Digital marketers love tools. My career is built on search marketing, a specialty known for its fondness of tools. For years, we have had unique tools with unique metrics for each part of our job. One tool to check and report rankings. One tool to check and report broken links. One tool to check and report authority. Lots of data and all of it living in its own universe, separated from the rest of the marketing mix.

To add to the complexity of this system, each tool is often 'owned' by different departments. Marketing may control goals and reporting, creative may control experiment setup, while development is tasked with making things function properly. Sometimes the left hand has no idea what the right hand is doing.

That system of intricate data silos worked for many years. We stitched the data together the best we could and made decisions knowing that while one hand delivered a report the other held an asterisk.

Google Analytics is changing that. It acts as the glue that holds our data together, allowing marketers to get a firm grip on KPIs and hold each digital marketing channel accountable for the capital it's allocated. Google Analytics strives to position itself as the hub of all digital marketing activities. It's working to be more than just a place to collect data and report numbers, it wants to be the place where stories that drive internal and external communications begin.

Enter Google Optimize, an A/B testing and personalization tool that uses Google Analytics data to power your CRO efforts. Obviously A/B testing is nothing new, neither is serving personalized content based on customer behavior. The true progress here is how Google Optimize pairs with Google Analytics, and how easily we can tie our experiments to KPIs in Google Analytics.

For an introduction to Google Optimize, I wrote a handy blog post to shorten your learning curve. The rest of this discussion assumes that you are familiar with the basics of Google Optimize and why marketers use it. In this article I focus on how Google Optimize and its enterprise version, Optimize 360, will change your life by putting everything under one roof.

Native integrations between Google Optimize and Google Analytics offer benefits at nearly every turn. From setup and design to reporting and iteration, the familial ties make website testing and personalization more successful. And you don't have to remember any additional passwords to do it.

Integrations Make Setup Easy

Google Optimize is powered by Google Analytics, it cannot function without being linked to GA. There are two ways to add Google Optimize to your site. If you already have a Google Analytics JavaScript snippet hard coded on your site, there's a single line that you add. Hopefully though, you've transitioned to Google Tag Manager (GTM) and can easily make tracking changes to your site without involving developers. If so, there is a built-in Google Optimize tag template in GTM already available. Fill in your information, fire the Google Optimize tag on the pages you want to experiment on (all pages is recommended) and you're off to the races. You can learn more about adding Optimize to your site on the Help Center.

Why use GTM? Event tracking, that's why. Google Optimize uses GA goals as experiment objectives and pulls data from GA to calculate experiment results. So if you want to test objectives that involve user interaction, you'll need to set up an event-based goal first. The easiest way to do that is by using GTM.

Want to track if a landing page variation causes more people to download a PDF? You'll first need to track downloads as an event using Tag Manager (easy to do) and then set up an event based goal in GA. Once the goal is set up inside GA you'll be able to test against it on Google Optimize.

Often, you're already using goals to track if users are reaching important pages or completing important actions on your site. Other A/B testing tools require you to replicate these goals, to the best of your ability, inside their testing interface, opening up the possibility of two different definitions of the same ultimate goal.

By using Google Analytics goals as test objectives, you can be confident that the results of your experiment will impact your analytics goals the way Google Optimize says it will. Remember the goal: consolidate your data and make it work for you.

Target Easier and Experiment More

Who you test is as important as what you test. You might have the best test idea in the world, but if it isn't relevant to half of the people that see it then you're not going to see the results you expected. That's why targeting is so important.

Basic Targeting

If you've used other A/B testing platforms, then you can expect much of the same targeting options here. Experiments can be setup to target URLs (host, path, query parameters, etc), Behavior (referrer and time since first arrival), Geography (city, state, zip, etc.), and Technology (browser, OS, device, etc).

And if you have some development chops it also includes the ability to target based on JavaScript variables, first-party cookies, custom JavaScript, and data layer variables. For a majority of people just starting out with Google Optimize, the default options will allow for quick and easy experiment creation.

For the Google Tag Manager users, you'll be thrilled to know that Google Optimize will automatically import your data layer variables into your experiment to allow you to use for targeting. Any server information that you've stored on the data layer will now be available to help you target a population or exclude a certain group.

Raise your hand if you're ready to be blown away. Let's talk about the advanced targeting options.

Advanced Targeting

If you're a marketer, don't let the word advanced scare you. As it relates to Google Optimize, the word advanced is synonymous with control. Control over the who, when, and where of your experiment.

Most testing tools focus targeting on the present - what page is a person on now or in what city they are currently located. Google Optimize 360 extends these traditional targeting options by allowing marketers import Google Analytics audiences, opening up our targeting to focus not just on the present but the past as well.

You may already be familiar with audiences that you can build and use with Google AdWords, grouping users by their behavior or any custom information we've collected. We can take existing audiences from Google Analytics and duplicate them to use with Google Optimize. This means we can target users that have at one time expressed interest in something or exclude users that have already converted, even if that happened in a different sessions weeks or months ago.

For example, LunaMetrics drives a lot of traffic to our blog, far more traffic than our training pages receive. Using GA audiences we can create an experiment that targets only users on the blog, who have visited a training page, but have not registered for a training. This example combines the present, what page they are on, with the past, they have expressed interest in our trainings and they are not already customers.

Here's how those targeting options would look in Google Optimize.

Not only does this ensure that the right people are being targeted, but it removes people from the experiment as they complete our experiment objective, a training registration (which is a goal we already have established in GA). Taking it a step further, we could use audiences to identify people who saw the experiment and converted, and target them with another test. And all of that is done using Google Analytics data.

Reporting For Everyone

Google Optimize has an easy to understand reporting interface that shows you how each variation is performing against the original, and how each variation is contributing to your Google Analytics goal completions. Here's what it looks like.

The results shown above are for an experiment we ran to see if displaying our blog subscription form in different ways would improve the number of people subscribing to our blog (spoiler alert, it did).

Reporting is not unique to Google Optimize, but rather how it collects data and makes it accessible. Unlike other A/B testing tools, Google Optimize does not collect data, it fetches data from the Google Analytics view that is tied to your experiment. That means that the results of your experiment are subject to any filters you have applied to that view. This helps to ensure that unwanted traffic, like internal traffic or bots, does not pollute the results of your experiment and adds another layer of confidence to your experiment results.

That connection with GA also allows Google Optimize to pass experiment results back into the Google Analytics reporting interface. That data is then accessible via the Experiments report (navigate to Behavior > Experiments), where you'll see the same data (experiment sessions, conversions, conversion rate, improvement over the original). And because this data originated from Google Analytics, you are also able to see additional metrics for each variant as well.

* those with a keen eye will notice that the “compare to original" numbers do not match up, more on that here.

But wait, there's more. You can also use Experiment Name, Experiment ID, and variant as secondary dimensions in all the standard Google Analytics reports or you can use the experiment dimensions to create a custom report. The gif below shows a custom report we made that is filtered to include only our blog experiment (using Experiment Name) so we can see how the experiment performed on a page by page basis. Those with Google Analytics access can view the results of the experiment right in the Google Analytics interface, a platform they're already familiar with and that they've been using for years. Now that's a real high-five!

Evolve and Grow

We finish an experiment and the results are conclusive. Now what? For many marketers, to-dos are added to development queues, weeks turn into months, and the next round of experimentation is launched. It is slow. It is labor intensive.

This is where Google Optimize excels. Traffic is simple to allocate between variants. So you can easily shift all of your traffic to see the winning variant going forward while negotiating the logistics of scaling your recommendations.

Chaining experiments together happens quickly. Before long, what started as traditional A/B testing begins to look more like personalization. Users can convert or self-select their way into unique subsets of the audience and have an experience tailored to the audience when the return.

For this website, Online Behavior, it might mean prioritizing new articles based on previous consumption patterns. Users with an affinity for analytics are exposed to more content from the Analytics & Optimization section. Or perhaps audiences are built around the website's personas.

Or career experience. Or degree of technical sophistication. The possibilities are endless.

Our experiments help us learn more about our users, insights that we can easily hand off to Google AdWords or Doubleclick through audiences connected in Google Analytics, keeping our message consistent across our site and other platforms where they'll see our company.

For me, this has been the most impactful result of using Google Optimize. Not only am I learning constantly through experimentation, but through the integrations with other Google products, I can take those lessons learned and improve the experience of my users.

If you liked this post, you might also enjoy Krista Seiden's Building a Culture of Testing and Optimization

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You might have heard a few weeks ago that the Google Analytics team launched Data Studio (DS), a robust platform for reporting and visualising data. If you missed that piece of news, take a look at this step-by-step guide - it's a great starting point.

DS can be used to visualise a number of different Data Sources, including AdWords, BigQuery, Google Sheets, and others. Whilst all of these bring considerable benefits, in this article I will take a deeper look into how Data Studio can empower your Google Analytics data and focus on how to do the following:

• Bring multiple GA accounts together in one place
• Highlight data for non GA users
• Customise and brand your report to your own style guidelines

Connecting your data

The first thing you'll need to do when visualising GA data in Data Studio is to create a new Data Source. Below are the steps you should follow:

1. Navigate to the Data Source section of Data Studio
2. Press the + button (bottom right corner of the screen)
3. Select the Google Analytics connector
4. Opt for the account that you're interested in visualising - this will display all the properties that sit within the account
5. Select a property - this will display all views that sit within that property
6. Select a view
7. Press "Connect to data"

On completing step 7, this will display the data schema of your GA data (see screenshot below). You'll recognise all the GA dimensions and metrics that you know and love from the GA interface and you should also see familiar custom dimensions and custom metrics that you may have created within your GA property.

One last thing to do to keep your dashboarding nice and tidy - give your Data Source a name. In this instance, this is my Android app view, so I'm going to label this accordingly.

Congratulations, you've now successfully created a GA Data Source!

Now seems as good a time as any to note what I think is one of the best things about using Data Studio (and this is especially useful for those of you who are GA 360 users): the GA connector in Data Studio provides you with the same level of processing and sampling as you would see in the GA interface. So for those of you who have come up against lower sampling thresholds when using the API to visualise data, this will be huge news!

Creating a report

Now we have a GA Data Source all configured and ready to go, we can start visualising the data. We left off looking at the Data Source schema. From here press "Create Report" - this opens up a new tab in the Report part of Data Studio and you'll be asked if you want to connect this Data Source to the report. Click "Add To Report".

You are now presented with a blank canvas on which to start painting your GA picture. Let's say you want to draw out a report that looks like the Audience Overview report in GA.

1. Add a time series chart and choose your metrics (plot more than one series, if you like)
2. Add a geo map to show where all your users are located
3. Add something that you are personally interested in – something that you can't add into the Audience Overview report yourself
   • Do you like to get a quick glance at total screen views? Then add a table with screen as your dimension and screen views as your metric
   • Is it important that you see how many unique events are firing for a particular event? Add a scorecard that looks at unique events for that one event category

As you can see below, we've created something really quite bespoke and we haven't spent much time doing so - this example below took less than a couple of minutes to build. And moreover, it looks smart, it‘s aesthetically pleasing and it's personalised to visualise the data that's most important to you.

For those seasoned users of GA, you are probably thinking "so far Data Studio looks good, but how is this different to the dashboard section of the GA interface?". Indeed, dashboarding in GA is a great feature and one that is highly adopted (see some examples). It allows you to highlight key figures very quickly and the ability to share these with all your users within the GA account is also brilliant... create a dashboard that your CEO will be interested in and share with them - they won't need to spend time digging around in the interface thus enabling them to grasp key points quickly.

Data Studio certainly owns similar features, but it extends the capabilities of the dashboard feature within GA. I'm not sure which of my points below is more important but I'll start with one that I can, personally, spend a lot of time on...

Styling your report

Using the control panel on the right hand side, you can choose to customise and style your reports pretty extensively. You can modify the theme of the page - change the background colour/add banners/choose font - and generally overhaul the blank canvas to your brand's style guidelines. But you can then get a lot more specific and granular about the style details... select each individual chart in turn and modify the colour of each series, change the colour of the text or move the chart legend around. There are plenty of options to choose from when styling your report, I'll just highlight a few of my favourite things to do when customising:

1. Add a "featured" box (with curved edges)
2. Add a logo or photo
3. Simplify the look of time series charts by removing grid lines (and even axes)

Adding multiple GA accounts (or other Data Sources) to a single report

We're now going to look at something which will hopefully prove extremely useful to a lot of data analysts. You'll need to create a new Data Source to do this, so go ahead and follow steps 1-7 used previously to do so. This time I've connected to my iOS GA view (we connected to an Android GA view last time). By creating this second Data Source and attaching it to the same report you've already created, you will be able to visualise data from two different Data Sources in one place. Finally(!) you can compare data across GA accounts in a really simple and easy way. Gone are the days of having two different browser windows open with one account open on each. You can now view a completely separate GA account in one, centralised location.

For this, I think we actually want to see a like-for-like comparison of the data across the two accounts so we're going to resize our current components and move them over to the left hand side of the page. Once settled there, we can then copy and paste them and move the duplicates to the right hand side of the page. Now we can go about changing the underlying Data Source of each chart. By selecting each chart in turn, we can use the properties panel to edit the Data Source being used. If we switch from the Android view to the iOS view, we can immediately see the chart adjust to using this new dataset. Isn't it great that you can actually copy/paste charts and just change the Data Source?!

Once we've changed each of the Data Sources, we can then very simply stand back and look at the differences between the two apps. Is one performing better than the other? Why is that? Is there an event that's not firing properly? Is there a particular screen where users are dropping off? Comparing these views side by side couldn't be easier in Data Studio, and why stop there? You're not just limited to comparing GA accounts – you can add in data straight from AdWords (using the AdWords connector) or add some data you've manually pulled together into a Google Sheet.

Sharing your reports

There is one last thing I want to mention before Daniel tells me this piece is too long for his website ;) It's an important one, so very much worth spending some time on.

As mentioned in Daniel's introductory post on Data Studio, sharing reports is really simple. Using the same functionality you see within Google Docs and Google Sheets, you just press the "Share" button to allow other users to either "View" or "Edit" a report. In addition to these options, there is another way to enable (or limit) users as to what they can see.

Owner's vs Viewer's credentials

In the process I've noted above, there was one option I missed when setting up the Android and iOS Data Sources. When you get to the schema stage, and before you jump into creating a report, you can select whether you give users "Owner's credentials" or "Viewer's credentials".

If we opt for the "Owner's credentials" option, this means that the report accesses and visualises the data based on the credentials that are possessed by the owner of the report. So, I have Admin access to the GA account where the Android data is being pulled from. Daniel, however, doesn't have access to this GA account at all. By setting the Data Source option to "Owner's credentials", when I give him View or Edit access to the final report I've created, he will be able to see the visualisations in Data Studio. He can filter and query the data to his heart's content, even though he doesn't have explicit access within the GA account itself.

However, if I set the Data Source to have "Viewer's credentials", the user of the end report would have to use their own credentials in order to see the data. In other words, in the situation where Daniel doesn't have access to the Android view, he won't be able to see the data in the report I've built. Some error messages will pop up and he won't be able to filter or query any of the data.

Using these options allows you to share or hide data from those who you wish to have access. These selections are up to you to depict and you can vary them based on the access you wish users to have. Ultimately, this is all about the security of your data and DS gives you a number of options to choose from to ensure your data is only seen by those you choose.

Closing Thoughts

Thanks for getting this far! Hopefully you've learned something new and now feel empowered to go and start building out some lovely looking reports in Data Studio. My goal was to show you just how simple it is to connect to GA and from there, indicate how to really make the most of Data Studio's fantastic features.

Combining multiple Data Sources in one dashboard is something a lot of data analysts need and Data Studio makes this as simple (and quick) as it can be. Now add in the ability to customise and brand your reports and it won't be long before everyone is asking you to create a report in DS for them. Finally, Data Studio doesn't forget to ensure all your data is kept away safely, giving you multiple options to decide who views your data and what they can do with it.

Happy dashboarding!

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A few months ago I wrote about two new Google Analytics 360 (GA360) integrations for ad supported websites: DoubleClick for Publishers (DFP) and DoubleClick Ad Exchange (AdX). As I said then, I believe they are major game changers, they provide a robust solution to measure and optimize ad supported websites. I still believe that, even more so!

In a nutshell, the integrations brought two great improvements at that point:

1. Data accuracy and completeness: a user that left the website through a click on a DFP or AdX unit, in the past, was considered a simple abandonment, but with the integration they are "seen" as ads clicked. This also allows a multitude of new analyses using metrics that couldn’t be merged before.
2. Reporting: having all the data in one centralized place can save a lot of time. The GA360 interface can be used to create custom reports, dashboards and emails.

But since my last article, a few important things changed in the product. Last week, the GA360 team released an outstanding case study (link to PDF) discussing how AccuWeather delivers enhanced value to advertisers with DoubleClick for Publishers and Google Analytics 360. Below is a descriptive scheme shared in the case study.

In this article I will discuss an important development in the DFP & Google Analytics 360 integration: the Audience Sharing feature (beta) that allows publishers to share Google Analytics 360 Audiences with DFP bringing a series of benefits.

Google Analytics 360 Audience Sharing ^BETA

Besides the reporting capabilities already discussed in my previous article, the DFP integration enables deeper optimization opportunities with the Audience Sharing feature (beta), a way for publishers to share audiences they created using Google Analytics 360 data directly into DFP. These Audiences can then be used to target users that performed a specific task, read a specific type of content, came from a specific campaign, or any other information available on Google Analytics. You can do that either by building a segment on Google Analytics and building an audience out of it or by directly creating an audience and sharing it with DFP.

Below I discuss two use cases for this feature: optimizing ad serving by not showing some ads to some users (decrease impression waste) and providing better targeting based on user behavior (optimizing targeting).

1. Decreasing impression waste

It is very common to use DFP to serve house ads, which are intended to promote an action inside your website (as opposed to promoting an advertiser); this could be, for example, a registration for a membership or a page where you are trying to sell something. For Online Behavior, I used house ads to promote my book, showing an ad unit below every post on the website.

However, if a user had already visited the book page and clicked on one of the links to purchase it, I was wasting those impressions, and it would be more profitable for me to show Backfilled AdX ads instead of the book promotion to that group of users. Easy peasy!

The first step was to create an audience on Google Analytics including all users that have completed a goal of clicking on one of the book links on that page. Note that in the first step in the screenshot below I chose to share this audience with my DFP account.

Once I finished creating this audience, I went on to DFP and edited my Book campaign line item to include a targeting criteria as shown in the screenshot below: Audience Segment is not Book Viewers.

Voila! Users that clicked on the book links didn’t see the ads anymore, they saw AdX Backfilled ads, and that helped raising my revenue :-)

2. Optimizing targeting

You might also go the other way around.

Suppose you have a paid subscription along with content you provide for free to your readers. And suppose that you are currently using Google Analytics to measure those subscription transactions (you might as well use a Goal). That means you could identify which of your users are starting but not completing your subscription process. You could then create an Audience of all those users and remarket to them using house ads on your own website to try and engage them back with the funnel on future sessions. This would follow the same process described above.

Or suppose you are selling inventory to advertisers that are interested in people that care about sports. One simple technique would be to show the ads only on sports pages. However, some of your users that are interested in sports might visit the website sometimes and only look at the news section, but they are also interested in sports, just not on the current session. With Google Analytics Audiences, you could save “interests” across sessions, meaning that a person that is interested in sports will be part of a sports audience even if they don’t see a sports page in the current session. That would increase the amount of impressions you have available for sports fans.

Here is a similar example from the AccuWeather case study:

The integration between DFP and Analytics 360 is helping AccuWeather advertisers in other ways. For instance, one of its advertisers, a health related consumer product, wanted to survey users who had seen its ads on AccuWeather’s website. AccuWeather used Analytics 360 data to build a custom audience, blending those who had been exposed to that company’s ads on its website with location data to reach the right users.

AccuWeather shared this audience with its DFP account, which delivered the survey to that select audience. That’s how the advertiser learned that those who saw its ad on AccuWeather.com were actually 6.5 times more likely than the typical user to buy its product within the next 30 days. It’s not too surprising that this advertiser is making additional ad buys with AccuWeather this year.

Last, but not least, suppose you have sections of your website which are not very good at engaging your returning users, such as news articles. As in the previous paragraph, if you have different audiences for different interests, you could use DFP on the news articles to engage your users by showing them an interesting post based on their interests, this would keep them engaged with the website by providing them with targeted content.

If you are a Publisher, I am sure you are super excited about the opportunities this tight integration brings to your business. Happy analyzing / optimizing :-)

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Do you really know what you're measuring? You probably think so, right? You've implemented Google Analytics on your site and it's tracking your page views. But what if you're getting more advanced and also want to know more about the interactions on your site, like a click to download button? These kind of interactions eventually lead to creating goals which will probably bring up questions about the context of your page views, so you'll start filling up your custom dimensions.

Before you know it, you have thousands of pageviews, hundreds of events, dozens of custom dimensions and goals, and so on - and this is all organized... in your head! Now suppose you've been doing this for a while and then somebody starts asking questions about how you're using Google Analytics in your company. Time to panic! Of course you forgot what most of your custom data (goals, custom dimensions, custom metrics, event tracking) was named after and why you decided to name it that way. Just admit you've been there, we've all been there.

When The Next Web (TNW) moved to Google Analytics 360, around two years ago, we had a great opportunity to add extra custom data, but the work involved to onboard new colleagues proved to be directly proportional to the amount of customizations in the tool. We needed better naming conventions in order to make it easier to explain the data in our set-up to people who weren't used to Google Analytics and so much data. That was hard, but luckily we found a good way to do it: a measurement plan.

The measurement plan is a document that describes what your Event Tracking is tracking, what your custom dimensions really mean, and how your goals are defined. It comes in pretty handy when you have over 50 custom dimensions! It can keep track of what you want to implement, what you already have implemented and what it really means.

In this article I'd like to explain a bit more in detail some of the lessons I learned while creating measurement plan for one of the biggest publishers on the web. If you want to jump into the water with both feet, feel free to make a copy of this template I created based on the spreadsheet we're using at TNW. You can find it here and make it your own by Making a Copy of the document.

What does a Measurement Plan look like?

The format that I like using for my measurement plans is a Google Spreadsheet with detailed explanations of the different categories you'll have in a regular set-up: goals, remarketing lists, custom dimensions, etc. Below are some of the must-haves, make sure to include detailed information about each.

• Pages: Explain what kind of structures you have on your site. Where is your search located, what's the location of your blog.
• Filters: How is the data in the interface being influenced, what filters did you apply and for what reason?
• Event Tracking: How are the naming conventions defined for your Event Tracking and what kind of events are you tracking and how to find them back.
• Goals: Why are you tracking what you're tracking?
• Custom Dimensions: What does your custom data really mean to provide more context.
• Custom Metrics: How are the custom metrics do you have valuable to your reporting?
• Calculated Metrics: What metrics are you combining and what do they represent?
• Remarketing Lists: If you have over 20 remarketing lists you're losing track basically of what a certain list contains. So what is the naming convention for your remarketing lists and what kind of users does it contain.

Frequently Asked Questions

Who should be using a Measurement Plan?

If you are the only person working on your implementations then it's probably not very useful for you to write down all of this information. For bigger organizations / teams working with multiple people in the same Google Analytics account I can see this being more useful, as one person can be working on the technical side of the implementation while someone else would like to know what is the status of a certain implementation. As a manager, I would recommend asking whoever is implementing GA to do it anyway, as employees change over time, so might be wise to have some written documentation.

How to track implementation progress?

The great thing about using a Google Spreadsheet is obviously that it can be edited by every team member involved with your tracking implementation. In our case it gets updated once new custom dimensions have been tested and implemented so we know our ideas are working.

Technical versus Business documentation

In the format that we've mentioned before we're only explaining from a technical perspective how the set-up works. As the people in our organisation are more technical than average they know what this means when it comes to reporting the data. I can imagine that for other types of organisations you'd like to have an additional document explaining what the data entails and how it can/should be used during reporting.

Would like to start your own measurement plan? I've created a template for you based on the spreadsheet that we're using at TNW. You can find it here and make it your own by Making a Copy of the document.

Bonus

Below is a picture of Krista Seiden, Analytics Advocate at Google, showing how TNW uses Google Optimize 360. Read more about it in this article I published recently.

Krista Seiden presenting at Superweek 2017

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SEOs of the world, rejoice! Google Data Studio has a new connector to bring Search Console data into the tool. This means you can start reporting your search data alongside other data sources in the tool. I am sure people will find all kinds of cool stuff to do with it!

Please note that this is not a replacement for the Google Analytics (GA) <=> Search Console (SC) integration. In that integration you will gain insight into your customer behavior by merging GA and SC, here you will be able to visualize it alongside GA and other sources, for example Google Sheets, which might be extremely helpful if you manage your SEO efforts in a Sheet.

A few months ago I published a sample Data Studio report on the GA blog showing how I use it for Online Behavior, focusing on GA data. In a previous life, I used to be responsible for SEO on a large user generated content website... so I thought it would be fun to wear my SEO hat (pun intended) and try building a good dashboard based on Search Console data.

In this article I will present a new page I created in that same report using the Search Console connector. Below I discuss each of the elements I created, but you can access the full report to play with it.

Search Console data available through the Connector

Once you connect your Search Console to Data Studio (see instructions) you will have two different tables to connect: Url Impression and Site Impression. The main difference between the tables is that the Url table has the Landing Page dimension, while the Site table has the Average Position metric. Here is an image showing the schema for both.

I recommend you create two separate sources, one for each table; this will enable you to use them alternatively in the same report, as I did below.

Header: Dates, Filters and Overview

I always like to include a header in my reports, especially when they have multiple pages. I think headers help "data consumers" to understand where they are; I also think it is helpful to provide some important stats in the header, some kind of tl;dr.

For Search Console data, I believe the most important dimensions to segment by are Country and Device, as they can bring insight into how to optimize SEO efforts (I used filter controls for that). When it comes to metrics, I showed Impressions, Clicks and CTR for the last 28 days (the report default) compared to the previous period (I used scorecards for that).

Impressions and Clicks trends over time

Trend lines are arguably the most effective type of chart, it shows how metrics are changing over time. In this case, I chose to looks at both Impressions and Url Clicks (same as above, last 28 days compared to previous period), I think those two metrics give a good indication on how well the site is ranking on Google (Impressions) and how well it is managing to convert those impressions into website visits (Url Clicks).

Note that in the chart below there is a strange spike in Impressions, which was not followed by an increase in Url Clicks, that is something I should analyze further; maybe Online Behavior started ranking for a query that I am not doing a good work to convert into Url Clicks?

I have not included it in this report, but I think there is a place for adding a chart with a Google Analytics data source showing how many conversions the website got from Google Organic. This would close the loop.

Countries and Devices comparison

As I mentioned earlier, as far as I understand Countries and Devices are important dimensions that can help uncovering interesting SEO insights. For example, in the chart below it is clear that while the US is leading when it comes to Impressions, the UK and India (2nd and 3rd) have a significantly higher CTR. Looks like Online Behavior snippets are working better for those countries...

Queries and Landing Pages performance

The charts above are a good way to track SEO perfomance, but at the end of the day, it is important to drill down into Landing Pages and Queries to understand what is working well and what needs to improve.

Landing Pages

Landing Pages show, for any number of queries bringing traffic to them, how well the content did in terms of Impressions, Url Clicks and CTR on Search. For example, in the table below we can quickly see two "anomalies":

1. Actionable SEO page (red square): this page has the 2nd highest amount of impressions but almost no Clicks, resulting in an extremely low CTR. The reason might be an overly technical meta description, creating a very unattractive search snippet.
2. Data Visualization page (green square): this page has a very attractive title and description, which may be contributing to a very high CTR

This table can only be created with the Url Impression data source.

Queries

Queries have always been the main focus of SEOs, probably because it is the easiest to explain: you rank #1 for 'best analyst in the world'! From my shallow knowledge of SEO, I understand that this statement cannot be used anymore, as a query doesn't have an absolute ranking anymore, it depends on a series of factors... hence the metric Average Position. But that doesn't make it less interesting or important, Query is still a very actionable and important dimension.

The table below also shows two very clear "anomalies":

1. google analytics: while it shows the highest number of Impressions and quite a good Average Position, this query shows an extremely low number of Clicks. Something is not right here :-(
2. google tag manager: while the Impressions are smaller and the Average Position worse, this Query shows an amazing number of Clicks. GTM FTW!

This table can only be created with the Site Impressions data source.

Closing Thoughts

This article is just a quick overview on what you can do with the Search Console data connector on Data Studio. I tried to showcase all metrics and dimensions and bring them to life with examples. However, the greatest power of Data Studio is that it enables professionals to see data from multiple sources side by side, so I am interested to see how businesses use it in different ways. If you are doing something cool with Data Studio drop me a line.

Happy visualizing!

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Not only is Data Studio now free for all (!) but, to add to our ever-growing excitement about the product, segment functionality for the Google Analytics connector is now available too!

Until now, Data Studio has provided us with the option to use filters. This functionality is equivalent to using an advanced filter within the GA interface whereby, for the data in the table or graph, the filter will narrow values meeting the condition(s) set.

Segments, on the other hand, allow for a more advanced analysis of users visiting your website. They allow you to create a subset of data by collecting users (or sessions) into that subset, which you can then query and analyse to your heart's content.

Adding GA Segments to Components and Pages

For this example, let's say we want to look at the behaviour of users who are browsing from a mobile device. In GA, there is a default segment already created called Mobile Traffic and this is set using this condition:

In the GA interface we can apply this segment, along with four other segments, to our reports to gain a better understanding of how our group of users (those browsing on mobile devices) behave on site. The good news is that you can now apply this same segment in the Data Studio interface as well.

To do so, you can either apply the segment at the page level or the component level. If you want the segment to apply to all components on the page, you can select Current Page Settings > Google Analytics Segment > Add A Segment. However, if you just want to apply the segment to one component, or a group of components, you will need to select the component(s) > go to the Data tab in the properties panel > Google Analytics Segment > Add A Segment.

At this stage, you can now pick your segment. There are three buckets that segments can fall into (see numbers in screenshot below):

1. Added segments: These are segments which have already been added to the report to another component or page (NB. this bucket won't show up if you've not added any segments yet.)
2. System segments: These are the default segments that are created within the GA interface. These include segments like New Users and Returning Users, which I'm sure you'll be familiar with seeing. The Mobile Traffic segment mentioned earlier falls into this bucket.
3. Custom segments: These are segments as created by you within your personal GA account… more on this later.

If you don't want to search through these buckets for the segment you're looking for, then just tap the search icon (4) in the top right hand corner and type in the name of the segment you're after and it should pop up.

Once you've selected your segment and opted to add the segment to the report, this will be applied to the page or the component(s) that you've chosen to apply it to. At this stage, you can happily go away and analyse the behaviour of your users browsing from mobile devices.

Applying multiple Google Analytics segments to a Data Studio page

However, let's not stop there; there is still more analysis to be done... you can set up identical components side by side in your Data Studio report in order to compare users visiting the site from a mobile device against users visiting the site from a desktop. So far you've added the mobile segment to the left hand components. To start with, copy the components from the left hand side and paste them onto the right hand side to get the visualisation ready. Now let's create a desktop segment and apply this to the components on the right hand side.

To do so, you need to first create the segment within GA. You can do so using the following condition:

Once that's saved in GA, you just need to refresh your Data Studio report and you can now find the Desktop Traffic segment you created under the Custom segments bucket. As noted previously, this bucket includes all the segments that you've personally created within your GA account.

Once again, you now just need to select the segment and add it to the components you're interested in. Select all four components on the right hand side of the Data Studio report (using the ctrl button) and apply the segment to all four at once. Don't forget to label the two sides of the report so that anyone viewing the report (including yourself!) will understand the data being visualised.

So now you have two different segments applied to your report! The fact that the subset of users browsing on mobile can be compared so easily to the subset of users browsing on desktop makes this a really useful feature within Data Studio. You can quickly and easily see the behaviour of these users, allowing you (if need be) to action upon the results you're finding.

From a quick glance of my report, you can see that mobile users spend a lot less time on a page on average (4 mins 38 secs) compared to desktop users (8 mins 22). If my aim is to ensure content is keeping users engaged on site, then this might mean I need to think about what to do with the mobile version of my website. Why are users less engaged with pages when using a mobile? Is content hard to read on a mobile web browser? Are there any issues with the pages loading on mobile browsers? These are just the initial questions I would begin to ask based on a quick glance of this data...there are many more that could come out of applying these segments to further components.

Synchronising Google Analytics and Data Studio Segments

One last thing... you'll notice a small icon next to the segment you've added in the properties panel.

This indicates if the segment has a sync enabled. If this is enabled, any updates to the segment within the GA interface will be replicated and synced with the segment applied in Data Studio. If you want to make sure that the segment you've applied doesn't change in definition, even when it's changed in GA, then you'll want to disable this sync. Otherwise, keep it enabled!

Happy dashboarding!

P.S. For more info on segments, head to the super helpful help centre articles on segments!

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Last year I wrote about the Marvel vs. DC war on the big screen. It was super fun to merge two of my passions (data visualization and comics) in one piece. It started with my curiosity to understand what all those movies are amounting to, and I think it helped me prove a point: Marvel is kinda winning :-)

One of the things that annoyed me was that I had to link to the interactive visualization, readers couldn't see the amazing charts in my article (!) - so I ended up including static screenshots with some insights explained through text. While some people clicked through to play with the data, I suspect many just read the piece and went away, which is suboptimal - when I publish a story, my goal is to allow readers to interact with it quickly and effectively.

I am extremely excited that now Google Data Studio allows users to embed reports in any online environment, which empowers us to create an improved experience for telling stories with data. This feature will be an essential tool for data journalists and analysts to effectively share insights with their audiences.

A year has passed since I did the Marvel vs. DC visualization, so I thought it was time to update it (5 new movies!) and share some insights on how to use Data Studio report embedding to create effective data stories.

Enable embedding

The first step to embed reports is a pretty important one: enable embedding! This is quite simple to do:

1. Open the report and click on File (top left)
2. Click on Embed report
3. Check Enable embedding and choose the width and height of your iframe (screenshot below)

Please note that the embedding will work only for people that have access to the report. If the report is supposed to be publicly available, make sure that you make it viewable to everyone. If the report should be seen only to people in a group, then make sure to update your sharing settings accordingly. Read more about sharing reports on this help center article.

But how do you make sure you are choosing the right sizes? Read on...

Choosing the right visualization sizes

Needless to say, people access websites in all possible device categories and platforms, and we have little control over that. But we do have control over how we display information in different screens. The first obvious recommendation (and hopefully all the Interweb agrees with me) - make your website responsive! I am assuming you have already done that.

On Online Behavior, the content area is 640px wide, so the choice is pretty obvious when Data Studio asks me the width I want for my iframe - make sure you know the width of the content area where the iframe will be embedded. Also, since you want the visualizations to resize as the page responds to the screen size, set your Display mode to Fit to width (option available on Page settings).

Without further ado, here is the full Marvel vs. DC visualization v2!

I personally think the full dataviz looks pretty good when reading on a desktop, I kept it clean and short. However, as your screen size decreases, even though the report iframe will resize the image, it will eventually get too small to read. In addition, I often like to develop my stories intertwining charts and text to make it more digestible. So here is an alternative to embedding the whole thing...

Breaking down your dataviz into digestible insights

As I mentioned, sometimes you want to show one chart at a time. In this case, you might want to create separate versions of your visualization. Below I broke down the full dataviz into small chunks. Note that you will find three different pages in the iframe below, one per chart (see navigation in the bottom of the report)

Right now, you can't embed only one page, which means that if you want to show a specific chart that lives on page 2 of a report you would need to create a new report, but that's a piece of cake :-)

I am looking forward to seeing all the great visualizations that will be created and embedded throughout the web - why not partner with our data to create insightful stories? Let's make our blogs and newspapers more interesting to read :-) Happy embedding!

BONUS: Data Studio is the referee in the Marvel vs. DC fight!

As I was working on my dataviz, I asked my 10yo son (also a comic enthusiast) to create something that I could use to represent it. He created the collage / drawing below, I think it is an amazing visual description of my work :-)

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