but it is here if anyone is interested: https://www.youtube.com/watch?v=U9SS9XGfOxQ

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