Google Data Studio Beginner Guide

So you want to build dashboards that inform decision making, lead to more insights and inspire your audience to take action? In this beginner guide I’m going to show you how to build a dynamic data dashboard in Google Data Studio.

Planning your Dashboard – Lesson 1

Don’t start implementing your Dashboard without having gone through some preparations. In this video we’re going to go through the essentials steps to prepare your implementation of an effective Dashboard.

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1. Exploration
2. Definition
3. Sketching / Wireframing

Links mentioned in the video:
Worksheet: https://measureschool.com/dashboardplan
Google Data Studio: https://www.google.com/analytics/data-studio/
Google Sheets: https://www.google.com/sheets/about/
Supermetrics: https://supermetrics.com

In this lesson we are gonna talk about the preparational steps that you need to undertake in order to build an effective dashboard. We’ll go through some questions you need to ask your audience, assemble all the different metrics and dimensions you want to pull from your tool and sketch out your different visualizations that you later want to implement in Google Data Studio.

All and more coming up right after this. Alright. In this course we’re gonna go through a three-step process in order to build our effective dashboard. First of all, we go through the preparation steps which we’ll do in a second here in this lesson. In the next lesson we’re gonna talk about the data setup and get our data sources ready so we can then in the last lesson visualize our data in Google Data Studio. But for now we are just gonna go through the preparational steps and these are very important. What does this actually entail? Well, before you begin implementing anything into different tools, you might want to ask yourself a few questions and this really goes into the exploration phase, what kind of dashboard are you trying to build, talk to your target audience, look into how they are analyzing their data maybe nowadays and what your dashboard will bring to the table. Then we’ll go into the definition phase where we’ll actually look at all the different metrics, dimension data and the sources that we need to have ready in order to build our dashboard and once we know what data we want to visualize we can actually start sketching out our wireframing, our dashboard on paper or in a wireframing software. So, let’s go a little bit more in depth into these steps. So, first of all, in the exploration phase you would ask questions. Now, who’s this data dashboard actually for? Be very clear about your target audience and then actually talk to your target audience, what they see the dashboard actually doing for them, what’s the goal of the dashboard, why do they want this dashboard to be built? How should this dashboard make their lives easier? Now if it’s for example about data analysis, then you might want to dig deeper and find out how they are doing data analysis without the dashboard at the moment. How can it actually save time? What data points are they looking at? What analysis are they going through? What are the different steps that they’re going through in order to get to insights. Now, all of that feedback should be taken into account once you conceptualize your dashboard so your dashboard actually fulfills its purpose for the target audience. Now, this might look like, in our case, take the Facebook optimizer that actually you would ask for questions asking what are your most important KPIs? What do you want to have on this dashboard. When you log into Facebook in the morning what steps are you going through? Why is that frustrating? How can that be solved through a dashboard? And you might get answers just like these that actually tell you a lot about what this dashboard should accomplish for the client, for the audience, for the user and that’s also how the dashboard can be really meaningful to the user because it actually saves some time and you’re actually using it in order to make decisions or get a quick overview in order to know where to dig deeper. So, this Facebook optimizer would maybe say I need an overview over my KPIs, I’m really going through these steps in order to optimize my campaigns, I’m looking at that data, I’m doing this analysis, and if the dashboard would help me to do this all faster, then it would be something that I would use. Now, there’s nothing worse than putting time into building an awesome dashboard that nobody uses. So, talk to your target audience and find out what they want on this dashboard before you start implementing anything. Now, once you’ve gone through that phase, you need to actually get some definitions straight. From the interview you probably elicited different metrics, different dimensions, and different sources where this data comes from and this is something that you need to write down in order to be ready for the next part when we pull the data actually from the system and prepare it in Google Sheets. If you don’t prepare that beforehand, first of all, won’t get clear on your visualization but may also find yourself going back and forth between your data extracts, the data that you want to display and the actual visualization which can be very distracting and not very efficient. So, get clear about the metrics, so for Facebook ads dashboard you might want to know how many clicks, how many impressions, what was the click through rate, what was the ad spend and so on. Put that all down onto a sheet of paper or into a document, so you’ll know what to look out for once you prepare your data in the next step. Now, once you have all the different data points that you want to visualize, it actually comes down to thinking about the visualization. How can I tell a story with my dashboard? And I’d recommend that you actually take time to look through other dashboards, look through other implementations of Google Data Studio dashboards, as for example the Google Data Studio gallery that you can see different visualizations from but also ask yourself how useful is this for my dashboard? Does it actually make sense to display the data in that way? Will it help my target audience to do what they want to do with the dashboard, for example, get you insights or keep a clear overview? Don’t overcrowd your dashboard and actually you can also look through other dashboards of other companies, of other tool vendors. There are also great examples that we will link up in the description below that you can check out and get inspired before you start actually sketching and wireframing. Now, for our example in this course I went ahead and built a little sketch in a tool called Balsamic. You can check that out in the description below as well, where I thought about okay, what story do I want to take my user through? What would be the visual layout? How would we segment the data later on? What is important to my target audience? And this is where I came up with this little sketch here where I said I want to have on the top my control panels where I can choose the date but also dig deeper into a campaign, then we would have our most important metrics in the first row and in these columns we would have the acquisition, the actual costs and the conversations, so we get a quick overview on how that is doing. Then we’ll go through and answer the most important questions such as how is this stacking up to my ad budget, my targets that I have in place already? Give some more context on the click versus cost per click price, the spend versus the revenue, we also probably also put in the whole return on investment metric and then down below we would have some more analysis in form of a table where the user can really dig into what campaign didn’t do so well, what was the driver of the growth of my campaign, or to the client and which ad sets should I be looking into further? Remember, the real goal here was to give a quick overview for a Facebook ads manager on how the performance of the campaign is doing and maybe also giving some points in order to ask more questions, dig deeper into the data, go into the Facebook ads interface and optimize his campaigns. So, I’d recommend for anybody to sketch out or wireframe your dashboard beforehand and get very clear about the visualizations that might make sense for your target audience to reach their goal. Alright, so there you have it. These are really the essential preparational steps that you should go through to build an effective dashboard. Now, if you want to think through this by yourself for your client or the dashboard that you are building we actually prepared a worksheet for you that you can download at measureschool.com/dashboardplan and there you can fill out this worksheet and really get clear on the message that you want to convey to your audience. And if you’re ready to proceed, then head over to the next video right over there and if you haven’t yet, then consider s

Combine Data Sources in Google Data Studio – Lesson 2

In this lesson we’re going to setup our data sources in Google Sheets with the help of Supermetrics. We’re going to pull data from Facebook Ads and Google Analytics and prepare it to later be used in Google Data Studio.

Google Sheets combined with Supermetrics is a great way to control data that later goes into our data dashboards – it gives you the possibility to change data around, clean it up and even combine data sources (which is not possible in Google Data Studio itself).

Links mentioned in the video:
Google Data Sheet: http://bit.ly/2tUX3EF
Full Playlist: http://bit.ly/2hkwUx4
Coding is for Losers: http://codingisforlosers.com/
Ben Collins: http://benlcollins.com/
The Dashboard Plan: https://measureschool.com/dashboardplan
Google Data Studio: http://bit.ly/2bcb7zt
Google Sheets: http://bit.ly/1GAUvK5
Supermetrics: http://bit.ly/2hmxvOR

In this lesson, we’re gonna set up our data sources in Google sheets with the help of Supermetrics. So we’re gonna pull the data from Facebook ads and Google Analytics. And then prepare it so it’s ready to be used as a source within Google Data Studio. All and more coming up right after this. Welcome back to this course. So in this lesson we’re gonna prepare our data sources in Google sheets. Now why do we go the route of Google Sheets as a source rather than all the other data connectors that are available within Data Studio. Well there are multiple advantages, but the first reason being that Google Data Studio doesn’t have a connector for Facebook ads yet. I don’t know if they’re gonna implement it at some point. I hope so, but it’s a Google product. So maybe they’re gonna leave themselves some time with the Facebook ads. But there is actually a tool that we can use in order to pull the data in and that is Google Sheets and the combination with Supermetrics. Now Supermetrics is a third party tool, an add on that you can install to your Google sheets account and then you can pull data in from various sources. They have many more data connectors than even Google Data Studio has and it’s a very versatile tool, and we’ll get into that in this lesson. But that’s not the only reason. Google Sheets gives us the opportunity to work with our raw data that we get from the tool like Facebook ads or Google Analytics before we then import it. We can actually change the data around, we can clean it up and most importantly, we can combine data sources. So that’s not possible within Google Data Studio itself to combine different data sources and then calculate metrics from that. So really, with the sheet, we have much greater control about the data that actually goes into our dashboard and if you run into any issues, we can investigate our data sources first and find out the error there. And that’s really why I love using Google sheets as this bridge between our raw data that comes from the tool and Google Data Studio that we will use for our visualization. Alright, enough said. We got still lots to cover in order to make this work. So let’s dive into today’s lesson. Alright, we will start out with a new Google sheet. And let’s think about what we actually need to make this dashboard work. We need our metrics and dimensions. Now just a recap, metrics are basically the numbers that you will have in your table later on, and the dimensions are the different properties that you want to divide your data by. And the table metrics will be represented by the different rows here and the metrics that would be in those rows. And the dimensions would be on top and represent the different columns. That’s important to think about, so we pull the data correctly. Next up, where do we pull the data from? Let’s go through a little bit of an exercise here. Let’s look at these metrics and decide where we would get them from and the impressions would come from Facebook. The clicks, as well from Facebook, then the CTR would actually be calculated. So this will be a calculated metric. The ad spent, as well from Facebook. The CPC, as well calculated, as well as the CPO. Then we have the revenue, which would actually come from Google Analytics and the conversions, also from Google Analytics. Now this is actually an attribution topic here. If you trust more the attribution of Google Analytics, then you want to pull that into your dashboard and we will do this in our example. Next up the return on ad spend, which is also calculated at the end. The ad budget, which will be put in manually. As well as the target CPO and the target CPC. So now have a good overview of what metric comes from where and what data we have to pull. Let’s talk about briefly about the dimensions because dimensions are the different columns that we want to pull. Now, these are obviously heavily based on our Facebook campaign. We have our date, campaign name, ad set, and ad name. But as we have seen, we want to also pull data from our Google analytics account. Now how would that be represented in Google Analytics? We may have the date available but not the campaign name, ad set and ad name at least by default in Google Analytics. And that’s what we need to actually connect both tools together in order to have all these dimensions in both tools. And that is done through UTM parameters. Now if you’re not familiar with UTM parameters, we have another video on that. But basically you need to make sure that when you input your campaigns into Facebook, you need to be clear on the campaign name, campaign content and campaign term that you would choose beforehand. Then you can go ahead and tag your link. So in this sheet, we have a link that we prepared in order for our campaigns to be tagged correctly and then when the user clicks on that link or goes to that URL, he will be automatically registered in Google Analytics with these campaign parameters. So make sure in Facebook, in your ads campaign, that your campaign name, your ad set name and your ad name actually, is tagged up correctly. So there’s an option for that which is called UTM parameters. You need to have these UTM parameters attached to your link in order for Google Analytics to register this correctly. So once the user clicks on the link, it will be registered in Google Analytics itself. So once you go to your source medium reports, you have here Facebook CPC. And then we would have our campaign name. So let’s put that in as a secondary dimension. This is called campaign in Google Analytics. Then we would have our ad content, which is the UTM content parameter. So here we have interest brands. And we would need to have our keyword, which is our UTM term, that we also fill with these parameters. So again, you need to make sure you tag them up correctly and send the user to the right link in order for this data that we want to break our metrics down by is available also in Google Analytics. So this actually only works if we have these dimensions in both Google Analytics and Facebook. Now in Facebook, these might be called date campaign name, ad set and add name. In Google Analytics itself, these would be called date campaign, ad content and keyword. Different naming convention, but essentially we should get the same data in Facebook when we query for this then in Google Analytics for these different parameters. So be sure you have the same data so you can connect it in your Google sheets later on. Alright, now that we are clear on what we need to pull from where with what dimensions, let’s go ahead and prepare our data sources. I’ll open up a new sheet here and call this Facebook data. And the second sheet called Google Analytics data. Now we can go ahead and pull our data with the help of Supermetrics. Now if you’re not familiar with Supermetrics, you can actually install it by going under the add-ons and go to get add-ons, and simply enter Supermetrics, and you can install it to your account. Now the capabilities that were used within Supermetrics are a paid feature, so you would need to upgrade your account. But for anybody who’s trying this out, you can use the Pro features for 30 days. So once you have it installed, you can go to add-ons and then on the Supermetrics, launch the side bar. And now you will be able to configure your query. Alright, first up, we will choose our data source. Now, Supermetrics has many data sources available and therefore it is more powerful than what the building capabilities of Google Data Studio actually provide. So you can pull in a host of other data into your dashboard if you choose so. And we will go obviously with our Facebook ads account. Now we need to enter our Facebook details. So you need to go through the process of registering this with Supermetrics, so it can pull the data into your account once you have that you can choose your account and then select the account, if you have multiple accounts, under you log in, then you can choose your different data sources and different accounts that you want to select. And we go on to actual date selection. Now how much data should be pulled here? There are differ

Creating Your Data Studio Dashboard – Lesson 3

Facebook Dashboards can be easily built with Google Data Studio if you have prepared the data correctly. In this lesson we are going to visualize our data set and build our Dashboard complete with date control, filters and calculated fields.

You are going to learn how to calculate custom metrics, change their metrics name, layout your dashboard, build the visualizations required, build filter and date controls and make it all look pretty.

The template can be found at https://measureschool.com/facebookdashboard

Links mentioned in the video:

Full Playlist: http://bit.ly/2hkwUx4
Google Data Studio: https://www.google.com/analytics/data-studio/
Google Sheets: https://www.google.com/sheets/about/
Supermetrics: http://bit.ly/2hmxvOR

Alright, so now that we have prepared our data in Google Sheets, we are ready to visualize it. We’ll first calculate our metrics, then lay out our dashboard, and then, finally, implement our data in Google Data Studio. All and more coming up right after this. Today our journey starts in Google Data Studio where we’re finally gonna visualize our data in our dashboard. Let’s start out by clicking on a blank sheet here, blank new sheet, and then choosing our data sources. So we can create a new data source and we’ll choose our Google Sheet. Now, from our Google Drive account, we’ll pick the sheet that we had prepared in our last lesson. Let’s do that. Now we can see that we can actually choose the different worksheets as a source. This is actually something that is unfortunate because you can only choose one worksheet as one data source. So you couldn’t say okay, I wanna choose the GA data from here and the Facebook data from here, and combine it all into one data source. No, you need to have them on one sheet in order to make this work. That’s why we prepared the data this way. Okay, let’s go over to Google Sheets and choose our combined datasheet, and then we can choose under the options to use the first row as headers. That is fine with me. If you look at the datasheet, this would describe our data, so we can pull that in. And we can include any kind of or filtered cells, which is not the case in our sheet, so I can untick this. You can also choose an optional range if you have data on that sheet in a special range. This will be all for us under the configuration. Let’s connect this all. And we will pull in our different dimensions and the different metrics. Now, as you might remember, in our last lesson we actually prepared that and looked into the metrics and dimensions that we want to pull. Unfortunately, first of all, these are not exactly the same name that we want to display them on the actual dashboard, and therefore I would recommend to actually rename them right here. It’s pretty easily done. For example, link clicks should just be clicks. You can just click on that and change the name around here. The amount spent will just be our ad spent. Our transactions are just our conversions, and our transaction revenue is actually the revenue. The campaign name, let’s rename that in just campaign, and the ad set, and the ad name can stay the same. All right. So now we have renamed our dimensions and metrics, but we also wanted to calculate some metrics. So for example, here’s the CTR which is impressions divided by our clicks, and we can easily input that by going into Google Data. So we’re clicking on this Plus button, and it will let us input a name, which in our case was the CTR, the click-through rate, and then the formula would be impressions, and it already pops up, our impressions from down here, divided by our clicks. Alright, let’s create this field, and it’s now part of our dataset. With calculating metrics, these are custom, so Google Data Studio doesn’t always know what kind of type it is, and this is actually a number but it’s actually a percentage. So you need to make sure that you choose that correctly. We’ll auto-aggregate that, that’s actually something we can’t really change. So let’s go ahead and do our other calculated metric, CPC. CPC is the cost per click. So let’s click on the Plus button here, CPC, and this would be the ad spent divided by the clicks. Alright, create this field. This is a number, that’s correct, but it’s also a currency, so we can choose that as well. Let’s pick US dollar here. And we have all CPC metric. Let’s go ahead and calculate the CPO, the cost per order. So let’s click on the Plus button, cost per order, that would be the ad spent divided by the order transactions. We called it conversions. All right. Create this field. And we have one more left, return on ad spent. Return on ad spent. This would be our ad spent divided by the revenue. Alright, that should do it. Now, we have a few more left that are actually manually inputted. This is something we could put into our sheet but it’s actually easier to update it manually in our sheet. Because sometimes Google Data Studio can actually pull the data from a sheet for certain cells. Okay, we have our data sources ready, let’s add this to our report. And here we go, now we have that available in our data sources if we choose to visualize anything. Right we you see our data range and our metrics, these are now available here. Okay, before we start out visualizing anything, let’s get clear on what we are trying to do. Fortunately, we have prepared a wireframe in our first lesson, so we know what we want to build and have an idea of what elements should be on our dashboard. So on this dashboard we have kind of like a three column layout here, and three columns here. Let’s mock that up really quickly in Data Studio, and we can choose the rectangle tool to do all this, just so we can get the proportions right. Because we don’t wanna mess with all the layout later on once we have our data together. This will make this whole process a whole lot easier. So let’s just mock this up. So now we have laid out our information, what goes where, and this is actually also a great template that we could use later on if you would like to build a new dashboard. So you can go ahead and actually make a copy of this and rename this in some kind of fashion to template so you have that available later on as well. Okay, let’s continue here with our report. Let’s fill this all with data now. So we’ll go ahead and fill out these panels. Up here we would have our controls, so that would be our campaign but also our date picker. So let’s put that in place. Date picker and filter control. Right here there’s actually a new filter control. This is for the different views of Google Analytics. That’s not something that we would use here. Okay, so we can get rid of the panels in the background, and this should give us capabilities of showing us the right campaign and the right date. Let’s view this and here we can see our different campaigns. There’s null. That is something we would investigate with our raw data again. And then we have our date picker, so that is all working as expected. Alright, let’s continue with these metrics groups here. Before we start out, I want to actually select the date range so we are working with dates that actually make sense for us, and in our case it would be the July date range. So I’m just gonna change this to this week here, and that will make all our data appear in that realm, and if anything goes wrong here, we can also adjust it later on. So let’s go ahead and build these metric groups. Now in our wireframe we see that we have these groups of acquisition, cost and conversion, and inside of them we have these different metrics that are represented by the actual impressions, the CTR and the clicks, and then also the comparison if it went up or down over the last period. So how do you implement this? You can do this with the scorecard elements. So we’ll just draw on the canvas here our scorecard element and choose the right metric, so in our case it would be impressions. And we can actually, this is a pretty big number, go into Style here and press on Compact Numbers, and this will make this a bit more compact so we can actually read it. We want to have our comparison metric, and for that to actually appear we need to implement this second date range so to compare date range, and this should compare to the previous period. This will give us this little number. Now you don’t see this actually, so I’m just gonna change this around and make the inside transparent. Okay. And before we move on and copy this over, let’s style this a bit so we don’t have to do this later on. We can easily copy it over and change the data around. So we want to have our CTR. That doesn’t make any sense so maybe there’s something wrong in our metrics. Let’s create a new metric or go to this menu and we have here our CTR. This should be not clicks, impressions, but clicks divided by impressions. Okay. Let’s update this field. And we see our data also changes. Now we als

Summary – Lesson 4

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

Links mentioned in the video:
Supermetrics: http://supermetrics.com/?aff=1014
Data Vizualisation Books: https://kit.com/Measureschool/data-visualization-books
Ben Collin’s Course: https://benlcollins-data-school.teachable.com/?affcode=69396_b3spab2w
David’s course: https://learn.codingisforlosers.com/data-studio-the-lazy-way?coupon=MEASURE
Data Studio Gallery: https://datastudiogallery.appspot.com/
Tableau Gallery: https://public.tableau.com/en-us/s/gallery
YouTube Channel: DataSaurus Rex https://www.youtube.com/user/datasaurusrex
Stay up to date with Data Studio Changes: https://support.google.com/datastudio/answer/6311467
Lea Picas Present Beyond Measure Podcast: http://www.leapica.com/podcast/
Facebook Ads Manager for Excel: https://www.facebook.com/business/m/facebook-ads-manager-for-excel

FAQs

19:32 How can I change the default Week-Day start?

21:40 Are there any alternative to Supermetrics?


27: 40 Can we connect different sources to the same report?


28:57 Can we make Drop Down menus for sources?


30:29 What is the best way to compare two date ranges in Google Data Studio?


31:49 What is the best data studio report for overall performance?


34:34 Can I connect Data Studio and Google Optimize?


37:35 Does Supermetrics have an Agency license?


39:49 What data can I pull with Supermetrics?

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