Ultimate Guide to Regular Expressions in Google Analytics

Regular expressions, at first they seem daunting. You realize how powerful they are once you get used to them. It might take a while, but it is definitely worth it!

They are useful for both marketers as well as more technically oriented people.

This guide clearly explains how to use regular expressions in Google Analytics. It includes everything you need to know te become a Regular Expression (RegEx) Master!

13 regular expressionsTable of Contents

Overview of Regular Expressions

There are 13 regular expressions in Google Analytics. This includes combinations of the most common regular expressions.

Some of them you will rarely use, others maybe on a daily basis!

The regular expressions that I use most often are on top of the list. Have fun exploring them!

Pipe (|)

The pipe symbol is the simplest one and means or.

An example:

Regular Expressions - Pipe

In this case I tried to match two pages: /ebooks/ and /tools/.

Please note that pages that contain these subdirectories match as well. Later you will learn how to be more precise when using regular expressions.

Dot (.)

dot matches any character. It’s like a wildcard.

So you could use it in the expression .ook. In this case it would match book, took, look, cook etc., but not ook.

The dot equals one character.

Note: the power of this RegEx lies in using it together with other RegEx characters.

Asterisk (*)

The asterisk means match zero or more of the previous item.

An example:

– boo*ks -> it matches boks, books, boooks, booooks etc.

Note: the power of this RegEx lies in using it together with the dot RegEx.

Dot-Asterisk (.*)

The dot-asterisk is definitely a powerful combination!

It matches zero or more random characters. In other words it matches everything.

There are many instances in which you would like to use this combination.

Check out the following filter:

GA - attach hostname to request URI

I have put parentheses around the .*, like this: (.*) This means get all characters and put them in a variable. So we get the entire hostname and the entire request URI in a variable, and then in the bottom field I combine both variables.

By doing this your full URL will show up in Google Analytics.

Tip: read this article about filters in Google Analytics.

Another example to make things clear to you. Let’s assume you are running a website and sell bicycles to men, women and kids. These are the three categories on the website:

  • /products/men/cycles/
  • /products/women/cycles/
  • /products/kids/cycles/

You could use /products/.*/cycles/ to match all three categories.

Note: keep in mind that the processing time of this RegEx is quite long. So don’t misabuse it!

Backslash (\)

The backslash RegEx is very useful and one of the regular expressions you should definitely use.

In my experience you will use this one a lot.

They turn special (RegEx) characters into normal characters.

Two examples:

  • Request URI = /gp/product/B009TGWVRG/ref=s9_nwrsa_gw_g318_i3\?pf_rd_m=ATVP
  • IP address = 67\.172\.171\.105

The first example is based on an Amazon url. You can see that I used a backslash to “escape” the question mark. By doing this I turn it into a normal character. There are a lot of urls that contain query parameters so this one might come in handy!

The second example is based on an IP address that contains three dots. We learned that a dot means a random character (RegEx). It’s better to escape it here since it should be read as a plain, normal character.

Caret (^)

The caret has a lot of value as well. It means that something begins with

An example:

^shoe -> It matches shoe, shoes, shoes for winter, but it doesn’t match winter shoe or winter shoes.

Dollar sign ($)

The dollar sign is easy to understand now you know how the caret works.

It means that something ends with

An example:

shoe$ -> It matches shoe, winter shoe, but it doesn’t match winter shoes or winter shoe guide.

Question mark (?)

question mark means the last character is optional.

In general this one it useful for targeting misspellings.

Let’s assume that Stefan is the CEO of a company called Reggex. This company is running a pay per click campaign and likes to filter out all brand searches on Stefan and the company name.

Here is a smart way to do that:

GA - question mark RegExThis way all pay per click keywords that contain stefan, steffan, reggex and regex are included. You will be astonished how often these type of names are misspelled.

Parentheses ()

I love using parentheses. Actually, they work in the same way as in mathematics.

Let me show this by two examples:

  • 2 x 7 +13 = 27
  • 2 x (7 + 13) = 40

By using the parenteses you group two numbers together before you do the calculation.

I have already showed these directories:

  • /products/men/cycles/
  • /products/women/cycles/
  • /products/kids/cycles/

You have learned you can use .* to match anything.

If you want to make a 100% match, you could use the following regular expression:

^/products/(men|women|kids)/cycles/$

Now we are getting somewhere!

  • The request URI starts with /products and ends with cycles/.
  • The middle directory contains either men, women or kids

The more you know about RegEx, the faster and more accurate you can work.

We are almost there, three more to go.

Square brackets ([])

The square brackets help you to make a simple list.

For example [aeo]. Combined with other characters t[aeo]p. It matches tap, tep and top.

Tip: use them together with dashes to create a powerful list.

Dashes (-)

The dashes are a great help to create a (more advanced) list of items.

It is a best practice to use them together with square brackets.

  • [a-z] matches all lower-case letters
  • [A-Z] matches all upper-case letters
  • [0-9] matches all numbers
  • [a-zA-Z0-9] matches all lower-case and upper-case letters and numbers

An example:

Jake is product manager of Nike Air Max Shoes and he is eager to sell more!

Nike Air Max

You want to monitor this year’s shoes but also a few legacy editions:

  • Nike Air Max 2012
  • Nike Air Max 2013
  • Nike Air Max 2014
  • Nike Air Max 2015

Google Analytics can filters these products in an easy way:

Filter Nike Air Max editionsAnother RegEx that would work in this situation:

Nike Air Max 201(2|3|4|5)

They both match the four editions and Jake is happy to monitor the product line perfomance in an easy way! :-)

Plus sign (+)

The plus sign matches one or more of the precious characters.

I use it on a rarely basis, but it is good to know this one exists!

An example:

hello+ matches hello, helloo, hellooo, helloooo (you got the point now :-)).

Curly brackets ({ })

We made it to the final one!

It’s probably not the most easy one to explain, so I will talk about this with the help of two examples:

  • {1,2} – it means, repeat the last “item” at least 1 times and no more than 2 times.
  • {2} – it means, repeat the last “item” 2 times

I have used the first one in RegEx IP ranges.

An example:

77.120.120.0 to 77.120.120.99 -> RegEx would look like ^77\.120\.120\.[0-9]{1,2}$

The second one I have rarely used, but an example with ZIP codes:

12[0-9]{3would match 12xxx. First two numbers of the ZIP code are 1 and 2 followed by three random numbers in the range of 0 to 9.

Five Effective Ways to Use RegEx

By now I hope you agree with me that regular expressions are very effective in Google Analytics.

To convince you even more, I will explain five situations where you really want to use RegEx.

1. Applying Report Filters

It has not always been that way, but happily it is now allowed to use RegEx in report filters.

This is very effective when you need to work with specific data in a standard or custom report.

An example:

I like to filter on the pages that begin with /google-analytics. It’s easy to set this up:

pages google analyticsI don’t have to go to the advanced filter section anymore.

If you know how to work with RegEx, you can literally set this up in seconds!

2. Setting Up Filters

In this article I have already shown a couple of filters that include regular expressions.

Make sure to use the RegEx in his module as it is the only way to build and apply all the filters that you need.

3. Setting Up Goals

Google Analytics currently has four different goal types:

  • Destination
  • Duration
  • Pages/Screen per session
  • Event

In the category destination goals regular expressions really come in handy.

Google Analytics goals set upVery often the thank you page of a goal includes query parameters or an orderID and looks quite similar to other pages.

Setting up your Google Analytics goals with regular expressions is easy and effective!

Helpful articles:

4. Defining Funnel steps

In the screenshot above you can see that defining a funnel is optional. You can turn it on and set up a goal including 20 funnel steps.

I hope you don’t have to set up that many steps. Since your conversion rate will be pretty close to 0 then. ;-)

Anyway, the same as with your thank you page, regular expressions are really handy when setting up funnel steps in Google Analytics.

5. Setting Up Segments

On default, Google Analytics reports on All Sessions.

There are dozens of reasons why you would like to dig deeper.

For ad-hoc segmentation I recommend to use Segments.

It is much easier to set up your own segments if you master regular expressions.

Segments and RegEx

Regular Expression Tester

Whether you are new to RegEx in Google Analytics or an advanced user, I always recommend to test your RegEx first.

There are two smart ways to test your regular expressions:

Example RegEx testerIt works like a charm!

Another great tip from Tobias Kraeft:

  • Regex101.com (fantastic online regex tester and debugger)

Regular Expression Cheat Sheet

A quick overview of what we have learned so far:
regular expression cheat sheet

Regular Expressions and Google Analytics API

It is not the right time to explain all details about the Google Analytics API.

There is one thing worth mentioning here.

It works differently, but you can use regular expressions in Google Analytics API queries:

Google Analytics API - dimension filtersImage courtesy

 You can also use regular expressions in filter expressions using the =~ and !~ operators. Their syntax is similar to Perl regular expressions.

Keep these two rules in mind:

  • Maximum length of 128 characters
  • Regular expression matching is case-insensitive

Well, this is everything I wanted to share.

I guess there is a lot to think about after reading this article.

Do you already use regular expressions in Google Analytics? What do you like or dislike about them? Happy to hear your opinion!

I am sure you will love my new eBook if you enjoyed reading this article!

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8 Tips to Evolve From Reporting Squirrel to Analytics Ninja

Minimize your reporting time and maximize your optimization efforts. And you are on the right path to become an analytics ninja!

For a lot of people this is easier said than done.

You might be stuck in corporate politics. Or you might have a lot of clients with these awful reporting needs.

You send a lot of reports and they are saved in the Inbox. And never looked at. Unfortunately this is true in a lot of cases.

I don’t say reporting is bad; it’s often overused and misabused.

Reporting - OptimizationChange is a must if you are stuck at 80% reporting and 20% optimization time!

It’s the same as investing 99% of your budget in traffic and 1% in CRO. It simply doesn’t work that way.

You want highly qualified visitors that are ready to convert on an astonishing platform!

Make a change before it is too late. The possible impact of automation is huge…

Believe me, it can be different! There is enough time left to save your career.

In this article I describe eight tips to change from a reporting squirrel to an analytics ninja.

Do you join me for an exciting ride? Good!

Reporting ≠ Optimization

Realize that reporting and optimization are not the same. It might sound like a no-brainer, but why are so many of us still devoting too much time to reporting?

Im my opinion:

“Reporting can aid your optimization efforts but doesn’t bring a direct ROI for you or the company you are employed.”

“Optimization efforts are directly related to improving the experience and ROI of your website visitors; this in contrast to reporting.”

External resources:

1. Become an Expert in Your Field

There are literally hundreds of ways to expand your skillset further. When doing so you will find your influence to grow.

If you say a lot of smart things, people are more eager to listen to you.

So you could more easily persuade your colleagues and clients to take the road that leads to the best outcome for all.

Five things to embrace:

Improve 10 Vital Web Analytics Skills

One of my most popular posts is about the top 10 web analytics skills. Read it and I am sure your knowledge rollercoaster is going faster than ever before.

Read, Watch, Learn and Adapt

There are a lot of interesting blogs, books, videos, podcasts etc. out there. Make a selection and derive great ideas you could apply in your unique situation.

Last year I published a post about the top 20 Analytics blogs (recently updated).

A few other interesting reads:

Learn Statistics

A/B testing might sound easy. You have your control page and put your ideas in an ideal version – all based on a simple hypothesis – and the $$$ come in.

Sorry to disappoint you, but A/B testing and Conversion Optimization isn’t that easy.

Ideally you should know about:

  • The company that is involved and the competitive field
  • Important characteristics of “the customer”
  • Quantitative data (website behaviour)
  • Qualitative data
  • Persuasion elements that drive conversions

And most importantly, if you interpret the data in the wrong way, you are wasting time and money.

So make sure to learn a bit about statistics as well.

Suggested resources and tools:

Start an Online Business

The best way to learn more about reporting and optimization is by putting things in practice.

Is there a better opportunity than starting an online business (or simple website) by yourself? I don’t think so.

I recently wrote an article about why every web analyst should start an online business.

Just read it and let me know what you think!

Keep on Learning

Everything changes.

Especially in the Analytics field – a rapidly changing environment – you can’t lose your attention for a long time without being “punished”.

Keep on the edge of things and you will do great!

2. Learn How to Deal With HiPPOs

You boss, well he or she might behave as a HiPPO blocking your road to Analytics nirvana!

Read these six tips to deal with your HiPPO to avoid another obstacle.

They actually made the fun out of it a few years ago :-)

Ask A HiPPO

3. Automate Your Reporting Efforts

Do you like to become an analytics ninja like Yehoshua Coren?

Simply stop wasting your time with reporting every week.

It will save you hours of your valuable time, each and every week.

Do you use Google Analytics? You will enjoy reading this post about reporting automation.

4. Report on Results

Forget pageviews, forget visits. These metrics are not the drivers of commercial success.

Develop your talent to explain and persuade your boss or client to take another direction.

back to school

Of course, no traffic means no sales. We all know that.

Put those numbers in perspective. I have seen websites with ten thousands visitors each month and hardly any sales.

What is driving online success? What keeps your business healthy in the long run? Focus on online metrics that matter and report on them!

How to discover great metrics? Read on, you won’t reget it!

5. Formulate Great Questions

Every web analyst should learn how to ask questions.

If you ask the right questions, you can quickly solve customer problems. And find out what are the exact reporting needs that actually make sense.

Have you heard about the five whys? It’s an effective method to find the root causes behind a problem. And distill an opportunity to make things better.

Here are 11 questioning tips from a fantastic post written by Himanshu Sharma.

  1. Overcome your fear of asking questions.
  2. No question is a stupid question.
  3. Ask questions whose answers seem pretty obvious.
  4. Don’t try to figure out everything on your own.
  5. Acknowledge the expertise of your client by asking questions.
  6. Ask questions every day.
  7. Give answers that sound like questions.
  8. Your client already know the answer, he is just unaware of it.
  9. Raise objections by asking questions.
  10. Ask follow up questions.
  11. Make asking questions your daily habit.

And I do agree that asking questions will skyrocket your Analytics career!

6. Determine One Key Metric

In relation to this point, I like to refer to two books you should have on your bookshelves:

A lot of people talk about these books and principles behind in relation to startups. I feel the lean and agile approach should apply to any business in one way or the other.

As a web analyst you are equipped with a lot of tasks. During the optimization process you don’t want to focus on the wrong metrics.

Like discussed before, it is very important to ask questions.

In addition, finding One Metric That Matters (OMTM) is crucial. Both for you as well as the business your work for.

In Lean Analytics the three core principles are:

  • What business are you in?
  • What stage are you at?
  • Who is your audience?

Take the time to answer these questions for you specific situation and you will move closer to your metric that matters.

Watch this video by Alistair Croll if you have some free time (96 min to be exact) :-):

Or give yourself FREE access to this Udemy course (click on screenshot to go to website):

Udemy Course - Lean Analytics

Vanity Metrics vs. Actionable Metrics

Here is how I see the difference between both type of metrics.

Vanity metrics:

“A vanity metric equals a number that might indicate an improvement (e.g. more visitors on your website), but the improvement is disconnected from your organizational online business goal(s).”

Actionable metrics:

“An actionable metric equals a number that might indicate an improvement (e.g. average order value) and the improvement is directly connected to your organizational online business goal(s).”

In the Lean Startup good metrics are described as: actionable, accessible and auditable.

In Need of More than One Metric?

It’s better to select one great metric instead of a few useless, vanity metrics.

Keep in mind that any additional metrics (KPIs) should be in line with your business goals. If they are not, ditch them!

You are one step closer to becoming an analytics ninja.

7. Answer the What, Why and How

Great, by now you have learned about:

  • How to become an analytics expert
  • How to effectively deal with your “boss”
  • How to move from reporting to optimization
  • Why and how to ask questions
  • How to select a great set of metrics

I am sure this wealth of knowledge will greatly benefit your career.

You all know Avinash Kaushik I assume. If not, feel ashamed. ;-)

He has introduced the web analytics concepts behind WhatWhy and How.

What, Why and How

The picture above shows how I feel about this.

  • What: is the lowest level of insights (quantitative insights) // what happened?
  • Why: is the next step (qualitative insights) // what happened and why?
  • How: is analytics nirvana (data + psychology + business + consumer understanding) // how can we influence the why based on the what?

In other words:

“It is good to know what happens on our website (our conversion rate is well below 2%)”

—>

“Our customers experiment technical issues when placing an order and don’t feel secure in the process, what about our prices?… great to know this”

—>

“Let’s combine these data insights with all the information we have about our business, market and consumers. How do they react on persuasion elements? We need to formulate effective A/B tests based on a smart hypothesis. This might improve the experience of our website visitors. Now we are getting there!”

Don’t get stuck in the What!

Interesting read: Trilogy of Analytics Tools.

8. Create a Data-Driven Culture

If you really aim to become a kick-ass analytics ninja, there is one more thing. And it’s very important!

“Transform your organization into a data-driven culture”

You will move from analytics to action. It isn’t easy, but the fruits of your labor are awesome.

How to Move Into a Data-Drive Paradise

There are seven steps that I – and my good friend Avinash – recommend to follow:

seven steps to data-drive decision makingYou must be tired, but… you are on your way to becoming an analytics ninja!

Do you have any tips to move from reporting to optimization that you would like to share?

I am sure you will love my new eBook if you enjoyed reading this article!

It contains 100 actionable tips to grow your online business.

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10 Actionable Tips to Master Metrics and Dimensions in Google Analytics

Google Analytics can deliver great insights and help you optimize your online business in a smart way.

However, it takes some time and experience to dig through the clutter.

Setting up an efficient reporting and optimization structure is not an easy task either.

Metrics and dimensions are two crucial terms you need to grasp to really understand and master Google Analytics.

After reading this post you know all about the why and how behind metrics and dimensions.

Introduction to Metrics and Dimensions

Let’s start with sharing a random, example report with you:

Example report - metrics and dimensions

This report consists of:

  • Two dimensions: Medium and Country
  • Three Metrics: Sessions, % New Sessions and New Users

On default, Google Analytics shows just one dimension in their standard reports.

A simple trick helps you to add secondary (and even tertiary!) dimensions to your reporting set.

The next section contains 10 different aspects of metrics and dimensions I recommend to read carefully.

1. Know the Difference

Before diving into other related topics, you need to understand the difference between dimensions and metrics.

Some people might think and say:

“A metric is tied to a number and a dimension isn’t.”

In most cases this is true, but unfortunately not always.

Two examples of dimensions that are actually numbers:

  • Hour of day
  • Screen resolution

So how can you distinguish between them?

  • A dimension is a characteristic of an object that can be given different values —> a dimension describes data
  • A metric is an individual element of a dimension which can be measured as a sum or ratio —> a metric measures data

For more information I recommend to visit this Google Analytics help article on dimensions and metrics. It clearly explains the difference between them.

2. Use Them as Building Blocks of Your Reports

In Google Analytics you have 80+ standard reports that contain a lot of information.

It depends on your business, KPIs and what you are trying to optimize which reports are useful and which are not.

Pick the reports based on what you are trying to solve or improve.

Tip: always start with a question before you get lost in your Google Analytics data.

Besides this huge set of standard reports, Google Analytics let’s you fully customize your reports.

Custom reports are a great help in combining metrics and dimensions that fit your business in the best way.

Google Analytics - Custom reports environment

Literally all Google Analytics reports are based on combinations of dimensions and metrics.

You can choose to start from scratch when building custom reports. However, it might be more efficient to take your standard reports as your starting point.

3. Apply Them to Better Understand Your Audience

A while ago I wrote a post on the adjusted bounce rate metric. I was happy to receive this comment from one of my valuable readers:

Comment Saif UllahI have answered to Saif that I agree – in general – that an overall bounce rate below 40% is quite good.

However, it doesn’t tell the complete story.

I will explain it with an example:

MetricsDimensionsThe website MetricsDimensions.com has 700 entrances in period X.

In total there are five landing pages, including the homepage. Bounce rates vary from 10 to 90% with an average of 50%.

Without segmenting your data (and using dimensions) you would focus on the 50% number.

Please note: most of the time averages do lie.

In this particular case it seems that the audience loves page B and C and hates page A and D.

Is this huge difference related to any particular traffic sources or device types? Or what other factors do influence these numbers?

Segmentation based on dimensions is very important if you want to understand your audience better.

It’s easy to fall in a trap and draw wrong conclusions.

4. Build Powerful Segments

In my last point I did explain about using dimensions to understand your data and audience better.

Besides applying them directly in reports, Google Analytics contains a great segment building module:

Google Analytics - Segment Building Module

You have missed a great deal of optimization opportunities if you haven’t used this module yet.

What does it have to do with metrics and dimensions?

Well, metrics and dimensions are important building blocks for custom segments (they can be applied to almost all reports in Google Analytics):

Segment building - metrics and dimensions

A few things to note here:

  • Dimensions are always in green and are tied to a character match (e.g. contains, does not contain, matches regex etc.)
  • Metrics are always in blue and are tied to a calculative matching condition (e.g. =, >, < etc.)
  • Sometimes there are metrics and dimensions in the same group. For example: Behavior and Ecommerce.
  • It is not possible (yet) to apply metrics and dimensions to your goal funnel reports. Happily there are a few smart ways to segment your funnel reports.

Make sure to check out dimensions and metrics in the segmentation building environment!

5. Don’t Memorize the Complete List

There are simply too many metrics and dimensions in Google Analytics.

You will get a headache and there is no need for knowing them all.

If you are getting more and more proficient in working with them, you will know which are useful and in what situation.

What type of site is involved and what are the main KPIs? Are there any segments that can be listed upfront?

Select the ones that are in line with your business goals.

It happens that old metrics vanish and new metrics are added to the list.

Example: last year the metric visits changed into sessions.

6. Know That Not All Metrics and Dimensions Go Together

There are virtually unlimited combinations of dimensions and metrics to build the reports that suit your needs.

However, it is possible some of your combinations might not work out.

Two easy ways to find out whether metrics and dimensions make a good match:

The second option reveals which metrics or dimensions don’t match by simply graying them out.

Here is an example:

example of metrics and dimensions that don't match7. Keep a List of Your Favorite Metrics and Dimensions

Are you working as a consultant with recurring, specific analysis needs?

I recommend that you keep a list of your most used metrics and dimensions. It helps you to speed up your analysis.

8. Use Them to Effectively Extract Data Via the API

In one of my latest articles I have explained about three ways to export data from Google Analytics.

Two of the options are related to using the Google Analytics API.

If you start using the API, the two most powerful building blocks of the API are dimensions and metrics.

One more reason to dive into them and learn at least the basics!

9. Understand That the API List Deviates from Segment List

In the API you have a broader choice in selecting metrics and dimensions if compared to the ones you can use in Google Analytics segments.

At the time of writing this is the current list comparison:

API and Segments - comparison M&D

At a quick glance you can see that the following groups are not available in segments:

  • Channel Grouping
  • DoubleClick Campaign Manager
  • Site Speed
  • Social Activities
  • Social Interactions

10. Try Custom Metrics and Dimensions for Pros

The last point I like to make is about custom metrics and dimensions. Don’t think about this if you are just starting out.

If you have some experience and want to take your business and insights to the next level, this might be something for you!

The way Google Analytics puts it:

“Custom dimensions and metrics allow you to bring additional data into Google Analytics that can help you answer new questions about how users are interacting with your content.”

Recommended articles:

Well, this is it.

I am a happy man – if you agree on how important and powerful these metrics and dimensions are after reading this post!

I am sure you will love my new eBook if you enjoyed reading this article!

It contains 100 actionable tips to grow your online business.

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#GAtips: Plot Rows to Compare Segments

Google Analytics offers many great features. Unfortunately some of them are hidden under the surface.

Or should I consider myself lucky to know where to find and how to use them. :-)

Anyway, like you already know, I don’t like to hold things back.

On default, if you select a Google Analytics report you see something like this:

Google Analytics default reportYou just see one data line with the option to compare two metrics.

In my opinion, most often comparing two metrics doesn’t deliver great results in Google Analytics. In that case I prefer to build my own reports in Excel.

“Plotting rows” is a very powerful way to compare data segments without the need to set up segments first.

On the left of “Secondary dimension” you see this grayed out Plot Rows button.

Tip 10: Plot Rows to Compare Segments

Why to Plot Rows in Google Analytics

In Google Analytics, you can compare segments on the aggregated level over the time period you have selected.

That is useful to a certain extent, but what if you would like to compare trends directly in Google Analytics?

Or maybe you have not yet set up your automated exporting tools and still want to export segmented data in an efficient way?

Plotting rows will save you a huge amount of time!

How to Plot Rows: Device Category Example

For the sake of this tutorial I use device categories in relation to plotting rows.

Google Analytics can show you data from:

  • Mobile
  • Tablet
  • Desktop

If needed, you can dive deeper into each category.

Please note that you can select six rows at maximum. This in addition to the aggregated data line.

How it works:

How to plot rowsAs you can see, you have two options here:

  • Select all devices (rows) at once
  • Select one or more devices (rows)

After you select the device categories, you need to hit the Plot Rows button.

In my example it looks like this:

Plot rows device categoryLet’s assume we would like to see the goal conversion rate instead of sessions. Besides that we need data on trends so we select six months of data. And we would like to see the data aggregated per month.

Here is the resulting graph:

Plot rows example - goal conversion rateIn the table below the graph you can only see aggregated numbers for this time period.

This graph shows a lot more! Think about the trend per device category and the relative performance in relation to the overall goal conversion rate. Interesting numbers!

In short, by plotting rows a lot more information is visually displayed to judge the performance of specific segments.

Tips:

  • When exporting data (e.g. for different campaigns or traffic sources) I recommend to plot the maximum number of rows that you need
  • Select up to four different rows when you want to use the graph function (otherwise numbers are getting hard to compare)

Why You Still Want to Use Segments

This plot rows function is powerful in specific situations. Please keep in mind that it only affects the report currently selected.

Google Analytics segments provide you with the ability to analyze many different reports for the selected segments.

Now it’s your turn.

What do you think about this feature and have you already used it?

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Three Learnings from My Adjusted Bounce Rate Case Study

A few weeks ago I wrote a post on adjusted bounce rate. I did explain why you should consider changing the default bounce rate calculation. And how to set this up via Google Tag Manager.

In the last month I have analyzed a huge amount of data on my website.

This article contains the most interesting insights and learnings from my adjusted bounce rate case study.

Data adjusted bounce rate OnlineMetrics

As you can see, my bounce rate values changed dramatically after December 25th 2014.

New Bounce Rate Calculation

Standard bounce rate in Google Analytics:

“Bounce Rate is the percentage of single-page sessions (i.e. sessions in which the person left your site from the entrance page without interacting with the page).”

In this calculation, “time on landing page” is not taken into consideration.

Before the 25th I have done some tests with the following time stamps:

  • 30 seconds (if a website visitor spends > 30 seconds on landing page, no bounce)
  • 1 minute (if a website visitor spends > 60 seconds on landing page, no bounce)
  • 2 minute (if a website visitor spends > 120 seconds on landing page, no bounce)
  • 3 minute (if a website visitor spends > 180 seconds on landing page, no bounce)

My bounce rate roughly varied from 15 to 45% with these four time stamps.

I have decided – based on my experiences – to set my adjusted bounce rate at 60 seconds.

My adjusted bounce rate calculation:

“Adjusted Bounce Rate is the percentage of single-page sessions (i.e. sessions in which the person left your site from the entrance page without interacting with the page) with time spent on landing page less than 60 seconds.

I feel that if you spend 60 seconds or more on my website (first page), you are most probably consuming my content.

Remember to experiment and set a time stamp that fits your unique website and audience in the best way. It doesn’t make sense to simply copy my numbers.

Three Important Lessons

It has been a very interesting experience and I have learned a lot.

That’s why I like to share the most important learnings with you.

A Lower, More Actionable Bounce Rate

If you run a blog like me, you know that your standard bounce rate is relatively high.

Most people consume – especially when landing after a longtail search – the first content page and leave. This is natural. You didn’t choose to buy a book.

In my case, the standard bounce rate varies between 65 and 75%. Compared to other bloggers, this is normal and actually quite good.

However, after implementing my adjusted bounce rate of 60 seconds, my bounce rate is around 20 to 25%!

In other words:

“More than 75% of my website visitors spend at least 60 seconds on the first page or view at least two content pages. I am pretty satisfied with this number. This helps me to judge the quality of my content in a better way.”

Which Content is The Most Sticky

As the title might already imply, I have measured differences in adjusted bounce rate (stickyness) of my blog landing pages.

In the overview below you can see some more details:
(top 20 blog posts based on number of sessions and filtered on bounce rate)

Landing page and ABR

The stickyness of the content varies between 14 and 29%. As I collect more and more data, the reliability of the numbers will grow.

“What content do YOU value the most and where should I write about more in the near future?”

Of course I could run a survey and ask this question to my valuable readers like you.

I love to hear your opinion, but this data set is worth a lot as well!

Correlation Between Bounce Rate and Pages per Session

Another thing I discovered is that there is no correlation between bounce rate and pages/session.

I have used a scatter plot (Excel 2013) to visualize any relationship there might be:

Pages per Session vs. Bounce Rate

There are 20 landing pages in this sample.

I have used the Excel function CORREL to calculate the number.

In my case the correlation coefficient equals -0,05.

Since this number is very low (near 0), it means there is a no (lineair) relationship between adjusted bounce rate and pages/session.

After collecting more data I might analyze this metric again in the near future.

Tip: set up a different GA property first before implementing adjusted bounce rate in your main data view. You can always decide to keep running two trackers on your website.

Not Running a Blog?

You are (not yet) running a blog? It doesn’t matter!

You could answer a ton of questions with this adjusted bounce rate metric. It doesn’t matter in what industry you are working or what type of website you have.

This metric delivers great insights on blogs, ecommerce sites, services sites, lead gen sites…

Really, just give it a try!

I hope my bounce rate case study puts you in the right direction.

Best of luck with your optimization efforts!

I am sure you will love my new eBook if you enjoyed reading this article!

It contains 100 actionable tips to grow your online business.

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