Introducing the Google Analytics Sample Dataset for BigQuery

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The Google Analytics 360 integration with Google BigQuery gives analysts the opportunity to glean new business insights by accessing session and hit level data and combining it with separate data sets. Organizations and developers can analyze unsampled analytics data in seconds through BigQuery, a web service that lets developers and businesses conduct interactive analysis of big data sets and tap into powerful data analytics.

To help you learn or teach practical experience with analyzing analytics data in BigQuery, we are pleased to announce the availability of a Google Analytics sample dataset. This is accessible directly through the BigQuery interface. The dataset includes data from the Google Merchandise Store, an Ecommerce site that sells Google branded merchandise. The typical Google Analytics 360 data you would expect to see such as AdWords, Goals and Enhanced Ecommerce data can be queried. You can see the fields part of the export schema that you can query here.

Google Analytics Sample Dataset for BigQuery

When it comes to helping businesses ask advanced questions on unsampled Google Analytics data, we like to use BigQuery. Its fast and scalable for big data analytics. When providing trainings on the benefits of the Google Analytics 360 and BigQuery integration, there is nothing like having a high quality dataset with sufficient volume to be meaningful. That’s why we are so pleased to see the public availability of a robust Google Analytics sample dataset with marketing and ecommerce data. Everyone can experience big data analytics!
– Doug Hall, Director of Analytics, Conversion Works

Self-Learning 
You can use the sample dataset to learn how granular information can be extracted from analytics data in BigQuery. We’ve created this guide to help you create queries to find answers to the following for the Google Merchandise Store:

  • What is the average number of transactions per purchaser?
  • What is the percentage of stock sold per product?
  • What is the average bounce rate per marketing channel segmented by purchasers?
  • What are the products purchased by customers who previously purchased a particular product?
  • What is the average number of user interactions before a purchase?

Education Programs

If you’re an educator trying to teach others to use BigQuery, then we encourage you to use the sample dataset as a tool. You can use it to create task based assessments and other learning materials for your students. We’ve started to do just that by integrating it into our education courses.

The Analytics Academy provides an introduction to BigQuery in their Getting Started with Google Analytics 360 course. The Data Insights course by the Google Cloud team provides an in-depth look at BigQuery with practical exercises.

Access the Dataset
You can learn more about the dataset including how to get access in this help article. If you need some help, please let us know through the Advertiser Community. Share any feature requests or ideas to make the dataset more useful. We hope the dataset gives you a practical way to learn about the benefits of analysing Google Analytics data in BigQuery.

Happy analyzing!
Posted by Deepak Aujla, Program Manager, Google Analytics Solutions

March 31st 2018 Uncategorized

Is Optimize set up correctly on your site? Let us double-check for you.

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Google Optimize helps businesses determine which website experiences work best for their customers. With easy-to-use A/B testing capabilities, marketers can use Optimize to create and launch a test in minutes — and manage the entire process on their own.

A/B testing with tools like Optimize drives results. In a recent survey, 72% of marketers found A/B testing to be a highly valuable method for improving conversion rates.

But we’ve heard from businesses that while testing in Optimize is easy, setting it up could be easier. 

Good news: We’re introducing a new feature that helps make sure Optimize is set up and working the way it’s supposed to.

Let Optimize double-check your code 

With Installation Diagnostics, Optimize will automatically alert and advise you on potential issues with your Optimize code each time you create a test. At a glance, you’ll see if your Optimize setup is correct and if you’re ready to run a test.

Let’s say you create a new page on your site but forget to add the Optimize code. Later, when you are creating a test for that page, Optimize will tell you that the necessary code isn’t installed. You’ll have the opportunity to fix the issue and then launch your test. Moving forward, you can be confident that your test results won’t be disrupted by an incorrect setup.

Optimize will alert you about other issues too – including if Google Analytics code isn’t installed on a page, or if an old version of Analytics code is installed. Then you can make changes to ensure you’ll be able to measure the performance of your experiment. Learn more.

Try it yourself 

Today’s top businesses are testing their way to success. They’re valuing data over opinions. They’re constantly learning — even from failures and mistakes. And they’re using their findings to improve the customer experience.

Whether your business is big, small, or somewhere in between, you can follow their lead. Try Optimize now and get ready to gain deep insights about your customers so you can enhance their web experiences like never before.

March 30th 2018 Uncategorized

Google Surveys goes global

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We’re excited to announce that you can now survey people in over 50 countries around the globe with Google Surveys.

When we first launched Google Surveys in 2012 (we called ourselves Google Consumer Surveys back then), our goal was to put quality market research in the hands of businesses of all sizes. Market research was costly and time-consuming in those days, and that made it hard for many companies to make decisions based on what their customers were actually thinking.

Google Surveys made it easy to keep your finger on the pulse of your customers with fast, reliable insights from real people. Since then, we’ve continued making improvements to the product. We also launched Surveys 360, our enterprise version, with advanced targeting, reporting, and sharing features.

And now users of both versions can reach people in more than 50 countries. Wondering if your new product line should launch in Portugal? Stop guessing and find out. Lacking competitive intel in India? Not anymore. Trying to convince your boss that your idea is better? We can help with that.

“As a global company, we’re especially excited about the targeting expansion to over 50 new countries. We’ll be using Google Surveys to run consumer research and brand awareness studies in markets that have historically been difficult for researchers to access.” 

Frank Kelly, SVP B2B, Lightspeed Research

These new targeting choices are available starting today. Hear more from our product team in this video:

To learn more about the new country options, see our help center article.

Happy surveying!

March 28th 2018 Uncategorized

Peace Out to My Digital Marketing Career – It’s Been One Hell of a Ride

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I tried several different ways to begin this post, but it seemed only fitting to be as blunt in this announcement as I have been during my entire SEO and affiliate career, thus the title of this entry. It’s been an amazing ride In 1998, after my oldest son suffered a massive bilateral stroke, I […]

The post Peace Out to My Digital Marketing Career – It’s Been One Hell of a Ride appeared first on Sugarrae.

March 22nd 2018 Uncategorized

Closing Data’s Last-Mile Gap: Visualizing For Impact!

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I worry about data’s last-mile gap a lot. As a lover of data-influenced decision making, perhaps you worry as well.

A lot of hard work has gone into collecting the requirements and implementation. An additional massive investment was made in the effort to perform ninja like analysis. The end result was a collection trends and insights.

The last-mile gap is the distance between your trends and getting an influential company leader to take action.

Your biggest asset in closing that last-mile gap is the way you present the data.

On a slide. On a dashboard in Google Data Studio. Or simply something you plan to sketch on a whiteboard. This presentation of the data will decide if your trends and insights are understood, accepted and inferences drawn as to what action should be taken.

If your data presentation is good, you reduce the last-mile gap. If your data presentation is confusing/complex/wild, all the hard work that went into collecting the data, analyzing it, digging for context will all be for naught.

With the benefits so obvious, you might imagine that the last-mile gap is not a widely prevalent issue. I’m afraid that is not true. I see reports, dashboards, presentations with wide gaps. It breaks my heart, because I can truly appreciate all that hard work that went into creating work that resulted in no data-influence.

Hence today, one more look at this pernicious problem and a collection of principles you can apply to close the last-mile gap that exists at your work.

For our lessons today, I’m using an example that comes from analysis delivered by the collective efforts of a top American university, a top 5 global consulting company, and a major industry association. The analysis is publicly available.

I’ve chosen to block out the name of entities involved. Last-mile gaps exist at all our companies. It is not important where this 2018 analysis came from. In the tiny chance that you recognize the source, I request you to keep it out of your comments as well.

For each of the 17 examples we review, I’ll share an alternative version I created. I invite you to play along and share your version of any of the examples. I’ll add them to the post, and credit you.

Ready?

Let’s go!

I persistently advocate for simplicity in slides. Don’t create handouts!

In this case the goal was to create handouts, perhaps to make it easier for audiences to consume the data by themselves. I would humbly still advocate for simplicity when it comes to data presentation.

optimistic_economy_sm

Some of the fixes to solve for simplicity could be to use fewer sprinkles, a simpler header – graphics and text –, and we can be very selective about what’s on he slide. As you look at the slide, I’m sure you’ll come up with other ways in which we can liberate the white space for the tyranny of text/colors.

Solving for simplicity contributes to communication effectiveness. It of course reflects on your brand, and, most of all, helps you have better control over the story you are trying to tell.

For the rest of this post I’ll ignore the simplicity and storytelling elements and focus exclusively on the data itself. How, what, why and instead of.

Look at the graph above, and the little table… Ponder for a moment what you would do to close the last-mile gap and help the essential message shine through.

Here are some things that stood out for me:

1. Graphing choices can exaggerate or undersell reality.

One way to exaggerate is to start your y-axis at 40, as it the case above. The resulting line exaggerates the trend and ends up implying something that might not quite be there.

Start at zero. Please.

2. False precision can cause clutter, and undercut the Analyst’s brilliance.

This is very subtle.

You’ll notice that the numbers on the graph are expressed with one decimal point. As in 47.7, 56.5, etc. If you pause and consider how this data is collected, via a small triple digit sample self-reported survey results, you’ll quickly realize that the error range in this data is likely a few points. If that’s true, showing the .6, .5 is implying a precision that simply does not exist.

Besides, this false precision also clutters the graph.

3. Remove the distractions, ruthlessly.

In an 11-year span, each data point is a lot less important than the trend. Do you need the dots on the graph? Do you even need the numbers for the individual months?

When it comes to closing the last-mile gap it is helpful to have a ruthless streak. It is helpful because in service of our ultimate objective, you’ll have to kill some of your favorite things, you’ll have push back against your boss/peers who might love clutter, and you might have to help change an entire culture. Hard, painful, work. But, immensely worth it.

Here’s an alternative way to present the data, using nothing more than the standard settings in good old Excel:

optimistic_economy_v2_sm

It shows the trend, simply. You can see it is up broadly over eleven years. That it was under 50 and is now close to 70.

Did you notice the trend is not as exaggerated as the original? And, still effective!

You might use a different font, perhaps have the graph be smaller, or maybe twist the month-year in the other direction. No problem. I’m confident if you apply the first three filters, whatever you create will close the last-mile gap better.

Here’s an example of doing exactly the opposite of principle #1. The y-axis is artificially set at 100%, as a result the trend is understated.

customer_sales_sm

You don’t need to go this far.

Just let your favorite graphing tool auto-set the major and minor-axis, which will result in the graph looking like this…

customer_sales_v2_sm

Simple. No funny business. 

The trend stands by itself waiting your words as to why it is meaningful.


This next one is pretty interesting. My request to you is to not scroll beyond the slide. Pause. Absorb the graph. Try to understand what the author is really trying to say.

For bonus points, consider the perspective of the person reading this graph rather than the person who created it.

Read. Don’t scroll. Absorb.

customer_priorities_sm

How well did you understand the trend and the insight being communicated? What would you have done differently if you’d created the graph?

Here are some things that stood out for me:

4. Show as much data as is required, and no more.

The goal in the original seems to be to show top priorities for 12 months. If so, is the data for August 2017 really adding value?

Often we want to show all the data we have (after all we spent time collecting it!). In this case, it get’s in the way of understanding the 12 month shift.

5. Experiment with visualization options, even in Excel!

We have five dimensions of data, and two data points each (if you apply principle #4). We want the audience to be able to compare two data points for each dimension, and look across all five dimensions.

The bar chart is a sub-optimal way to let the audience see this. Consider experimenting with different visuals in Excel (or D3js).

I applied the radar chart to this data, and got this lovely end result…

customer_priorities_v2_sm

It is ten million times easier to see the two data points for five dimensions, and realize that only two have changed.

Likewise, the overall trend also pops out at you so much easier in this case.

It would have taken ten minutes for us to explain the data and trend in the original. We can do that in five seconds now. You can use the time remaining discussing why this trend is material and what to do about it (if anything). Actually allowing data to play its natural role: Influence decisions.


This is a really nice example of a lesson that we tend to forget all the time (myself included).

You know the exercise by now. Pause, reflect on this slide, then scroll.

marketing_budgets_growth_sm

Here’s what stood out for me:

6. Don’t send a graphic to do a table’s job.

In this case, we are comparing two simple data points, on two dimensions (past, present). Why do we need a graph taking up all the space?

Why not just have a table that shows previous 12 months as 7.1% and a row under it with next 12 months as 8.9%?

Even better, why not just have one line of text:

Percent change in marketing budgets = +1.8 PP

Why have two fat bars?

Once you arrive at that conclusion, you’ll apply principle #4 and realize that the most interesting data on this slide is not the visual… Rather, it is the table on the top right corner of the slide.

Bada, bing, bada, boom, ten seconds later here’s your slide:

marketing_budgets_growth_v2_sm

A simple table with a touch of colors that draws out the core message simply, directly and quickly.

The lighter shade for the core numbers will result in them being pushed a bit into the background. This simple choice guides the reader’s eyes gently to the delta (the most important bit).

I like playing with the borders a bit, as you see above. You might have other things you are picky about. And, that is ok. 🙂

To illustrate principle #6, here’s another slide where the graphic is completely unnecessary:

spending_on_marketing_sm

A tiny table with two data points will do just fine.

Here’s a bonus lesson for the analysis ninjas out there. Please don’t imply a linear trend between “current levels” and “next three years.” There is no indication that data from 2017 to 2020 is available, and it is highly unlikely that it will follow a linear trend. This is another example of breaking principle #1.

(Let’s not lose sight of the big picture: I am delighted that spending on analytics is going to increase that much! As our leaders spend this largesse, I hope that they’ll remember the 10/90 rule to ensure optimal returns. The money needs to go to you!)


This one flummoxed me.

Let’s see if you can internalize what is going on. Stare at the graph intently, seriously, and see if you get the points…

consumer_services_sm

Bold items naturally catch the eye, in this case the blue bars. Most people in the western world look from left to right, that is how you’ll likely try and understand what’s going on.

Your first impression will likely be that the blue bars are showing a random trend in marketing spending.

If you are the curious type you’ll realize that is the wrong conclusion, and you’ll want to understand what’s really going on. Soon enough you’ll get to the x-axis and a carefully review will illuminate that the reason for the weirdness is the choice to show the industry names alphabetically!

7. Please, please, please keep the end-user in mind.

In this case the end-users (our senior leaders) would be primarily be interested in understanding where marketing spending is highest and lowest. This is very difficult to accomplish above.

Secondarily, they’ll want to know where they fall in context of all other industries, this is almost impossible to accomplish above.

The reason the x-axis is organized alphabetically is to allow you to look up your specific industry easily. This thought is good. My hypothesis is that it likely forms a small percent of the use cases, primarily because just knowing your spend is not that valuable. What’s valuable are the above two use cases.

Here’s what I recommend keeping front of mind: If a non-analyst is looking at the data, what uses cases form the basis of the value they’ll extract. Then, ensure the info viz is solving for that.

In this case the bars with the data seem to be randomly sorted. The visualization is getting in the way, creating a wider last-mile gap.

Luckily this is a quick fix in good old Excel. Two minutes later, you’ll have a little waterfall…

consumer_services_v2_sm

It is easy to see the outliers and the pack of eight that are close to each other (something you can’t even see in the original).

It will certainly take an extra couple of seconds to find your industry, but in service of the two bigger use cases,  it is a small price to pay.

You can play with the layout to your heart’s content. If you dislike waterfalls for some reason and prefer towers…

consumer_services_v3_sm

I like the waterfall, but this is not bad. 🙂

Play with the colors, drop shadows, fonts, and more. Make the graph your own. Just don’t forget to look at it through the eyes of the end user and solve for their use cases.

(Speaking of colors… I’m partial to chart styles 17 through 24 in Excel. In my work you’ll see a particular affection for style 18.)


I hate pie charts. I really do.

You can read a 506 word love-letter to my profound dislike (including a lovely exercise you can do).

Here’s the scientific reason:

Comparison by angle is significantly more difficult than by length.

That is well on display below…

marketers_talent_sm

The colors in the pie will catch your eye. Yet, from the sizes of the slices it is difficult to internalizes the differences between each dimension.

8. Eat Pies, Don't Share Them!

Since humans find comparing lengths much easier, it should only take a few minutes to take the data and convert the slide above into something that closes the last-mile gap efficiently.

marketers_talent_v2_sm

The above slide is a good example how to apply all the principles you’ve learned thus far. The question and the data are the hero, almost all by themselves. Allowing you to focus sharply on your story.

Scroll up and down and compare the two slides. You’ll see many more differences.


I’ve extoled the virtue of using a table, instead of trying to be extra sexy and throwing in a graphic.

The challenge with tables is that they can become overwhelming very quickly.

Here’s an example that illuminates that clearly.

sector_differences_sm

It feels like there is a lot. It also breaks principle #2, false precision,  which makes things worse.

Considering the core message the analysis is trying to send, I believe that it is also breaking rule #4, extra perhaps unnecessary data.

9. Make your tables pop, guide the reader’s eye.

There are numerous tools available to you inside Excel to make your tables pop. I usually start by playing with the options at my disposal under Conditional Formatting.

One straight-forward option is to use Color Scales, green to yellow, to produce a simpler table that pops…

sector_differences_v2_sm

The elimination of the overall average makes the table tighter.

It is easier to look at the trend in each column. What’s even more delightful is the second use case of comparing the highs and lows across the four dimensions. So much easier.

While all the data is still there, most senior leaders want to understand trends and the contrasts. They want relative positioning, the above table does not require expending too many brain cells to get that. And, if your boss does not trust you… She still has the numbers there.

Notice the combination of fonts, colors, style treatments, in the table above. Bunch of subtle points there.

If your personal tastes are different, no problem. There are other styles you can use.

Here’s the data rendered using solid fill Data Bars…

sector_differences_v3_sm

In this case I feel data bars add clutter, but they make internalizing the trend across individual dimensions easier.

If, like me, you are biased towards radical simplicity via white space, you can keep the table. Consider applying some subtle font color treatment to create something that’s still a step change over the original…

sector_differences_v4_sm

I’ve shown the highs and lows in a way that you’ll see them quickly.

Red was chosen on purpose to emphasize that it was the most important thing from the customer’s perspective. Blue fades into the background a bit because it is the least important.

One final touch.

I felt it might be of value to see the product and services dimensions together, comparing them across B2B and B2C.

Here’s that version…

sector_differences_v5_sm

There’s a little air gap in the table to emphasize the two comparisons are different. You can usually use visual cues like these to help the consumers of your analysis.


We disagree on a whole lot of subjects in our country these days, but the one thing we can all agree on is that the human attention span is probably ten micro-seconds.

Add to that short attention span the fact that each executive has 18 other urgent things taking up their brain cells. As if all that was not hard enough, while you are presenting they are also likely on their phone or laptop.

Persuading anyone in these circumstances is a herculean task.

With that context in mind, how many leaders do you think will understand what’s going on here…

marketing_knowledge_sm

4 dimensions x 5 time periods x crazy swings = Ouch!

For bonus points, notice the randomness in the x-axis. It jumps from 2014 to 2017 without any visible explanation. To make things worse, look at the trend lines – they connect the two data points to imply a trend between 2015, 2016 that may or may not exist.

For even more bonus points, notice that there are four Februaries and as if it is no big deal an August is thrown in randomly.

Ouch. Ouch.

These might seem like small issues, but I assure you that you’ll instantly lose credibility with any intelligent leader in the room. They won’t raise their hand and start to berate you. They’ll quietly make a mental note about you, and then not pay any attention to anything you are saying.

There’s an even more important principle to learn from this visual…

10. Let the higher order bit be your north star.

It can be difficult to figure out how to go from the complex to the simple.  My recommendation is to start with the most important thing you are trying to say.

In this instance the goal is to illuminate the percent change in marketing knowledge in the next 12 months. So, are the rest of the data points necessary and of value?

In service of the higher order bit, I would argue that we can also get rid of the two Februaries and the lonely August. (Though I sincerely respect the effort it took to get those data points.)

With those decisions we are left with just two data points. We can move to a simple table and close the last-mile gap by creating this slide…

Simpler, right?

We can do one better.

If the objective is to just show the change, we can just show the percentage change.

marketing_knowledge_v3_sm

The colors help focus the attention even more.

To see the dramatic change, scroll back up and look at the original and then come back here. Incredible, right?

It might seem that this is hard work that takes time. It does take more time. But, it is not in the ink rather it is in the think. Discussing, debating, really thinking through what we are trying to communicate. The visualizing part takes a lot less time.


The biggest problem with this type of analysis, compiled into 95 slides, is that it never answers the question why?

Take this slide as an example. It shares a very positive view of analytics…

analytics_spend_sm

The slide breaks all ten principles we’ve discussed in this post, but beyond that there is a bigger problem here.

11. Why. Your job is to answer why!

Your first instinct is the marvel at the shift (all blues are up!), and reflect on how this graph is long-term job security for everyone who reads this blog.  But, you’re an Analyst and that good feeling won’t last.

Your mind quickly goes to… Why? What is causing this shift?

Look at Mining/Construction, 60 percent points of change. OMG! Why?

The entity creating this report sadly never answers any why question anywhere. Perhaps by design.

But, consider this: Data creates curiosity. If the Analyst does not satiate that curiosity via deeper analysis that explains why, the same data turns into a disappointment. It certainly drives no change.

I’ve written about this topic before, using an example from Econsultancy and Lynchpin: Smarter Survey Results and Impact: Abandon the Asker-Puker Model!

Without the why your last-mile gap is a million miles wide. If you are going to be in the data regurgitation business, please consider it your job to answer the why question. Without it all this is… fake news.

A challenge for you to tackle.

Now that you are aware of the 11 principles that aid in closing the last-mile gap, I want you to tackle something on my behalf.

I had not idea what to do with this slide… Can you create an after version?

product_service_org_sm

Partly the issue is that I could not truly internalize what was being said. Partly it is that the numbers don’t really seem to change much. Partly it is because I was torn between the graphic and the table on the top right.

Regardless, I gave up. Perhaps you can teach me, and our readers, what a version with a reduced last-mile gap will look like.

Just email me your version (blog at kaushik dot net) or comment below.

Here’s a summary of the 11 principles you can use to close the last-mile gap:

01. Graphing choices can exaggerate or undersell reality.
02. False precision can cause clutter, and undercut the Analyst’s brilliance.
03. Remove the distractions, ruthlessly.
04. Show as much data as is required, and no more.
05. Experiment with visualization options, even in Excel!
06. Don’t send a graphic to do a table’s job.
07. Please, please, please keep the end-user in mind.
08. Eat Pies, Don't Share Them!
09. Make your tables pop, guide the reader’s eye.
10. Let the higher order bit be your north star.
11. Why. Your job is to answer why!

I wish you smaller gaps and more decisions that are data-influenced.

As always, it is your turn now.

In your practice, how wide is the last-mile gap? What do you think contributes to the gap the most? Which of the above principles have you used, to good effect? Do you have a favorite principle, or five, to close the gap? If you had to kill one practice when it comes to data presentation, who would be the chosen candidate?

Please share versions of the above examples that you’ve taken a crack at fixing. And, your lessons, best practices, and as always your critique via comments below.

Thank you.

The post Closing Data's Last-Mile Gap: Visualizing For Impact! appeared first on Occam's Razor by Avinash Kaushik.

March 19th 2018 Uncategorized

Tag Manager 360: From Approvals to Zones

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Whether you’re a small business with a single site or a large enterprise with many complex sites and apps, Google Tag Manager makes it easier to implement and maintain the tags for all your marketing and measurement tools.

Over the past few years, we’ve continued to improve the core functionality of Tag Manager for all users while also introducing enterprise features for customers with more advanced needs.

For Tag Manager 360 customers, we recently added Approvals functionality, enabling enterprise users to involve more stakeholders in the tagging process without needing to give them full Publish access.

Submit Changes screen in Tag Manager 360 showing how users with Edit access can request approval.

Using Approvals, you can limit select users to requesting approval for tagging changes. Then you can use the built-in commenting capability to work back and forth with them to get things just right.

Today we’re excited to announce another new feature that gives you even more control over your tagging: Zones for Tag Manager 360!

With Zones, you can give users access to publish certain types of tags on certain parts of your site. Zones work by letting you link additional containers within specified page boundaries.

Zone configuration screen in Tag Manager 360 highlighting the steps of linking containers, defining zone boundaries, and optionally turning on type restrictions.
When a page loads within the zone boundaries, any containers linked within the zone will load alongside your main container. For example, you could give your marketing team and agencies Publish access to their own containers, but limit them to only your marketing pages. This gives them the flexibility to manage their tagging independently and reduces work for admins and developers.

For even more control, you can turn on Type Restrictions to choose what types of tags, triggers, and variables will work from containers within a zone.

Type Restrictions for a Zone in Tag Manager 360 highlighting how individual tag types can be allowed or restricted.
So, whether you’re making a few quick updates to who can publish which tags or building a comprehensive tagging plan for a network of global, regional, and local websites, Zones gives you more power and flexibility to set up the right tagging workflows for your organization.

If you’re already a Tag Manager 360 customer, you’ll see a new Zones section in the left sidebar of your containers starting today. Visit our help center to learn more about Zones.

Want to become a Tag Manager 360 customer? If you’re already a customer of another Google Analytics 360 Suite product, you can reach out to your Account Manager. If you’re brand new to the Analytics 360 Suite, visit our website to learn more.

Posted by Scott Herman, Product Manager, Google Tag Manager

March 15th 2018 Uncategorized

New Analytics Academy course: Getting Started With Google Analytics 360

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Today, we are introducing a new course in Analytics Academy: Getting Started With Google Analytics 360.


Krista Seiden and Ashish Vij Introduce Getting Started With Google Analytics 360 (Video)

In this course, you will join instructors Ashish Vij and Krista Seiden as you learn key Google Analytics 360 features such as Roll-Up Reporting, Custom Funnels, Unsampled Reports, and Custom Tables. You’ll gain insight into how you can benefit from reporting with BigQuery and native integrations with DoubleClick products, and we will provide you with real-world examples to illustrate how you can leverage Analytics 360’s features and integrations to drive performance and achieve your business goals.

By participating in the course, you’ll learn how to:

  • Set up Roll-Up Reporting
  • Analyze customer journeys with Custom Funnels
  • Leverage Unsampled Reports and Custom Tables
  • Analyze big data with BigQuery Export
  • Evaluate marketing performance with DoubleClick reporting integrations
Sign up for Getting Started With Google Analytics 360 now and start learning today.

Happy analyzing!

Helen Huang & The Google Analytics Education Team

March 15th 2018 Analytics

Some terrible personal news

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Cindy Cutts, my wife and best friend, passed away earlier this week. While I was traveling for work recently, Cindy went to visit her family in Omaha, Nebraska. On Sunday, while enjoying time with family, Cindy started having trouble breathing. Her family quickly called 911 and paramedics took Cindy to the hospital, but Cindy lost and never regained consciousness. She passed away on Monday.

Cindy didn’t want any callouts on my blog, so I always just referred to her as my wife. But I’d like to tell you something about her. She loved her family and her cats, Emmy and Ozzie. She danced in the Bay Area with a fantastic troupe of kick-ass women for years. She ran a half-marathon once and then decided that she never needed to do that again. She sang in show choir in high school and could still rock a karaoke room with an Adele song. She wrestled with anxiety and depression at times, as so many people do. We should all talk about mental health more to lessen the stigma for other people who think they’re alone when they’re not.

Cindy enjoyed falling asleep to Parks and Rec. She liked re-reading William Gibson’s novel Pattern Recognition. Cindy made quilts for her family and crocheted scarves. She kept me healthy and on track and moving in the right direction, and I paid her back with occasional head rubs. Cindy was whip smart, with a particular gift with languages, from French to Chinese. I tackled small details like paperwork and license plates and paying bills, but she was the one who looked at the big picture. Cindy was the person who said “Let’s go try this Google thing for a while.”

Cindy and I knew each other for 23 years and we were married for 18 years, which is no small thing. I’m unmanned and unmoored without her. I’m just going to tackle the details in front of me and count on time and family and friends to pull me back on course at some point.

If anyone wants to send flowers, the service is at Heafey-Hoffmann-Dworak-Cutler at 7805 W Center Rd in Omaha on Saturday, March 10th, starting at 3pm.

For the people who didn’t get to meet her, Cindy looked like a movie star:

Cindy movie star

She loved hanging with her family, like her sister and niece and nephew:

Cindy with her family

She had the best smile and amazing green eyes:

Cindy smiling

And her cat Ozzie adored her as much as I did:

Cindy and Ozzie

Please give your friends or family a hug for me. We never know how much time we have with someone, and sometimes it’s all too short.

March 8th 2018 personal

More reasons to get started with Google Optimize

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With Google Optimize, we want to empower any sized business – big or small – to take steps to make their sites better. Since releasing Optimize last year, we’ve been able to help many businesses identify and provide better site experiences experiences to their users – for free. Today, we’re pleased to announce two new initiatives that will help businesses take even bigger steps when improving their sites.

Higher experiment limit 

Many of our users have given us the feedback that the current limit of 3 simultaneous running experiments is too low. This limit forces them to make difficult tradeoffs about which tests and customer segments should be prioritized. To help address this, we will soon be increasing this limit to 5 experiments. We believe this will give you more opportunity to use Optimize across your entire site.

New “Getting started with Google Optimize” video series 

For many, running website tests may appear to be a daunting task. To make things easier, for anyone completely new to testing or recently started using Optimize, we’ve created a “Getting started with Google Optimize” video series. This will help you start testing in no time. Plus, you can watch the entire series in less than 15 minutes!

Optimize Overview: Quick primer on what Optimize is and how it can help you

Set up your account: Shows you how to link to Google Analytics and get your site ready to run tests 

Create your first experiment: Use the Optimize editor to change your site without writing any code 

Understanding your results: See how Optimize clearly tells you which changes worked best

Once you’re done watching the video series, be sure to create an Optimize account, if you don’t already have one.

We hope you like these changes. Stay tuned, because there are more improvements coming!

March 2nd 2018 Uncategorized

Richer Google Analytics User Management

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Today we are introducing more powerful ways to manage access to your Analytics accounts: user groups inside Google Analytics, and enforceable user policies. These new features increase your ability to tightly manage who has access to your data, and amplify the impact of the user management features we launched last year.

User Groups

User groups can now be created from and used within Google Analytics, simplifying user management across teams of people. This is a big time saver if you find yourself repeatedly giving out similar permissions to many people, and simplifies granting permissions as individuals rotate into or out of a team.

To start with user groups, visit either Suite Home or Google Analytics, navigate to the user management section, and click the “+” button. You will then see an option to add new groups, which will walk you through creating a user group, adding people to it, and assigning permissions to the group. Here is a full list of steps to make a user group.

Google Analytics User Management page highlighting the new option to create a user group


Enforced User Policies

Google Analytics 360 Suite user policies let you define which users will have access to your Analytics accounts, and which do not. When a user violates a policy, you will be warned of this through the user management section in Google Analytics or Suite Home and have the option to remove that user from your organization.

We have enhanced these policies so you can choose to block policy-violating users from being added to your Analytics accounts. While policies aren’t enforced by default, you have the option to block violator additions.  When you create or edit your organization’s user policy, you will see a toggle switch like the one below:

User policy setup showcasing the new enforced policy option

User groups and enforced user policies are supported in Google Analytics today, and support for more products is coming, as we continue to plan features that help customers better manage access to their critical business data.

Posted by Matt Matyas, Product Manager Google Analytics 360 Suite

March 1st 2018 Analytics