How to Calculate the Conversion Rate of Two Events in Google Data Studio

Ahmad is back with another Data Studio tutorial. Today, he will take us through how we can calculate the conversion rate between two Google Analytics events within Data Studio. In this video, let’s learn the trick of using filters and subsets to blend data sources and create custom calculated metrics.

? Links:

Ahmads Siavak –

Google Data Studio Calculated Fields:

More Videos on Google Data Studio:

In this video, Ahmad is going to show you how you can calculate the conversion rate of two Google Analytics events inside of Data studio. All and more coming up.

This video is brought to you by our premium membership MeasureMasters. If you truly want to master the art of analytics and data, then become a member today. And get access to over 70 hours of premium video content, exclusive tools, and mingle with other fellow MeasureMasters in our community. And if you ever get stuck, I’m there for you to answer your questions and support you along the way to measure mastery. Join today and become a MeasureMasters.

Thanks, Julian. This is Ahmad again with another tutorial for Google Data Studio. And in this tutorial, I’m going to show you how to calculate the conversion rate between two different events and report it using Google Data Studio. Let’s start by creating a new report in Data Studio and connecting it to Google Analytics data for Google merchandise store. And now, let’s first take a look at the events that are existing in this data set. So what I want to see is events category, events action, and events label. These are my dimensions and then for the metric, I want to see the total number of events. Quick tip, you can double click on any of these edges to quickly adjust the width of these columns of any table in Google latest video. Now, let’s review and take a look at what kind of events that we have. So most of them seem to be quick, we use clicks. But what I’m interested in for the purpose of this tutorial is enhanced ecommerce Add To Cart, enhanced ecommerce product click. Now when people come to this website and they click on products. And then they might add Add it to the cart. Okay, what we want to calculate right now is the ratio between product add to cart and product clicks. So it is kind of product Add To Cart conversion rates, as to say. So let’s go back to edit mode and see how can we define such kind of calculated metric in Google Data Studio. So normally when we have a metric here and we want to create a calculated metric, we can click on here in pageviews and create a field. Now we can name it anything we want, like products, click to ATC for Add To Cart conversion rate. So from each hundred product links that we get coming out to constantly get up. Here, we cannot use a normal formula. Because in normal formula, for example, we can divide page views. Okay, a page. page views, okay, by the number of sessions for example.

We can do that. We can divide a metric by another metric, we can add a metric to another metric. Okay, we can do arithmetic calculations with any two metrics that we want. But we cannot do it with with a subset of each metric. So the metric that we are looking at right now is total events. Okay. You can see, we can put total events in here, but we cannot divide it by anything else. Like, we cannot divide total events by total events. We cannot say that, OK, Google Data Studio, please divide just those events that match the event action Add To Cart by those events that match the event action of product link, we cannot say that directly in the formula, it is a single metric. And we cannot use subsets of a single metric in our calculations. We cannot say just some events, divide by some other events. Okay, the total of some events divided made a total of some other events, we cannot say that directly. So what should we do? Let’s see the trick that makes this happen.

So first of all, what we need to do is to create a scorecard for our main metric which is total events. Okay, so total events, we put it here. And we can see. So the total number of events. And if we add a summary row to this table, we can add the you can see that the numbers match. So this is the number of total events that these Google Analytics accounts had in the timeframe of this report. But how can we get the total number of events with event action product click? So how can we get this number? To do this, we need to add a filter. So a scorecard events. Go on and add a filter, I’m going to call it I usually called the name of a filter, exact criteria for that filter. So for example, for this one, I say event action equals product click. That way, when I’m looking at the list of filters in my Google Data Studio reports at a future date, I can quickly see what is this filter doing. Okay, I just need to include events, actions, actions are equal to product click exactly as it appears here. Ok. So now, it should filter this scorecard and show me the number of product clicks. Right now, it is not total events anymore. It is filtered version of total events. That’s why I need to click here and rename it to product clicks. Because I like the title of the metric to be descriptive to show what the metrics is about. And this is how we do it, we can click here and change the name of the metric temporarily not on the data source level, just for this scorecard to whatever we want. Okay, now, what I’m going to do is I’m going to hit Control C and control V to copy and paste this scorecard and create another one. And for this one, I’m going to apply another filter. So let’s again, click on add a filter and click a filter. And this time, guess what, event action equals Add to Cart. Okay, this is the event action that I want. So let me copy this to save myself from typing it again. And we want to include event actions that’s are equal to add to cart. And now we should have this number 7,373. Okay. Now, again, this is our product clicks. This is add to cart, or add to cart. Let’s keep it just like this. Okay, so I have the number of cards, and I have the number of products. These are basically the same metric. These are both total events. But these two scorecards have been filtered differently. So it is a total events. One filter applied this total event, another filter, filter, applied.

Now here’s the trick. When you create your metrics and filters in the way that you want and you’re already getting the numbers that you want, certain number of add to cart and product clicks, you select both of those, right click, and click blend data. And a newest scorecard appears. Okay, this is the new scorecard. Here’s the difference. If you check any of these two scorecards, these are connected to Google Analytics Google merchandise store as their data source. But this one, however, is connected to blended data. What is this blender data? This is a combination of two data sources and in this case is one data source is product clicks, total events filter to show product clicks only. And the other data source is total events filtered to show add to carts only. And right here, we can see that we have only two available fields, product click and Add to Cart. If you click on the metric, only two metrics are available in this blended data source.

Okay, now how to calculate the conversion rates between add to cart and product click. how to divide them together. You can click here on the metric only here, you can click on creative field, name it. So I’d usually in this cases, I use this naming convention CR for conversion rate between Add To Cart from between product, click and add to cart. So conversion rate between product links and add to Cart. It should be somewhere close to 65, 70%. Because it’s 7000 over close to 12,000. Okay, but here, we don’t have any metric name total events in this blended data set anymore. The only thing is that we have our add to Cart and we can divide it by product clicks, okay. And these are the names that we’ve given to these two metric, this could be my name, this could be Julian’s name, you know, put any name that you want on the metrics that you create. But you know, let’s be clear and descriptive. Right now, if I divide these two metrics together, it will give me a number less than one. But that’s why I need to choose here, instead of number. I want a percentage that converts that ratio to a percentage. And then I can click Apply. Because it’s a percentage, I tend to choose average, because if it’s you know reported in a tables and you have the summary row available, it will add percentages together for. For example, if you have all 1% 1% 1%, it will add them together to create a 3% summary row, which is not true. So I put average whenever I create a percentage metric or a conventionally, okay, so let’s click Apply. See, what do we have? Okay, so conversion rate between product link and Add to Cart is 61%. This is conversion rate between a subset of events, those who match our filter here, the way we created and the other subset of events.

You can use this same technique to blend and combine up to five different metrics of this type together. In other words, any blended data source can have up to five different data sources blended together in itself. Okay. Now, this creates a whole lot of possibilities for you. So for example, let’s say we also have events for product impression product checkouts, product removes from cards and product purchases. So impression, click, add to cart, checkout and purchase five different metrics. Okay. And if you have all these five metrics together in a single blended data, then we can divide them together and come up with calculated metrics like product, CTR, or click through rates, which would be the number of click divided by the number of impressions. Or we can have the checkout conversion rate which is the number of purchases divided by the number of Checkout. Or maybe even checkout abandonment, which is one minus the ratio between product purchases, and product checkouts. So the sky’s the limit and then you are not limited to events at all. So it is not that we can only do this with events, we can do the same technique, we can apply it to page views of the set and page titles or page URLs that can apply to sessions. Any metric in Google Analytics and in your data source that you can filter and create a subset of, you can combine them together, select them, all right click and blend them together and use the blended data source to create custom calculated metrics. You can also use the same technique on page views. And for example, divide the number of page views of your cart page by the number of page views of your product page and category page and homepage. Or if you have a funnel from the landing page. to the order page. to the thank you page, you can have three different metrics of page views to these pages, blend them together, and calculate the conversion rate between each step to the next step. Now that you learn this technique it’s your turn to put it to use.

All right, so there you have it. This is how you can calculate the conversion rate between two Google Analytics events within Data Studio. Did you know about this technique beforehand? I actually didn’t know I always used another trick but which was a little bit more complicated. So thank you Ahmad for making this video for us. Definitely, we’ll appreciate it. If you liked this video, then please leave us a comment down below how you found it and give us a thumbs up. And also if you haven’t yet, maybe consider subscribing to our channel right over there because we bring you new videos just like this one every week. Now my name is Julian and on behalf of Ahmad, til next time

Advanced Date Selection in Google Data Studio – Create rolling dates

Google Data Studios Advanced Date selection feature lets you input rolling dates into your Dashboards. For example you can create a data visualization based on the last two weeks, without having to update your date range each day. In this video, we have Ahmad of Siavak to walk us through how we can utilize this new feature.

? Links:

Get started with Google Data Studio:

Our Google Data Studio Playlist:

Ahmads website:

Previously it would have been really hard to configure a dashboard based on a time backward or forward based on the date that is today. Luckily, there is a new functionality within Data Studio which is called the advanced date functionality which will now find in all the date pickers that we have in Data Studio, and I asked Ahmad to come up with a little tutorial to show off this new feature and how we can utilize it inside of our dashboards. Now, we’ve got lots to cover so Ahmad, take it away.

Thanks, Julian. This is Ahmad from Siavak. And in this video, I’m going to walk you through the advanced stage selection feature in Google Data Studio. This is a fairly new feature. And now let’s dive in and see how it works. This is a blank reports that I’ve made, I’ve already added a data source. And let’s create a time series chart to see what options do we have for selecting the date range. But this is our time series chart. We want to see the number of sessions over time for this website. And by default, the date range that is applied to a chart in Data Studio is auto A means last 28 days. We have other options as well. So we can choose for this to show other period of time by using custom. Let’s see what options do we have. The first one is fixed which is simple. Instead of last 28 days, we can choose this supports to always show for example, May 1st to May 14th. And regardless of the date that the end-user access this chart and views this report, they’re always going to see the report of the first of May to the 14th of May.

This isn’t dynamic. But we have some dynamic options as well which are based on the actual date that the report is being accessed. Just like this last seven days, last 14 days, last 30 days. And not last 30 days according to today, it is based on the actual date that people access the report. These are the options that we kind of always had in Google Data Studio. So it’s this week, last week, this month, last month, this quarter, last quarter. But what if we wanted to set the date range for this chart to two months ago until two weeks ago. So we couldn’t do that. Or let’s say we wanted the last three weeks, not including the days that have passed from this week. We couldn’t do that either. But with the new Advanced Data selection feature, we can do this and lots more.

Let’s see what it means. First of all, we need to select the start date. For this type day, we can choose fixed which is a fixed date. So let’s say, for example, we want first of January 2019 until last week, every time we want to load the reports we want to see the first of January until last week. This is how it’s done. So the first day would be first of January, fixed and end date with me today, minus one, not one day, one week, and we can choose a starting and Sunday or Monday as well. So when I applied this, whenever a user accesses the report, whenever they’re viewing the report, they would see the report is starting from first of January, going to the last week, not including the days that have passed from that week. Now let’s do something else. Even for the start date, we can have some dynamic value. So let’s say we wind from four weeks ago, to last week, that would be today, minus four weeks till today, minus one week. We can also have this with quarters, with years, or with months. So from three months ago, to one month ago, minus one month. Today, which is the first of June, it loads from the first of February till the end of May. That’s cool, isn’t it? Now let me show you another usage for advanced time selector. Let me add another this time, scorecard. And for this, I want to see the average order value.

Let me delete this one. So we just take a look at the average order value here. Okay, this is the average order value today for instance order last 28 days. Okay. But let’s think about this hypothetical situation that the client wants to see the performance of their website in terms of average order value over the last seven days compared to how it used to perform from first of January up to last seven days. So basically, they want to compare last week to the whole period of time from the first of this year to that point. For this, we want to first set the custom date range for the actual value of this scorecard to be from today, last seven days. Okay, we wanted to show the average order value for the last seven days. And then we want to compare it to the average order value from the first of January, up to eight days ago. For this enabled the comparison, for the comparison, historically we had fixed we had a previous period, which is our last seven days compared to the seven days before that, or from the same seven days of the previous year. But now we have advanced and we can choose this time to be fixed. And for this situation, we want it to be first of January. And to end date would be today minus seven days. If I apply, I will have a comparison. So this is the average order value for the last seven days. And it’s down from the average order value performance over there up to last seven days. But this is not clear. So let’s go to style it, uncheck this so it actually shows us what’s the main value is being compared to. So domain value is minus 1.3%. down from what we had from first of January to 21st of May. And by the way, you can show absolute changes as well. So it is nearly $3 less than the average order value that we had from first of January to 21st of May. So as you can see, not only we can use the advanced date range feature for the main scorecard or the main charts that we’re using, but also we can use it for a more flexible date ranges for the comparison with previous date ranges. That’s it. Thank you for watching and see you on the next video. Bye

All right, so there you have it. This is how you can utilize the new advanced date feature within Data Studio. Now, do you actually utilize this already? Or do you have a good use case I’d love to hear from you down in the comments below. And if you want to find more out about Data Studio about different features, functions, and so on, we have a playlist on Data Studio right up there where you can find out more with more tutorials. And as always, if you liked this video, why not give us a thumbs up and don’t forget to subscribe to our channel because we bring you new videos just like this one every week. Now my name is Julian, see you in the next one

How to use the Data Studio Case Function

The Case Function is one of the most advanced and most useful functions in Google Data Studio. In today’s lesson, Ahmad is here to give us a tutorial on how we can use the Case statement in building our dashboards and create calculated fields.

? Links:

Google Data Studio

Ahmads website Siavak

I T T T. If This Then That. If you are using Data Studio, then you should learn about the case function. Now functions are a very important part within Google Data Studio to create calculated fields. And the case function is probably the most advanced, most useful, but also most powerful function you can learn. And it really works like If This Then That. So I’ve asked Ahmad to come up with a little tutorial to explain the case function to us and how we can utilize it inside of our Data Studio dashboards. Now, we got lots of cover, so Ahmad take it away.

Thanks, Julian. This is Ahmad from Siavak. And in this video, I’m going to show you how to use Case When statement in Google Data Studio to create calculated fields. The Case When statement is one of the most powerful formulas available in Google data studio which you can use in many situations. But for the purpose of this tutorial, we’re going to use it to build a table that shows traffic and conversions from Google Analytics by weekday versus weekend. And you know, that’s not a dimension that is usually available in Google Analytics. This is a rather advanced feature of Google Data Studio. So I assume you already know the basics, like how to go to build reports, a data sources and visualize data using charts. Let’s begin. We are going to start with creating a blank report and connecting it to Google Analytics demo account as a data source. So let’s select Google Analytics and demo account. Master view and connect it. Here we have our dimensions and metrics.

Let’s add this data source to the report. And we are good to go. So remember, our aim today is to create a table and categorize days of the week into two groups of weeks days versus weekend. We want to be able to visualize and analyze sessions, conversion rates, etc, per weekday versus weekend. For this, we first need to table. So let’s add one.

Now let’s add our metrics. I want sessions. It’s good to have page views, conversion rate. And we also want the average order value. Average order value. Let’s make it a bit bigger. So we can see all the data. And just a quick tip, if you double click and one of these edges of the columns, it will resize all the columns automatically to show you all the data. Now for dimension, we don’t want medium. We want something closer to see type of the weekday which we have day of week name, which is something that is available in Google Analytics by default. Okay, we just want to use it as a starting point. So here we go. We have our weekdays at the name of the day of the week, number of sessions, page views, e-commerce conversion rate, and average order value. Now, this is quite close to what we want, but not exactly. Instead of seeing the name of each day separately on its own row we’d like to have only two rows. The first-row being weekday and the second-row being weekend. This is not a dimension that is already available in Google Analytics. So to do this, we need to create a calculated field. Calculated fields are fields that their values are evaluated based on some calculations on other fields values. Sounds good. Let’s create one, we need to go to the resources menu and click on Manage added data sources to see our data source and edit it. When we edit our data source, we can add new fields. Were clicking on this all we arrive at this interface which allows us to give our new field a name. I’m going to name it date type, or let’s say sorry, day type. And then in the formula section, I’m going to use and introduce you to case when the statement. So before I start typing in the formula, let’s see what do we want to achieve in the table above. We want to write weekday. If it’s a Thursday, we want to write weekday, if it’s a Tuesday or Wednesday or Friday. But if it’s Saturday, or it’s Sunday, we want to write weekend instead. Okay, now let’s see how can we write this logic with a case when formula? Okay, CASE WHEN formula, and I write it in uppercase, but it’s okay, you can write in lowercase as well.

Looks like this. Okay, now, it is gonna say it’s invalid, but it’s okay. Because I’m just showing you it doesn’t allow me to input sample texts here. Okay. So, I mean, when a condition is met, the condition is met, we want to return a result. Okay, let’s allow the studio to do its autocomplete. But what I mean is results. Let’s see if we can get rid of that. Yeah.

And when another condition is true, we want to return another result. And we always end it by the word End. We can do something else which is else. Which means if none of these conditions are met, then return another result. This is the syntax of a case when statement. Now in this case, we do not need else because we only have seven values for weekdays for the name of the day of the week. And we already know for each value we want to return which string or text value. So let’s start the writing.

When day of week name equals two. Let’s start with Monday. Then return weekly. Similarly, when it’s Tuesday, return weekday. It’s Wednesday, again return weekday. Also, return weekday for Thursday and for Friday. But I pasted it twice now. But if it’s a Saturday, return weekend. And if it’s a Sunday, again, return weekend. Let’s wait for it to validate a formula. It says our formula is valid with this green checkmark. And now we can save it and click finished. Now let’s add our newly created dimension which is date type. So our table okay looks good. For each day of the week, that is a weekday, it has returned weekday. And for Sunday and Saturday, it has returned weekend. But we didn’t want this we wanted only two rows. So I need to get rid of their week name as a field. And we’ll have our table we can see our sessions, page views, ecommerce conversion rate, and average order value per week day versus per weekend. And we already can see some interesting data in here. So if I was the owner of this shop, I would rather advertise and promote my products on weekdays because the average order value is like five times bigger than average order value on weekends. Now this is going to be a quick tutorial. And I cannot show you all the details of case when statement. But let’s go back to the formula. Because I want to show you some interesting things about it. Again, we go to resource, manage added data sources. It is our data source.

Type in to find our field, click on function. And here is our formula. Now the first thing I want to talk about is the logical operators that you can use in the conditions section of case when the statement. So instead of equals, we can use exclamation mark equals which means does not equal something. Okay. And another thing that we can use, we can use all and raise another condition. So all day of week name equals Tuesday. It’s a big day.

So as you can see in a single line, we have checked for both Monday, and oh my typo. For both Monday and Tuesday. Okay, now we can’t bring all of these days into one line. But there is still a better solution. Here, let’s remove line three to line six because I want to bring all weekdays in one line. For this, I’m going to use in.

In a case when statement we can use in and then parentheses. And then inside these parentheses, we can have multiple values separated by comma. Okay, like Monday and Friday.

So the result is the same. But we achieved all the logic that we wanted. Now to combine Saturday and Sunday, for there’s a few more little bit more technical, I’m going to use a regular expression. And yes, you can use regular expression, in a case when a statement, which you know is powerful. And it brings the flexibility of this formula to a whole next level. If you do not know what regular expression means then it’s fine you can use in as so before. We wrote it much more easier in seven lines, it is possible. But I encourage you to go and check out regular expression because it’s so much powerful. And it comes in handy in many situations. So for this, I’m going to write regexP_match.

And then I’m going to have my field name. And then I’m going to have my regular expression in here which in this case is simple. Saturday pipe, which means or Sunday. So basically in the language of regular expression, I’m saying that when day a week is Saturday or Sunday. The formula is valid, let’s update it and click finished. Great. Now we have the same table which has two lines of code. If you’re curious to know more about the case when statement, please refer to Google Data studios documentation. It’s easy just Google it. And hope you’ve enjoyed this tutorial and thanks for watching. Bye.

All right, so there you have it. This is how you can utilize the case function within Data Studio. Do you have any other use cases that we haven’t thought about? Then let us know down below in the comments. We’d love to hear from you. And if you want to learn more about the functionality of Data Studio, definitely check out our playlist right there. And don’t forget to subscribe to our channel because we bring you new videos just like this one every week. Now my name is Julian. See you on the next one.

The Ultimate Google Data Studio Tutorial (2020 Updated)

Google Data Studio is Google’s prime tool to build Data Visualisations. Today, Ahmad of Siavak is going to show us how to make the most of this Analytics tool and build a quick dashboard for us.

? Links:

Google Data Studio Tutorial Playlist –

Google Data Studio –

It’s 2019. And one of the hottest products from the Google Analytics line is probably Google Data Studio. They have improved the tool so much over the last few months and over the last few years that I wanted to update our tutorial on Google Data Studio. Now, I’ve asked Ahmad to come up with a quick overview video on talking you through the most important features of Google Data Studio, how to build a dashboard with it, and go through the general process of creating a dashboard and sharing it out to your stakeholders. Now, this is by no means a complete tutorial. All the features that are out there, and Google Data Studio this will take far too long. But we have a playlist on Google Data Studio where we explain certain features in mode, so check those out as well if you’re interested. But for now, we got lots to cover, so Ahmad take it away.

Thanks, Julian. This is Ahmad from Siavak. And in this video, I’m going to give you a quick overview of Google Data Studio. Data Studio is a free tool from Google that allows you to connect to and pull data from different data sources like Google Analytics, Google Ads, Facebook Ads, or even Google Sheets, and then easily create visual reports to share with your clients, stakeholders and team members. In this video, I’m going to show you many features of Google Data Studio as we create this awesome ecommerce reports together. Exciting, isn’t it? Let’s dive in and see how it works. This is the main interface of Google Data Studio. We can see your previous reports in the middle and at the top, you can choose to start with a template or create a blank report.

We are going to start with creating a blank report. The first thing we need to do is to connect our report to a data source. We can do it by clicking on Create new data source. Google Data Studio can connect to a lot of different data sources. There are free connectors available for Google products, such as Google Ads, Google Analytics, Google Sheets, or even Google Bitquery. And for everything else, we can use partner connectors. Partner connectors allow us to connect to many different data sources like Bing, Facebook, Instagram, Adroll, etc. For the purpose of this tutorial, we’re going to hit cancel. And it starts with the sample Google Analytics data provided with Google. collect data reports, and we are good to go. First things first,let’s give our report a name.

Next, let’s add the header. We started creating a rectangle, resizing it and changing its color. Next, we’re going to add a title. You can select the text, change the color, and change the size of the font, and also resize the widget and move it to better place. Now let’s add some numbers to our reports by adding some scorecards. Score points are good for showing KPIs or key performance indicators. Basically, any number. I’m going to head to is style tab to give our scorecard a border and round the corners. Let’s adjust the padding as well. And then I’m going to head over to the data and enable the date range comparison. So I’m going to compare this metric to the previous period. And here he appears. Now that we are happy with the scorecards, we can duplicate and create more. This retains the styling and the configuration for the date. The only thing we need to do is to change the metric. For this one, let’s choose transactions. We can drag it over to replace the metric. Or we can continue this process to make more scorecards. For this, I’d like to have product detail list. Drag and drop, and we’re done. Next one is a revenue.

And I’m going to change this one to show a compact number. Next up average order value. So as you can see, I can just type in here to search for all the metrics that are available in my data set. It’s a drag and drop them to the left to change the metric. Now let’s create some room for our final metric which is ecommerce conversion rate. I’m using my keyboard to move the scorecards around. And I can even control C and control V on my keyboard to copy and paste and create a new ecommerce. Now let’s see from which countries are we getting our revenue from. For this purpose, I’m going to add a map to the report. Now each shade represent the number of sessions, but we’d like to replace it with revenue. See which country bringing more revenue to our commerce store. And just to make it clear for the end user that what metric is being represented by this graph. Let’s add a title and call it revenue by country. Next, let’s see some trends. By adding a time series charts to our report. Just like the scorecards above, I’m going to give it a border and also change the radius for the corners. Let’s decide what numbers do we want to show on this chart. I’m going to head over to the Data tab and search for some metrics. I want sessions to be either, but they also want to see product detail we use product adds to cart and transactions.

Let’s duplicate this and create another chart for revenue. Right click and select Duplicate. Ok. So now I’m going to remove the metrics and replace the final one because I’d like to see the trend of revenue over time. But for this chart, I’d like to see how revenue builds up over time during this time period. Now if you’re going to duplicate this and create not a chart to show e-commerce conversion rates over time. I’m using my keyword to move this around. Just like before, you can head over to the Data tab, search for metric and replace the metric on the chart by dragging dropping the new metric over the previous one. For this one, however, I don’t want it to be cumulative. So uncheck this but I do like to see a trend line. Cool, isn’t it. Now let’s add a pie chart. To see the distribution between male and female users. Drag it over here choose gender as the dimension for this pie chart, and we can leave the metric to be the number of sessions. I’m going to head over to style tab. But I want to move the legend over the top just to make some room for the next visualization we want to create.

Now let’s use this space to create another chart and see which cities are we getting the most revenue from. For this purpose. let’s choose the bar chart, the horizontal bar chart. Move it here, resize it to fit, adjust the size to make room for the name of the cities, changed the metric to revenue. Premium sessions and change a dimension to city. Our ultimate commerce reports is almost finished. Let’s see how does it look for the end user. Looks nice, doesn’t it. But there is a problem. This report is a static and the user cannot interact with it. They cannot choose the time period or take a look at different segments of data. So let’s go back to edit mode and add some features and interactivity to this report. The first thing we’re going to do is to allow the end user to change the time period after report. And this is done by adding a date control filter just like this. Going to head over to this time tab and change some colors to make it visible. Now, the reviewer of the report can easily choose the timeframe for which they want to look at this report just like this. Now let’s go back to edit mode and add some more cool features to this report. I’m going to select these all, bring them down to make some room for the extra features we’re going to add.

First, we’re going to add a drop down menu that allows users to take a look at traffic from different source mediums. This is done by adding a filter control to report. By default it sets to medium as its dimension, we’re going to change it to source medium. Again, we’re simply dragging and dropping. Next, I’m going to duplicate this. Create another one for device category, which basically means desktop, mobile or tablet and allows them to look at traffic only from desktop devices, the tablets or mobiles. And finally, I’m going to duplicate again and allow the end user to filter my user time. Which basically means is that the new user or a returning users. Now let’s review our report again and see how does it look. Now it’s possible for the end user to filter this report base on any of these criteria. For example, they can choose to only look at desktop traffic. And all the numbers and charts will be updated to reflect the choice.

Now that we created this ultimate ecommerce report together, it is time to share it with others. Let’s take a look at different sharing options we have access to in Data Studio. The first option is to download the report as a PDF form. And we can even protect it with a password. The next option is to set up automatic delivery of the report via email. So Data Studio, emails a PDF version of this report to the email addresses that you choose on a daily, weekly or monthly basis. We can simply create a link to this report. Or we can share it with other people, just like any other Google Drive document. Just like a Google doc or Google Sheet, you can get a shareable link, or you can share this report with the specific emails. So as you can see, there are lots of options for you to share a report with the people who need to review it. Okay, I hope you’ve enjoyed today’s video, in which we learned how to use Google Data Studio to connect to a data source and create a beautiful, interactive and fully functional report and share it with others in about 15 minutes. I’m going to post a link to this report in the descriptions. So you can review it and grab a copy for yourself to play around with. To make a copy of the report inside your own Google Data Studio account, simply click on this little icon. That’s it for today. Thanks for watching.

All right, so there you have it. This is a quick overview on Google Data Studio, how you can use it and hopefully you know now if you should use it for building your dashboard. It’s pretty easy actually. But there are some quirks of it. So I also encourage you to check out our playlist which I’ve linked up right over there which will show you a few more details of Data Studio and a few more specific details on how you can utilize the tool for effective dashboard building. And as always, if you haven’t yet, consider subscribing right over there to our channel because we bring you new videos just like this one every week. Now my name is Julian. See in the next one.

Most Important Chart Types in Google Data Studio (feat. Ahmad Kanani)

Google Data Studio has several chart types thus allow users to create and design their reports in unlimited ways. In today’s video, Ahmad of Siavak is back, this time to show us the 6 most significant charts in data studio. Plus, he will give us some tips and tricks on how to design your data visualization dashboards more efficiently.

? Links:

Siavak –

Google Data Studio –

Chart Types Reference –


In this video, Ahmad is going to introduce you to the most important chart types within Data Studio. And he’s going to give you some tricks on how to build more effective dashboards in Data Studio. All and more coming up.

Hey, there measure geeks. Julian here back with another video for you. Today, we want to talk about Data Studio and data visualizations. Now, you’re seeing we are remodeling right now our little studio here and I’m thinking what should I put in the background? Let me know in the comments down below. Today, we gonna talk about Data Studio. And this is a quite new tool in a Google universe. So it’s always evolving further and further. There’s always stuff I learned from different people. And today we want to learn from Ahmad who’s going to show us the different chart types within Data Studio. He’s also going to give us his tricks on how to become more effective in building a dashboard. He’s actually going to build a very small dashboard in this video

in a very short amount of time. Now we got lots to cover. So Ahmad take it away. Thanks, Julian. This is Ahmad from Siovak. And today I’m gonna show you 6 useful charts in Google Data Studio and we are gonna create this simple report dashboard together. Okay, let’s begin. First, I’m going to create a new blank Data Studio report. And I’m going to create a new data source for it. For this tutorial, we’re going to use Google Analytics and we are going to connect to Google Analytics demo account.

Let’s choose the Master View. And connect. It takes a few seconds, and then you can add it to your report. Click Add to report. And we are ready to go. The first data visualization side we’re going to create is a scorecard. Scorecard is best to show KPIs with key performance indicators. To do this, let’s go and add a chart. Choose a scorecard.

Resize it a bit and position it our canvas. As you can see the scorecard chose a metric, we can change the metric, for example, to sessions.

We can drag and drop sessions over page views for it to change. We can see the date range of this metric. So this is the number of sessions over the last 28 days. And we can also have a comparison to the previous year, previous period, or a custom time period. I’m going to choose previous period and hit apply. We can also filter the scorecard to show the number of sessions only for segments of our audience. To do this, we can add a filter.

Let’s for example, say viewers only say country equal to United States. Hit Save. Now it reloads to reflect the number of sessions from users from United States. We can also change the name and title of the metric to US only sessions. So it’s clear what’s number is a scorecard is going to represent. Let’s revert it back to default. And remove the filter. Because I’m going to show you at the end of this video, a really cool trick and a new feature of Data Studio that is actually more useful. The scorecard right now is pretty basic. So the next thing I’m going to do is to apply a bit of a styling to the scorecard. We can go to the style tab, choose a background and border and border radius. For background, we can use a solid color, or you can use a gradient from top left to bottom right. And let’s choose from white to a light gray.

Let’s change the border radius to four and add a light gray border as well. Okay, this looks much better. Now let’s copy and paste it to create three more scorecards. So control C and control V to create another one. For this, I’m going to show revenue.

For the next one, I’m going to show transactions. And for the final one, I’m going to show e-commerce conversion rate.

That’s it. Now, let’s say we want to see the trend of sessions over the time period of last 28 days. This is only the total number. But if you want to see the trend, we can use another data visualization chart, which is a trend line. We can either add a trend line or time series directly from the menu. But because I want to keep this styling, I can do this from another way. Let’s see, I can copy and paste this to create a new one. As you can see, the metric is still sessions and the comparison time period is set. I make it a bit bigger to come here to this menu and change the type of the chart from a scorecard to time series chart. That’s it. So we have our time series here, which we can adjust. It’s already showing sessions during the timeframe of last 28 days. Now let’s create another type of visualization that shows us the top five acquisition channels that send traffic to this website during last 28 days. I’m going to copy and paste again because I want to keep this styling. And now I’m going to change the charts type from time series to a horizontal bar chart. Let’s close this. And for the dimension, I want to use default channel grouping,

which is the acquisition channel for these sessions. I want to sort it not based on the name of the default channel grouping, but based on the number of sessions descending. And then in this time tab, I can choose to show only the top five channels. Now I’m going to make it a bit smaller and resize it a bit. That’s it, we know the number of sessions, we can see the trend of sessions over time. And we can see the top channels that have been sending these users to our website. Now let’s say we want to know the geography of and the countries that are sending traffic to this website. I can copy and paste this again, resize it a bit to make it bigger. And then I can change the type to a geo map. The dimension automatically changes to show the country and in shows the number of sessions per country based on you know the shade of the blue color. The darker the country, it means the more sessions we had from this country. The next chart I want to create is an area chart. An area chart helps us to see both the channel contribution to the traffic and the trend of users over time, we already know what to do copy, paste. And the reason for copy pasting is just simply retaining the styling. Otherwise, you can come here and add a area chart quite easily. And once here, we can change it to this area chart. For the time dimension we’re using date, of course. And for the breakdown dimension, we’re going to use the default channel grouping.

Here, we can also go to this style tab and change the number of series to five. Because we want this to match to the bar chart above. As you can see, we can now see boosted trend of sessions over time, and also the distribution of the default channel grouping per date. Next, let’s see some demographic data. We’re going to use a pie chart to show the ratio of male and female users to this website. Let’s copy and paste the bar chart and change it either to a pie chart or a donut chart. They’re basically the same, they only look different I like the donut chart. Let’s make it a bit ticker. And in data, let’s choose gender as the dimension of this pie chart. Now also, I’d like to come to the style tab and change the position of the legend. That’s it. Congratulations our simple dashboard is ready. We can preview the dashboard. Or you can also share it with your client or the end user. If they hover on any piece of the pie, or bar, or any data into the top online, or any country, they can see the actual number of sessions or any other metrics that you have on your visualization. But other than that, it’s a static report, they cannot interact with it. So if they click, nothing happens, it’s basically stays the same. So now it’s time for the cool trick that I promised to show you. Let’s go back to edit mode, we want to make this dashboard a bit more interactive. Let’s start by making the pie chart interactive. You can select the visualization widget. And on the Data tab at the end, we have interactions, and we can choose it to apply filter. Now let’s see how it behaves. Now that we’ve enabled the interactions for this pie chart, if we hover and click on any piece of the pie, every other widget in this dashboard will get updated to show us the data for that segment only. Let’s try. Now every other widget on this dashboard is updated to only show the number of sessions for male users. We can click again to reset it and go back to default. Now let’s go back to edit mode and enable interactions for the rest of the widgets, for the map, for the area chart and for the timeline. And so we have a totally interactive report dashboard. We can click on organic search to see data all for organic search which is coming up with an error. I don’t know why. We can click back to reset, we can click on the US to only see the other data for US users, just like what we did with the manual filter or the sessions at the beginning, and then can click back to return it. We can also apply two or more filters at the same time. So let’s say female users from United States. Let’s click again and reset. So we have two types of filtering and a report interaction Data Studio. The first one is filtering which is clicking on a segment on a graph to filter the rest of the widgets on the same report dashboard. The second one is called brushing, which is the selection of a time period on a chart like time series on an area chart to update the rest of the widgets in the same document to only represent the data for that time period. You can select the date, or any time period that you want.

That’s it. So to recap, today, we learned about six most useful charts and Data Studio and how to use them to create a simple report dashboard like this and it looks good and beautiful as well. We bring scorecards, time series, bar charts, area charts, a map and a pie or donut chart. Plus, we learn how to make our dashboard interactive for the end user. Now it’s your turn, go make some cool dashboards and share the links in the comments section. Thanks for watching and good luck.

All right, so there you have it. Thanks Ahmad for this quick introduction to the different chart types and how you have actually built this dashboard in such a short amount of time. I’m still amazed by this trick of duplicating your data visualizations and then there’s changing the chart types so you don’t have to do all the styling over again. Something I will keep in mind the next time I build a dashboard. What have you taken away from the video? I’d love to hear from you in the comments down below. And if you haven’t yet, then maybe consider subscribing right over there to the channel because we bring you new videos just like this one every week. Now my name is Julian.

See you in the next one.

? First Look: Data Blending in Google Data Studio

Google Data Studio Data Blending lets you combine data sources in one visualization. Let’s take a look at the new Data Blender together and see how the new feature works.


? Links:

Google Data Studio Tutorial

? Learn more from Measureschool:

?Looking to kick-start your data journey? Hire us:

? Recommended Measure Books:

? Gear we used to produce this video:


In this video, we’re going to take a first look on the new data blending feature in Google Data Studio. All and more coming up.

Hey there, welcome back to another video of teaching you the data driven way of marketing. My name is Julian, and we are live right now talking about the new Data Studio feature of data blending. Now, if you are aware of our other tutorials we did on data studio, you might know that we took the work around at that time, at least to pull in data to Google Sheets blend it together and then importing it into our Data Studio dashboard. This gave us a lot of flexibility. But at the same time was a little bit inconvenient. Now Google has done something about it, or at least the Google Data Studio Team, because they have announced the community connectors that actually let us pull in data from different data sources, then the Google products into Google Data Studio. So we can now pull data through third party connectors, like super metrics directly into Data Studio with these new functionalities of these data connectors. And that is important for Facebook ads. Now,what we are not what we were not able to do is actually take that data and then blend it together with other data sources. What do I mean by blending? Well, if you wanted to have data from Facebook ads, and Google Analytics in one table on one visualization, that was not possible to do within Google Data Studio, you would still need to go back, for example, to a third party system like Google Sheets, or a database blend the data there together and put it then into Google Data Studio. This has now been fixed with a new feature of data blending within Google Data Studio and we’re going to take a first look. So without further ado, let’s dive right into our little demo here.

So I’m here at Data Studio, let’s just come up with a completely new report. And at the beginning, we are asked to choose our data sources. Now, I’ve already connected my facebook account and my Google Analytics account, I actually want to just demo this. And let’s find out how many clicks we had on our Facebook ads campaign. So I’m adding this to my report. And we get our familiar Canvas em, now I will work with dates. So I’m going to just put this date picker right here and select the range,let’s go to some old data we have in the system from November,let’s go with the 15th year. Okay, so this is already pre put in here. Now, the next thing I want to do is actually make a table. And in this table, I want to show my Facebook ads not by campaign name, but actually by that date. So up here, we can choose our dimensions and our metrics, what I want to do is choose the data dimension, so we have appear time and this the super metrics connector to Google Data Studio, that you can then connect to your Facebook ads account, I have done this in this case, we are simply go with the date dimension. And as you might know, dates are in a in a spreadsheet, they are really the columns that you put in here. Now for the rows, I actually want to not have impressions here, I want to show the actual clicks that we had on our campaigns. So I’m going to go here to campaign and go with the link clicks.

And let’s get rid of, well, we can leave in the impressions doesn’t really matter. Now, what I want to do is actually know how many people converted. What I can do from my Google from my Facebook data is, obviously if I use the conversion tracking of Facebook, I can put that in as well. But Facebook will always give me different data, maybe that will be a great thing to actually compare if I can find the right metric here. Because as you might be aware, the website conversion value, Facebook API gives us a lot, a lot of data to look into. And I don’t, I’m not quite sure how I tag this, if this is just a custom conversion.

Let’s see if that does the trick. Yes, we have custom conversions here. So this is what Facebook actually records from the facebook pixel. Now, I want to compare this with Google Analytics data, right? Google Analytics has a different attribution modeling going on. Because you might know that Facebook is really looking just at how many people come to their website, click or come to the website, and then convert and they look back, if there was any contact point with Facebook, it will be attributed to Facebook. And Facebook will show that Google Analytics is different to that because its last click wins, or the last source that brought the traffic to your website, and then converted how many people that and how does that compare to Google Analytics. Now, I could do this in spreadsheets, obviously. But for demonstration here, we want to actually blend this data with our Facebook Ads data. And there is this new functionality here in the data sources where we see blend data. And I’m going to click this,  this opens up this new menu down here, where we have our data sources. And we can blend multiple data sources to each other or into each other I guess. The data source that we are predominantly using right here is Facebook ads, this will be our primary data set, I’m going to add a data set to it. And available sources here, I’ve already connected this is my Google Analytics account. So I’m going to add this to the report.

Now, we have two reports in here. And we want to join this. Now, in order to join data with each other, you will need to have a Join key. Join key and databases, they’re also called primary keys are date metrics that you have in both data sources that are aligned to each other. So in our case, it would be the date obviously, the data is not is in Google Analytics and in Facebook ads, and it would atch that up correctly. What you could also do, if you have tagged your UTM parameters in your Facebook ads correctly so that through, for example, the source medium or the landing page so, you need to have a join key in order to align this to the data sources together. And we have date here so that is all fine. Now the last thing I want to do to make this a little bit bigger is to actually add a metric to this, now that that data is aligned, we can add the metric. And in our case, I want to just take a goal completion on my case, it would be the email sign up, find the right one here. That is the goal completion. Yes. And we’ll just drag that in. Let’s save this and see what it does for our table. Now we have our email signups in here. Now, you might notice that this is kind of screwed, because we have that many link clicks. And we have so many email signup. So it’s much higher than we would expect here. For the clicks that we are getting on this day. Maybe it’s much higher, it’s actually a little bit beneath it. But what you always need to keep in mind is that when you pull data from a second data source, that doesn’t mean that it’s automatically filtered based on the data source that it’s connected to. So in our case, we actually would need to say, or these email signups that we see right here are email signups that are originated from maybe different sources that came into our Google Analytics account. These are the totals of all goal completions on that day, just added to this, this table here. And therefore, we need to go in and actually implement a filter. So we can add a filter here. And we’ll just call this Facebook traffic. We want to the data sources master and only include we have here our dimensions, let’s go with the source medium and condition should contain I think that’s what I entered as the as the UTM parameter so that should be correct. Let’s just save this, save this again. Now our data should be filtered down, or at least that column of email signups to only the Facebook data. So here we go, we see that it’s much lower, and the sources have been attributed differently. So we can look at what Facebook actually says it generated 71. What Google Analytics says it generated from our Facebook source quite interesting to see. Now if I would be honest, I would like to know the conversion rates, right. So I would like to put in a another column here saying what is the conversion rates between the link clicks and a website conversions. This is easily done for a native data source. So if we have Facebook ads, just as a data source or just Google Analytics, we could build a custom metric or a custom calculated metric from this. Unfortunately,this is not something you can easily do or not something I found in the interface at least to be something that you can do in the blender data form. So once you use blender data the custom metrics out of the play you can’t know the you can’t calculate the link clicks, conversion rate to the email signups of goal completion for. That said,probably something that they’re gonna fix at some point. For now, if you really need to do this, I guess you would need to go to something like Google Sheets Connector again, and do this first and Google Sheets and then import the data.

But overall, a pretty interesting feature that they have added and it was something that people needed. It also just simply breaks up the whole data silos, silos, right? You have data silos. Now you can import them into one dashboard. But they’re still silos in itself. But now you can blend them together and have much more interesting insights, I think in terms of comparing data, putting it together from different systems. That is really the power of building a custom dashboard. Then just looking at a dashboard and Google Analytics or on Facebook ads, this is something that we really needed and it’s now implemented into Google Data Studio.

Alright, that’s it for this little demo. If you have any more questions, then please leave them in the comments below. We also have new videos coming out all the time and live streams so be sure to subscribe to this channel and also check out this video.

My name is Julian. Till next time.

? First Look: Pivot Tables in Google Data Studio

Pivot Tabes in Google Data Studio give you the ability to display your data in a table with multiple dimension at the same time. This gives you the ability to use Data Studio for Data Exploration but also it gives you a new ability to display your data in this Dashboarding Tools.


? Google Data Studio Connectors (for Facebook Ads and more…)

Let’s chat live about the new Community Connectors announced for Google Data Studio. We now have the ability to Facebook Ads and more directly to Data Studio, which opens up a lot more capabilities.


? Links mentioned in the video:

Offical Announcement:
Ben Collins:

? Learn more from Measureschool:

?Looking to kick-start your data journey? Hire us:

? Recommended Measure Books:

? Gear we used to produce this video:


? Data Studio Course: Summary und Live Q&A | Lesson 4

We have build a Dashboard. Let’s summaries our approach, see some more resources about data visualization and answer your questions Live.

Watch the full Course:

Q&A Section:
19:32 How to change the default Week day start?
21:40 Alternative to Supermetrics?
27: 40 One Report different sources?
28:57 Report Drop Down menu for sources?
30:29 Best way to compare 2 date ranges?
31:49 Best report for overall performance?
34:34 Connect DataStudio and Google Optimize?
37:35 Supermetrics is it an Agency license?
39:49 What data is available in Supermetrics?

? Links mentioned in the video:
Data Vizualisation Books:
Ben Collin’s Course:
David’s course:
Data Studio Gallery:
Tableau Gallery:
YouTube Channel: DataSaurus Rex
Stay up to date with Data Studio Changes:
Lea Picas Present Beyond Measure Podcast:
Facebook Ads Manager for Excel:


? Learn more from Measureschool:

?Looking to kick-start your data journey? Hire us:

? Recommended Measure Books:

? Gear we used to produce this video: