Google Data Studio comes out of beta

The company continues to develop new features for the reporting product.

The post Google Data Studio comes out of beta appeared first on Marketing Land.

Google’s two-year-old free reporting and data visualization platform, Google Data Studio, is now generally available.

Integrations and data connectors. Data Studio, now part of Google Marketing Platform, has native integrations with Google Analytics, Google Ads, Display & Video 360, Search Ads 360, YouTube Analytics, Google Sheets and Google BigQuery. Marketers can also connect to hundreds of other non-Google data sources.

A crop of data connectors from companies like Supermetrics, a digital analytics and reporting tool provider, has also sprouted to help marketers pull data from multiple sources into Data Studio.

More new features. Over the past two years, Google has steadily added features and capabilities to Data Studio. As of this month, Google Marketing Platform (GMP) users can access Data Studio reports in other GMP products, including Analytics, Optimize and Tag Manager. Among other updates, in August, Google added the ability to combine data from multiple sources in a single chart or table with data blending (shown above).

Google says millions now use the product.

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How to capitalize on the competitive advantage of real-time data analysis

Contributor Stela Yordanova explains how to capitalize on the competitive advantage provided by real-time data analysis.

The post How to capitalize on the competitive advantage of real-time data analysis appeared first on Marketing Land.

The Real-Time report in Google Analytics allows you to monitor website activity as it actually occurs on your website or app. The report is continuously updated, and website activity is reported just a few seconds after it happens. This immediacy of real-time data provides digital marketers with unique and valuable insights.

There are many ways you can use real-time reporting such as gauging the effectiveness of your mobile app through event tracking and monitoring one-day promotions on your site.  Today I want to focus on and recommend marketers use Google’s Real-Time report for three specific things:

  1. To quickly monitor results for short-term campaigns or promotional efforts.
  2. To track immediate interaction with newly published content.
  3. To test and verify Google Analytics and Google Tag Manager implementation.

Real-Time Overview

The Real-Time report contains an Overview plus five specific reports:

  • Location report.
  • Traffic Sources report.
  • Content report.
  • Events report.
  • Conversion report.

Each report is described below with suggestions on how marketers should use them to analyze real-time website data and improve marketing results.

 

[Read the full article on Search Engine Land.]

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Step-by-Step Guide for Google Analytics 2016

Google Analytics stands apart in a league of its own as the best tool for understanding your web traffic and conversions. Yet, as the best tool, it can be confusing and overwhelming to figure out. In this article, we first look at how you set up an account in Google Analytics, and then we look […]

The post Step-by-Step Guide for Google Analytics 2016 appeared first on Landing Page Optimization Blog.

Step-by-Step Guide for Google Analytics 2016

Google Analytics stands apart in a league of its own as the best tool for understanding your web traffic and conversions.

Yet, as the best tool, it can be confusing and overwhelming to figure out.

In this article, we first look at how you set up an account in Google Analytics, and then we look at some more advanced tips for making it work for you.

Here’s your step-by-step guide for Google Analytics:

Get Started

To make use of the wide reporting options, you first need to set up an account. Follow these straightforward steps:

  1. Visit Google Analytics.
  2. If you don’t have a Gmail address, you’ll need to create one.
  3. If you do have a Gmail address, simply sign in to your account.
  4. Once inside Google Analytics, name your account with your company name, website name and provide your website’s URL.
  5. Google will then provide you with tracking code. You’ll want to copy this code and insert it into all of the pages of your website. You can either add it yourself to your html code, or if you’re using WordPress, use a plugin to do it for you.
  6. Give it a few days before you start looking at reports.

Now that you’ve got the set up done, let’s dive into the advanced tips.

Set Up Goals

You want to track your conversions, so you will use conversion goals to measure them.

Setting up goals allows you to dig deep into the performance of your website. You’ll learn if your website visitors are actually doing what you want them to do.

To set up your goals, we recommend using the SMART method.

  • Specific
  • Measurable
  • Achievable
  • Relevant
  • Time based

You want to be extra sure that your goals are measurable.

So, the first thing you want to do is decide the action that defines a specific conversion on your website. Google Analytics then uses your goals to track the conversion. Once the goal is achieved, the conversion is logged.

Some examples of goals include:

  • Purchase
  • Newsletter sign up
  • Online registration
  • Information request
  • Download

Next, you want to either create or decide on a destination page. For example, if you want your user to sign up for your newsletter (this is the goal), then the destination page would be the thank you page they land on after completing the task.

This tells Google Analytics that your website visitor completed your form.

Once you’ve got your goal and your destination page, you can set up Goal Tracking.

  1. Visit the Admin tab.
  2. In the View column, click Goal.
  3. Click on +New Goal.
  4. Select a template that meets your needs. Choose from Revenue, Acquisition, Inquiry and Engagement. These are shown to you based on your industry.
  5. Click continue to name your goal.
  6. Choose how you will track your goals. In most instances, you’ll use your destination page.
  7. Click continue and paste in the URL of your destination page.

Measure Conversions

Your Goals are set, and Google Analytics will get to work recording your conversions.

It’s now time to measure your website conversions. Deciphering your reports can be confusing. Here’s how to view them.

You’ll notice a Goal Conversion Rate. This shows the percentage of your page visits that resulted in the conversion you defined in your goal.

This is a key part of your reporting as this is perhaps the best indicator to gauge the effectiveness of your page.

For example, if your conversion rate is high, you’re obviously bringing in good website traffic. This means you’re doing something right.

Yet, if your conversion rate is low, you need to change something on your website. It could be the image, headline, text, call to action, colors, etc. A low conversion rate means you aren’t meeting the needs of your customers.

It’s ideal when you see your goal conversion rate continue to increase over time. This means you’ve refined your landing pages for the best conversion rates.

Next, take a look at your Goal Completions. This number shows you exactly how many website visitors converted. This is a tangible number you can use in your marketing reports.

Third, you want to look at the Goal Value. This is very simple – it’s the monetary value of your conversions.

Conversions are great, but the Goal Value number tells you what each of those conversions is worth to you monetarily.

Understand Your Audience

Now, we’re going to touch on how to evaluate your audience in Google Analytics. Why is this important?

It lets you know if you’re reaching the right people in the right way. You’ll learn things about your website visitors such as:

  • Age
  • Gender
  • Location
  • Interests
  • Browsers
  • Mobile Devices

On the left-hand side in Google Analytics, you’ll see the category for your Audience. Open each of these sections, paying special attention to Demographics and Geo.

Get familiar with your Acquisition report as well. This report shows you how your website visitors arrived on your site.

This helps you know if it was email, social media, organic or pay-per-click.

Discovering the data in these reports helps you learn what works best for driving traffic to your website to help you increase your goal conversions.

Your audience reports can also tell you how many page views your website had. For example, if a visitor landed on your site and visited five pages, you can see where he went.

You can also view the number of new and returning visitors.

Think about your website goals. Are you after new or returning visitors? This is where you’ll see if you’re meeting your goals.

Take a look at your visitor flow as well. This helps you see where people came from.

To Conclude

In this guide, we’ve touched on just a few of the many things Google Analytics has to offer.

Once you’ve set up Google Analytics on your website, you have access to an abundance of information.

You want to use that information to make changes and additions to your website so you can continue to improve your conversion rate.

You’ll find it relatively easy to set up Google Analytics and view your first set of simple reports.

We do encourage you to get started with Goals as they can really help you gauge your conversion rate. Use our advanced tips to get started and stick with it.

The more you use Google Analytics, the more comfortable with it you’ll become.

Are you ready to squeeze more profit out of your website by tracking your conversions in Google Analytics? That’s terrific! We’re here to help you optimize your website so it works fluidly for your website visitors. In fact, we promise you we’ll do just that.

With our guarantee, you can rest assured we will increase your profits through landing page optimization.

If you’re ready to work with the leader in landing pages and conversion rate optimization, contact us today.

We’ll provide you with our FREE site performance analysis so we can work on your landing page conversion rates.

Image: Louis Llerna

The post Step-by-Step Guide for Google Analytics 2016 appeared first on Landing Page Optimization Blog.

The Best Google Analytics Reports for Improving Websites

Google Analytics isn’t just for knowing how much traffic your website is getting, your top pages, and how your traffic sources and marketing efforts are performing. Nope. There is an even better use for it!…

best google analytics reports

Google Analytics isn’t just for knowing how much traffic your website is getting, your top pages, and how your traffic sources and marketing efforts are performing. Nope. There is an even better use for it!

It’s also really important to use it to help improve your website – so it converts many more visitors into sales, leads or subscribers. But unfortunately Google Analytics can be a little daunting at times, particularly with seemingly endless reports to check out and analyze. Where should you start for best results?

To help you make sense of this, I’ve created a list of the best Google Analytics (GA) reports so you can quickly gain more insights into your website performance and what needs improving most. I have also recently included a video of me walking you through all these great reports. Let’s get started…

The best Google Analytics reports to improve your website

Update: Watch a video of me guiding you through all these key Google Analytics reports

Last year I created a premium video about these best Google Analytics reports. It was originally part of a paid membership but I have decided to now include it on this article for everyone to watch for free. In this video you will also learn how to create a Google Analytics dashboard for these reports. Enjoy!

Check the landing pages report for pages with high bounce rates and low conversion rates
Your top landing pages (entry pages) are crucial to optimize because they often get very high levels of traffic, and are the first pages your visitors see on your website. If visitors don’t find what they are looking for or are confused, they will leave your website often within just 10 seconds!

To improve your website with this report, pull up the your landing pages report for the last 30 days (found under ‘Behavior > Site Content > Landing pages’).  Then see which pages out of the top 10 have highest bounce rate (over 50% is high) and which have lower than website average goal conversion rate (both indicated below in yellow) – these are indicators of poorly performing pages on your website.

Google Analytics landing pages report bounce rate and conversion rate

Then optimize these poor page performers first – improving headlines, benefits, imagery and call-to-action buttons are some of the best ways to do this. Optimizing these helps increase visitor engagement and increases the chances of them converting for your key website goals.

Use In-Page Analytics feature to reveal exactly what visitors click on
Don’t presume you know what visitors are doing on your pages, and what they are clicking on – it can often be different than you might expect. Use this great click map feature in GA (found under ‘Behavior > In-Page Analytics’) on your key pages to gain better insights into your visitors journey and flow around your website.

Then based on what insights you find, to improve your website you should focus your visitor’s experience on more important links. This can be done by deemphasizing less useful links (or removing them) from key pages, and reorganize your navigation menus to focus on major website goals.

Google Analytics In-page Analytics

Note: Ideally you should turn on the ‘enhanced link attribution‘ option in your settings – this makes the clicks more accurate for when you have multiple links on one page going to the same destination page.

Check the browser report for poor conversion rate performers
Your webpages can sometimes look slightly different or even break in some browsers (often due to small differences in how browsers show CSS code). This can unknowingly cause many lost sales or leads!

To make sure this isn’t negatively impacting your website, you need to regular check the ‘Browser & OS’ report (found under ‘Audience > Technology ) and make sure your conversion rates aren’t much lower for any browsers. If you see ones on this report that are much lower, you should go ahead and check for technical problems like CSS rendering issues and fix them immediately.

Google Analytics browser report

Analyze your Funnel Visualization report for high-drop off rates and optimize
It doesn’t matter how good your website is if visitors struggle to get through your checkout or sign-up flow pages. To understand how well your visitors complete that process, its vital you check your Funnel Visualization report. On this report (found under ‘Conversions > Goals > Funnel Visualization’) you can see how many visitors get through each page of your funnel (like your billing page), and which pages are most problematic – even where they go if they go to another page.

You need to pay great attention to any pages with a high drop off rate (more than 40%) and optimize those first – adding security seals and risk reducers, reducing distractions like header navigation, and improving error handling often work well. Improving these pages will greatly increase your sales or signups.

Google Analytics funnel visualization report

Note: Obviously you will need to have made sure you have setup your goals for your website adequately, including adding key pages in your goal flows. Here is a great guide on setting goals up.

Check your traffic overview report for poor performing traffic sources
Improving the quality and quantity of your traffic has huge impact on your website conversion rates, sales or leads, and its vital you gain insights into traffic performance and optimize the major sources.

To help you gain greater insights into this, pull up the ‘Channels’ report as Google calls it (found under ‘Acquisition > Channels) and check which of your top 10 traffic sources (channels) have high bounce rates (over 50%), and also for sources that seem low or missing from the top 10.

For example, you may find your email traffic isn’t as much as you had hoped for or isn’t converting well, so you should optimize your email marketing campaigns soon. Same goes for your paid search and SEO too.

Google Analytics acquisition overview report

Use the mobile overview report for tablet/mobile insights
Mobile traffic is bigger than ever before, often accounting for over 20% of total website traffic – and these visitors have very different needs due to smaller screen sizes, and often convert much lower than regular website traffic.

To understand your mobile traffic, and its performance, you need to check your ‘mobile overview’ report. Here you need to see just how high your traffic levels are for both mobile and tablet devices, and see what the conversion rate for each is. If conversion rate is much lower for any, you need to check your website on that device for key issues and fix immediately.

And if you haven’t already done so, to increase your conversion rates it’s critical to have a mobile optimized website as soon as possible (like using responsive design), particularly if your mobile traffic is over 20%.

Google Analytics mobile overview report

Check the exit pages report to find problematic pages
You also need to find out which pages are most often causing your visitors to leave (called an ‘exit’ page) – and improve and optimize those too.

To find these top exit pages, check your ‘exit pages’ report (found under ‘Behavior > Site Content > Exit Pages). In particular look for any pages that shouldn’t be in the top 10, and try to figure out why so many people exit your site on them. Also look for pages with especially high exit rate (over 50%), as this often indicates problems.

A few ways to improve these exit pages is by using and optimizing call-to-actions at the end of them, and try using exit intent popups to show a great incentive (discounts/free guides etc).

Google Analytics exit pages report

Analyze the top pages report for key missing pages and high exit rates
Your top pages report can contain some real gems for insights – not just what your top 10 pages currently are (found under ‘Behavior > Site Content > All Pages).

First you can see if any of your top pages have high exit rates (important to optimize those ASAP) and also to check if any pages relating to your key goals seem missing from this report or have low traffic. For example, perhaps few people are visiting your ‘why us’ or benefits page – making links more prominent to this page will hopefully increase sales/leads.

Google Analytics top pages report

These are the simpler reports, there’s many advanced ones too

These are just some of the simpler Google Analytics reports that will help you improve your website. Here are a couple of the many more advanced ones to learn about:

  • Using the ‘Converters’ visitor segment to figure out the behavior of people who convert for your main website goals (sales/leads etc).
  • Using the ‘Site Search’ report to find pages causing most amount of internal searches (indicates visitors not finding what they need).

If you are interested in learning more about these advanced GA reports, simply comment and let me know.

No time to analyze Google Analytics reports or not good at it?

If you don’t have time or the skills to gain insights from your Google Analytics reports you should check out my ‘Google Analytics Insights’ service – I’m sure you will find it useful for  improving your website.

How to Use Google Analytics Content Grouping: 4 Business Examples

Content Grouping is a useful feature that let’s you group your website or app content together and view aggregate metrics for each group. This is particularly useful if you have a lot of content to analyze. Rolling up your content, based on your specific business structure, is very helpful when creating dashboards and other custom […]

How to Use Google Analytics Content Grouping: 4 Business Examples is a post from: Analytics Talk by Justin Cutroni

The post How to Use Google Analytics Content Grouping: 4 Business Examples appeared first on Analytics Talk.

Content Grouping is a useful feature that let’s you group your website or app content together and view aggregate metrics for each group. This is particularly useful if you have a lot of content to analyze. Rolling up your content, based on your specific business structure, is very helpful when creating dashboards and other custom reports.

In this post I’ll talk about how to actually use the data and walk through some examples for various business types.

If you have not set up content groupings, please check out my post on how to set up Google Analytics content groupings.

Standard GA Reports

Your content groupings are available in Google Analytics behavior reports. Navigate to the Behavior > Site Content > All Pages report. Notice at the top of the data table there is a selector for the primary dimension. This drop down list all of the content groupings that you added to Google Analytics.

Use the selector to choose a specific content grouping in your Google Analytics Content reports.

Use the selector to choose a specific content grouping in your Google Analytics Content reports.

This selector also exists in the navigation flow, so rather than viewing how users move from page to page, you can view how users move between the different types of content on your site.

You can also use your content groupings in the Navigation Summary report.

You can also use your content groupings in the Navigation Summary report.

Very handy for understanding the behavior of users!

It also exists in many other content reports, like the Landing Pages report and the Site Speed Page Timings report.

But who uses the standard reports these days? :) Analysis driven organisations use Custom Reports and Dashboards. Let’s look at how you can use content groupings in both features.

Custom Reports & Dashboards with Content Groupings

When you create a content grouping, Google Analytics will create a dimension for each content grouping.

Remember, a content grouping contains a number of groups, and each group can contain a number of pages or screens.

Each content grouping contains multiple content groups. A content group contains multiple pieces of content.

Each content grouping contains multiple content groups. A content group contains multiple pieces of content.

This means that the values for the content grouping dimension will be all of the content groups that you created within that grouping.

You can create up to five content groupings in Google Analytics, therefore you could have five new dimensions, one for each content grouping.

Use the content grouping dimensions just like you would any other dimension. Here’s a simple custom report that shows some a potential content grouping for a blog.

You can use your content groupings in a Google Analytics custom report.

You can use your content groupings in a Google Analytics custom report.

Then, when you look at the report, you’ll see something like this:

When you add a content grouping to a Google Analytics custom report, the data will be aggregated based on content group.

When you add a content grouping to a Google Analytics custom report, the data will be aggregated based on content group.

Note: I added this custom report to the Google Analytics solutions gallery. You can add it directly to your account here.

You can also use the content grouping dimension in your dashboards. Here is a very simple example using the page value metric and the content grouping dimension.

You can also use the Content Grouping dimension in a Google Analytics Custom Dashboard.

You can also use the Content Grouping dimension in a Google Analytics Custom Dashboard.

That’s really all there is to using content grouping in Google Analytics custom reports and custom dashboards. No go and give it a try!

One other note – the content grouping dimensions are hit level dimensions. This means that you can only use them with hit level metrics, like pageview, time on page, etc. You can not use them with session level metrics, like conversion rate, or revenue per visit.

Content Grouping Strategies

To really take advantage of content groupings you need to plan your content grouping carefully. You need to understand how your organization wants to analyze this data. So let’s look at a how different types of businesses might use content grouping.

Ecommerce: Patagonia.com

Patagonia sells outdoor equipment for men, women and children. They’re known for their ethos that you should travel “fast and light” when in the outdoors – take only what you need. They’re also known for their environmental advocacy. They incorporate both of these messages into their marketing stories.

Effectively breaking down the content structure could help each department at Patagonia better understand their marketing initiatives and site optimization efforts.

So how might we create a content grouping strategy based on their business?

Google Analytics Content Grouping can be used to organize the content on an ecommerce website.

Google Analytics Content Grouping can be used to organize the content on an ecommerce website.

Product pages: I would start by grouping all product pages together. It’s really important to understand what percentage of your users are making it to product pages. If people don’t look at product pages then they usually can’t buy something. And I’d take it one step further – group product pages by product line. I’d also be sure to differentiate category pages from the generic product pages.

You can mimic your product architecture with your content groupings.

You can mimic your product architecture with your content groupings.

Special selling tools: One cool feature that the Patagonia site has is the ‘kit builder’. This is a tool that let’s a customer build the best clothing combination for different conditions or activities. This is another section that could really use it’s own content group.

Special shopping tools can be categorized in their own group.

Special shopping tools can be categorized in their own group.

Checkout pages: Next I’d group all checkout pages together. These are all the pages in your checkout process. The percentage of people that see checkout pages might be very small, but I like to put these pages in their own group. They’re not product related, and they’re not marketing related. So they need their own group.

Account management pages: Many ecommerce sites let customers manage account settings, check the status of their order, manage returns, etc. I would lump all of these pages together in an Account Management group.

Marketing pages: Now we get into a large chunk of the content – marketing pages. Patagonia has a lot of information about their brand, and initiatives. Rather than lump all of this together as just Marketing pages, I would actually break all of this up into groups based on the different initiatives.

In the case of Patagonia I would use all of these different groups that you can see in the navigation.

Use a Google Analytics Content Grouping to categories marketing pages.

Use a Google Analytics Content Grouping to categories marketing pages.

Support pages: Business is all about relationships – and that’s represented by different types of support content. We can create a support group that containing any materials related to support. Again, you can create sub-groups for different types of support content (product support, order support, etc.)

Error pages: I like to group all error pages into a single group, then I can drill into the group and view the specific errors. This group can contain all different kinds of errors, depending on your personal preference. It could be technical errors, like 404 or 502 errors. Or it could be more functional errors, like when a user adds an incorrect credit card number during their purchase.

Software as a Service: Mailchimp.com

Mailchimp is a popular service that helps businesses manage their email marketing initiatives. Like all SaaS sites it’s primarily divided into two sections: a marketing section and an application section. The content grouping will mimic this general structure of content.

Product marketing pages: If people are going to sign up for the Mailchimp service then they need to know about the features! Product marketing page are pages dedicated to product information, this includes information about price, features, etc.

For a SaaS site, create groups for different kinds of marketing content.

For a SaaS site, create groups for different kinds of marketing content.

In addition to specific product information, there’s also a lot of thought leadership material to help drive marketing.

Marketing content pages: These pages are non-product marketing pages that help you demonstrate your thought leadership. It may be blog pages, or other content. In our example of mailchimp.com, there might be multiple groups. For example, they have a blog, but they also have a ton of research about email marketing. I would put this material in a marketing content group. Or even better, in the Reports group!

I would create a Google Analytics content group for the research reports on the MailChimp site.

I would create a Google Analytics content group for the research reports on the MailChimp site.

Application pages: The other side to a SaaS site is the actual application. This is the section of the site where you log in and actually use the product. Like the marketing pages, there can be many different types of application pages. Let’s go back to our example of Mailchimp.com. I would break down the content based on product features.

Perhaps we could use the application navigation as a template for the content structure.

You can create different groups for each part of the online application.

You can create different groups for each part of the online application.

Account management pages: Here’s another example of grouping different parts of the application together. We could easily group together the pages that control account management. And you can see from the image above that there are sections of the app dedicated to other functionality – all should be grouped accordingly.

Error pages: Like other types of sites it’s a good idea to group all error pages together. See the ecommerce section above for more details. These groups can be both website errors or application errors – like a login error page.

Gaming Application: Clash of Clans

We all use our mobile devices for incredibly important things, like waging medieval warfare on other clans! HA! Anyone out there like Clash of the Clans?

You can categorize app content using Google Analytics Content Groupings.

You can categorize app content using Google Analytics Content Groupings.

In reality, gaming apps are very similar to other business models – like publishing and commerce. Some games generate revenue from in-game ads while others up-sell users on features, like new levels. Some do both. We can group games content together just like we do ecommerce.

Game level screens: Most of the content for a game is probably level based. We can replicate this base structure in Google Analytics. If you’re a fan of Clash of the Clans then you there are other parts to the game in addition to levels. There are attack screens, chat windows, etc. All of these screens can be added to groups to roll-up the data.

Ecommerce screens: These screens are used to sell the user on pay features. In the case of Clash of Clans you can buy more gems, which can then be used to purchase other items, like more armies!

I would put all ecommerce app screens into a separate content group.

I would put all ecommerce app screens into a separate content group.

Configuration screens: Most apps have a configuration section. This is where the user can change everything from the language, to colors, etc.

Error screens: Last but not least we have error screens. Again, these can be technical app errors or functional errors, like login issues.

For Publishers: MarketingLand.com

Let’s face it, content grouping was made for the publishing industry! They’re the ones that have to organize thousands of pages of content. I don’t want to dwell on publishing too much, but let’s take a look at MarketingLand.com, a popular destination for anyone working in the digital marketing world.

I’ve actually written about how to customize google analytics for publishing sites in the posts Custom Variables for Publishers and how to measure how far users scroll down a page. I think both of those techniques still apply.

But now, if you’re a publisher, you can also use content groupings to organize the data about your content. This provides one more way to roll up data for analysis.

Content Category: Almost all publishers group content by category – and now this can be done with the content grouping feature.

Publishers can create content groups based on the organization of their content.

Publishers can create content groups based on the organization of their content.

Some publishing sites organize content in other ways, like by author or publication date. I would suggest creating content groups for topic categorization, and custom dimensions for any secondary organization (author, date, etc.)

Account pages: Some publishers, like the New York Times, offer a premium membership service. This is not the case with MarketingLand.com. But, if it did have a member’s section, you could group all of those pages together.

Error pages: Do I need to go over this again :)

I hope this post provides some inspiration for how you might use Content Grouping for your business. Ultimately how you organize your content groupings will be based on your organization. There is no right or wrong – just use a structure that is useful.

Questions or comment? Leave a note below – and happy grouping!

How to Use Google Analytics Content Grouping: 4 Business Examples is a post from: Analytics Talk by Justin Cutroni

The post How to Use Google Analytics Content Grouping: 4 Business Examples appeared first on Analytics Talk.

How to Set Up Google Analytics Content Grouping

Today everyone is creating content – lots and lots of content. Measuring that content can be a challenge given the sheer volume that’s out there. That’s where Google Analytics Content Grouping can help. This feature let’s you categorize your content based on your own business rules. Then, rather than view your data based on page […]

How to Set Up Google Analytics Content Grouping is a post from: Analytics Talk by Justin Cutroni

The post How to Set Up Google Analytics Content Grouping appeared first on Analytics Talk.

Today everyone is creating content – lots and lots of content. Measuring that content can be a challenge given the sheer volume that’s out there. That’s where Google Analytics Content Grouping can help.

This feature let’s you categorize your content based on your own business rules. Then, rather than view your data based on page URL or screen name, you can view based on your specific groups.

In this post I’m going to talk about how content grouping works and how you set it up.

Key Vocabulary: Groupings and Groups

There is a little terminology we need to cover before we get into the setup: groupings and groups.

You can create multiple content groupings in Google Analytics.

Within a grouping you can create multiple content groups.

A group is a collection of content. It could be pages in a certain section of your website. Or it might be screens from a certain part of your app. It can be just about anything.

A grouping is just a bunch of groups.

Each content grouping contains multiple content groups. A content group contains multiple pieces of content.

Each content grouping contains multiple content groups. A content group contains multiple pieces of content.

You can create multiple content groupings in Google Analytics and switch between them in the reports.

Here’s an example. For my blog I created a grouping called Blog Content Categories.

Within that grouping I create a number of groups to categorize the different types of content on my blog. There’s a group for posts, a group for about me pages, a group for error pages, etc. In the configuration I created a rule that puts each page in a group based on the structure of the URL.

You can view your content data based on groups, rather than URL, screen name or title.

You can view your content data based on groups, rather than URL, screen name or title.

Any item that is not added to a group will appear in the (not set) content group.

It’s important to know that there is not one specific report where you access this data. When you create a grouping it’s literally becomes a new dimension of data. You choose to view that dimension in almost all of the content reports.

Let’s take a look at how you actually create a grouping and groups.

Creating Groupings & Groups

Google Analytics does not automatically create content groupings – you must configure the tool to do that. Navigate to the settings for a specific view and choose Content Groupings.

Content Grouping is a view level setting.

Content Grouping is a view level setting.

Here you will see a list of all your groupings. You can choose to create a new group or edit an existing group.

Here's a list of your Google Analytics content groupings. You can add or edit groupings here.

Here’s a list of your Google Analytics content groupings. You can add or edit groupings here.

There are three methods you can use to create a content group – let’s take a look at each.

Tracking Code Method

This method requires you to add a small piece of code to each page on your site or in your app. The code will literally set the name of the content group when the page or screen renders. Here’s how the code would look for Universal Analytics:

ga('create', 'UA-XXXXXXXX-Y', 'example.com');
ga('set', 'contentGroup5', 'Group Name');
ga('send', 'pageview');

Or, if you’re working in iOS the code might look like this:

id tracker = [[GAI sharedInstance] trackerWithTrackingId:@"UA-XXXX-Y"];
[tracker set:[GAIFields contentGroupForIndex:5]
value:@"Group Name"];

The code for a content group is similar to the code for a custom dimension. You can set 5 content groups using the tracking code. Each group is associated with a number, one through five, as shown in the example above.

Check the Google Analytics support documentation for more code examples.

Basically this method let’s you suck in the group name, via code, from some other system. It might be a CMS, a data layer, or just the HTML of the page.

The key is that you somehow add the name of the group to the Google Analytics code.

Pros: Using the tracking code method you can use code to automatically adjust to changes in your content and new content groups.

Cons: It requires IT involvement to set up. But once it’s configured very little IT support.

I should also mention that content grouping is coming to Google Tag Manager. This will provide another way to programmatically set the content group – so stay tuned.

Extraction Method

The extraction method extracts (get it) the name of your content groups from an existing dimension of data. The idea is that you use a regular expression to parse the dimension and automatically extract the name of your group.

For example, the name of your content groups might be in the page title, like this:

Your website might use the name of the content in the Page Title or Screen Name dimension.

Your website might use the name of the content in the Page Title or Screen Name dimension.

I would need to specify that my group name is in the Page Title dimension, and then provide a regular expression that extracts the appropriate value.

The content grouping extract method will automatically pull the name for a content group from a dimension of data.

The content grouping extract method will automatically pull the name for a content group from a dimension of data.

For those of you that do not use regular expression, the value in the parenthesis will automatically be extracted. Google Analytics will then use the value as the group name.

You can see that this one rule will work for every product page on my site – as long as they are well formatted.

Pros: No coding involved. Flexible collection.

Cons: You might need to update your regular expressions when you add new content to your site or app. Specifically something that does not match your existing rules. Believe me – updating settings SUCKS. People forget to do it all the time.

In you’re new to regular expressions check out this reg ex tutorial in the Google Analytics help center.

Rules Method

The rules method is almost exactly like the extract method. The ONLY difference is that you have to MANUALLY name the group. The value for the name is not automatically pulled from a dimension of data.

The content grouping extract method will automatically pull the name for a content group from a dimension of data.

The content grouping extract method will automatically pull the name for a content group from a dimension of data.

Like the extract method you can create rules based on different dimensions of data- the page title, page url or the screen name. If the dimension value matches the rule then the content is added to the group.

Pros: No coding. Don’t need to know regular expressions.

Cons: You need to remember to update your rules when you add new content or if your site urls or app screen names change. Again – updating your analytics settings SUCKS. People forget to do it all the time.

Which method should you use?

That’s a tough question. Personally, I think page category is a critical piece of data that should be added to a page data layer. If you take this approach then using the tracking code method is very scalable.

I also like the extract method. It’s very flexible and reliable – as long as you have processes in place to maintain your implementation :)

Important things to know

Ok, so here are a few very important things to know.

You can use all three methods for creating groups within the same content grouping.

The grouping logic is applied to your data sequentially. That means that Google Analytics first applies the tracking code method first. Then it applies the extraction method. And finally it applies the rules method. You can use all three methods for your implementation.

When a page or screen matches a rule it is added to that group.

A page or screen can only be in ONE content group at a time! That means that an page or screen can only belong to one group at a time.

And finally, content groups are NOT applied to historical data. They are only applied from the moment you configure the feature.

A Best Practice

Because Google Analytics applies all grouping methods to your data, it is possible to use a combination of grouping methods.

But, because they they are applied SEQUENTIALLY, it’s a good idea to put your very specific grouping rules first, followed by your general rules. This way the later, general rules will catch anything that slips through the early, specific rules.

Content Group methods are applied sequentially.

All three content grouping methods are applied to each piece of content. They are applied sequentially.

It’s really, really important to try and get your groups right the first time. While you can edit your groups, there is no way to change the data that has already been processed.

Make sure you test your groups first before announcing them to your entire team.

It’s also a good idea to add an annotation to Google Analytics so everyone knows when the data was added.

Ok, I think that’s it for how to implement this feature.

Don’t worry – I’ll explain how to use content groups in a couple of days.

How to Set Up Google Analytics Content Grouping is a post from: Analytics Talk by Justin Cutroni

The post How to Set Up Google Analytics Content Grouping appeared first on Analytics Talk.

Dimension Widening: Import data directly into Google Analytics

There are lots of different ways to put data in Google Analytics. You can collect data from a website with JavaScript. You can collect data from an app using an SDK (Android or iOS). Or you can collect data from any network connected device using the measurement protocol. But there’s another way to add data […]

Dimension Widening: Import data directly into Google Analytics is a post from: Analytics Talk by Justin Cutroni

The post Dimension Widening: Import data directly into Google Analytics appeared first on Analytics Talk.

There are lots of different ways to put data in Google Analytics. You can collect data from a website with JavaScript. You can collect data from an app using an SDK (Android or iOS). Or you can collect data from any network connected device using the measurement protocol.

But there’s another way to add data to Google Analytics – you can import data using a feature called Dimension Widening.

You can add data to Google Analytics a number of ways - including Dimension Widening.

You can add data to Google Analytics a number of ways – including Dimension Widening.

With Dimension widening you can import additional dimensions and metrics directly into Google Analytics via a CSV upload or programmatically import data via an API.

Let’s take a look at how you might use Dimension Widening to augment the data in your account, and ultimately do better analysis.

Why Add More Data?

Analytics is more valuable when you can align the tool more closely with your business strategies and tactics. Adding additional data, like customer history, content publishing information, advertising cost data, etc. can help provide context to your data, thus making it easier to gauge performance and identify opportunities for improvements.

Adding additional data can also streamline your reporting (yes, basic reporting still happens) by consolidating all of your data in a single system that everyone has access to.

That’s where Dimension Widening comes in.

It is a mechanism to move data into Google Analytics.

How Dimension Widening works

You can upload two types of data to Google Analytics: Dimensions and Metrics.

A dimension is an attribute of a user or the sessions she creates.

A metric counts something – like time, money, clicks, etc.

When you use Dimension Widening you are uploading values for one or more dimensions or metrics.

You can upload values for existing dimensions/metrics or you can upload values for new dimensions/metrics that do not exist in Google Analytics.

When Google Analytics process the data it will join your custom data to the the existing data using something called a key.

The key binds your data, the data uploaded in a CSV file or sent programatically, to the Google Analytics data. When Google processes the custom data it will look at the value for the key, and then try to find the same value in the Google Analytics data.

If Google Analytics finds a matching keys then it will take the data in that row of the custom data and pull it into Google Analytics.

The key links your custom data to the data in Google Analytics.

The key links your custom data to the data in Google Analytics.

There are four basic steps to configuring Dimensioning Widening.

1. Identifying the data you want to import.

Step one is really simple, identify the data that you want to add to Google Analytics.

Remember, you can import a value for any dimension or metric that currently exists in Google Analytics. OR you can import values for custom dimensions and custom metrics that are not normally found in GA – more on this below.

When choosing the data you want to import ask yourself this – what data to I need to understand the behavior of my users? How can I make my analytics life easier by consolidating data in Google Analytics?

You also need to define your key. This is obviously critical. If you can’t define a key then you can’t import data.

2. Create the schema in Google Analytics.

Once you define your key and the dimensions/metrics you want to import it’s time to add the schema to Google Analytics. Think of this step as telling Google Analytics how to interpret the CSV file (or data feed) that you will import.

Choose a property in the admin section, then choose Data import and Dimension Widening.

The Dimension Widening settings are in the Data Import section of a property.

The Dimension Widening settings are in the Data Import section of a property.

To begin you need to name the data set you will import. You can actually upload multiple data sets (more on this later), so make sure you name it something very descriptive, like “Campaign Data” or “Content Information”.

Then choose the view where you would like the data applied.

Every data set must have a name, and you must specify which views to apply the data to.

Every data set must have a name, and you must specify which views to apply the data to.

TIP: Dimension widening will permanently change the data in a reporting view! It’s a good idea to test your dimension widening on a TEST view before applying it to your main reporting view.

Now add the schema. First, add the key that you’ve defined for your data.

Next, specify the dimensions and metrics that you want to add.

You must enter a schema into Google Analytics. Add the key along with the dimensions you would like to widen.

You must enter a schema into Google Analytics. Add the key along with the dimensions you would like to widen.

Here’s something cool – as you choose your key and dimensions Google Analytics will automatically show you the column headings that you will need to add to your CSV file.

As you add your schema Google Analytics will provide the column headers for your CSV file.

As you add your schema Google Analytics will provide the column headers for your CSV file.

Notice that they’re not the names that appear in the drop down boxes. They’re the dimension/metric names that are used in the API. Fear not – you don’t need to understand what they mean.

3. Build your CSV file.

Once you finish defining your schema choose save.

You’ll be presented with two options: get more details of your CSV file OR get an API key to upload your data programatically. Let’s focus on the Get Schema option.

Once you define your dimension widening schema you can download a CSV template or get an API key.

Once you define your dimension widening schema you can download a CSV template or get an API key.

Click the Get Schema button.

This window contains some really useful information. First, a list of the column headers that you need to add to your CSV file. This includes your key and all the other dimensions that you are adding to Google Analytics.

There’s also a way to download a CSV template for your specific data. The template is just an Excel file with the headers added to the first row.

Google Analytics will provide the column headers for your CSV and provide a CSV template that you can fill with your data.

Google Analytics will provide the column headers for your CSV and provide a CSV template that you can fill with your data.

4. Upload your CSV file or Send Data via API

Remember, there are two ways to add your data – via an API or manually via a file upload process. Let’s focus on the later – the file upload.

This isn’t too complicated, just click upload :) Once the file is uploaded Google Analytics will widen your data as it is processed.

You can check on the processing of your data using the Refresh button.

You can check on the processing of your data using the Refresh button.

NOTE: when you use Dimension Widening the data you import is NOT applied to historical data. Your data is only applied going forward.

I find that GA can process the file very fast (minutes). You may want to refresh your list often to determine if the new data has been added.

That’s it! That’s the basic process.

But you probably want to use Dimension Widening to import custom data, not data that’s already in Google Analytics. Let’s take a look at how to do that.

How to add Custom Data

You can also add custom dimensions and custom metrics to Google Analytics via dimension widening. The process is almost exactly the same. The only difference is that you must first define your custom dimensions or metrics in the Google Analytics admin section.

To upload a dimension or metric that does not exist in Google Analytics you must first define those custom dimensions or metrics.

To upload a dimension or metric that does not exist in Google Analytics you must first define those custom dimensions or metrics.

There’s not a lot of configuration here. Just give your dimension a name and choose a scope.

NOTE: You can only widen between dimensions and metrics of same scope. For example, you can’t widen from user scope Key to Hit scope dimensions. Check out this (somewhat old) article on Custom Variables to learn more about scope.

That’s it. Now you can choose these custom dimensions (or metrics) when you add your schema for Dimension Widening.

Once you define a custom piece of data it will be available in the Custom Data schema interface.

Once you define a custom piece of data it will be available in the Custom Data schema interface.

Then create your CSV file with the correct headers and upload your data.

Note: Custom Dimension and metrics are only available in Google Analytics customizations – this includes custom reports, custom segments and dashboards. They can also be used in certain analysis tools, like secondary dimensions.

An Example: Uploading simple publisher data

Let’s say I’m a publisher. I want to add the publication year, author for each article. My key to join my data with GA data is the URL of each page. I already defined two custom dimensions, one for page publication year and one for page author.

I’m going to define my data schema in Google Analytics.

Defining a custom data in your dimensions widening schema.

Defining a custom data in your dimensions widening schema.

Now I build my CSV file using the correct headers for my key and dimensions that I would like to widen.

A sample CSV file with custom dimensions.

A sample CSV file with custom dimensions.

Next I upload my file…

And finally, I have data in my custom dimensions. Here I can see the data in a Custom Report.

Custom Dimensions can be used in a Custom Report, Unified segment or other customization features.

Custom Dimensions can be used in a Custom Report, Unified segment or other customization features.

Best Practices for Managing CSV files

You might want to widen your data based on multiple keys. For example, you might want to widen your product data (using the product ID as a key) and your campaign data (using campaign name as a key).

In this case you’ll need to define two different schemas and upload two different CSV files. Make sure you name them something logical!

Another thing to consider is when to update your CSV files.

For example, let’s say that you’re a publisher, and you’re uploading new data about your content. But you’re publishing new content every day. And probably multiple times a day. You would need to upload a new CSV file every time you publish content. This is too manual. In case you probably want to consider a programmatic solution.

Use the CSV file for things that do not change often. Use the API for things that change a lot!

What about JavaScript and real-time collection?

Given my previous example, you may be asking yourself, “can’t I just collect custom data in real-time using JavaScript?”

Absolutely!

You could do something fancy, like add the data to a data layer, then pull it into some custom dimensions. No problem!

The point is that you don’t always have the time or the IT resource to implement the data collection. Even if you use a cool technology like tag management, it may be that the data you want to add comes from an isolated system. And that it would take too much effort to transport the data from it’s home all the way to the web server.

Dimension widening can be seen as a somewhat faster, less IT intensive way of joining your data together.

Things to be aware of…

Ok, a few things that you need to be aware of when using Dimension widening.

1. Your data is NOT applied to historical data. Your data is only applied going forward.

2. You can NOT widen on ALL dimensions. You can NOT widen on the following dimensions:

  • custom variables
  • product dimensions and metrics
  • campaign dimensions
  • time-based dimensions (hour, minute, etc)
  • geo-dimensions (country, city, etc)

3. If you would like to expand your dimensions and populate Custom Dimensions you MUST use Universal Analytics. The reason is that Custom Dimensions only exist in Universal Analytics. They do not exist in the previous version of Google Analytics.

4. You can not change a schema once it has been entered into Google Analytics. You must delete the schema and then define your new schema.

I know some of these caveats may seem limiting, but remember, this is just the initial version. I know the team is working hard to expand the functionality.

Do you think you will use Dimension widening? If so how? Feel free to share your examples below!

Dimension Widening: Import data directly into Google Analytics is a post from: Analytics Talk by Justin Cutroni

The post Dimension Widening: Import data directly into Google Analytics appeared first on Analytics Talk.