Aprobar el examen de google analytics ruben velasco

Aprobar el Examen de Google Analytics IQ
(Última actualización: Febrero 2012)

Documentación extraída de los vídeos de
Conversion University
Editado por Rubén Velasco
www.rubenvelasco.es

1. First Steps............................................................................................................................... 3
1.1. Introduction to Google Analytics ................................................................................... 3
1.2. Installing the Google Analytics Tracking Code ............................................................... 9
1.3. Working with Report Data ................................................................................................... 17
2. Interpreting Reports..................................................................................................... 32
2.1. Pageviews, Visits, and Visitors ..................................................................................... 32
2.2. Time Metrics ................................................................................................................ 38
2.3. Traffic Sources .............................................................................................................. 42
2.4. Content Reports ........................................................................................................... 51
3. Fundamentals ...................................................................................................................... 57
3.1. Account Administration ............................................................................................... 57
3.2. Campaign Tracking and AdWords Integration ............................................................. 70
3.3. Analysis Focus – AdWords ........................................................................................... 86
3.4. Goals in Google Analytics ............................................................................................. 86
3.5. Filters in Google Analytics .......................................................................................... 101
3.6. Regex and Google Analytics ....................................................................................... 118
3.7. Cookies and Google Analytics .................................................................................... 132
3.8. E-commerce Tracking ................................................................................................. 141
3.9. Domains and Subdomains.......................................................................................... 151
4. In Depth Analysis ............................................................................................................... 162
4.1. Advanced Segmentation ............................................................................................ 162
4.2. Analytics Intelligence ................................................................................................. 169
4.3. Internal Site Search .................................................................................................... 169
4.4. Event Tracking and Virtual Pageviews ....................................................................... 182
4.5. Additional Customizations ......................................................................................... 193

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1. First Steps

1.1. Introduction to Google Analytics
In this lesson, you will learn:

 what Google Analytics can do for your business or website
 how Google Analytics works
 how often your data is updated and how Google stores it
 about data confidentiality

Google Analytics is a free, web analytics tool that is hosted by Google.

Google Analytics shows you how visitors actually find and use your site, so you'll be able to:
• make informed site design and content decisions
• improve your site to convert more visitors into customers
• track the performance of your keywords, banner ads, and other marketing campaigns.
• and track metrics such as revenue, average order value, and ecommerce conversion rates.


Google Analytics can help you answer important questions about your site and your marketing
initiatives, such as:

• How are visitors using my site?

• How can I make my marketing campaigns more effective and accountable?

• Is my content effective?

• Where are visitors abandoning their shopping carts and where do they go afterwards?

• How can I improve my site navigation and help my visitors get more out of the site?


Google Analytics has been designed to meet the needs of novice users as well as web analytics
experts.

Some of the features include:

• Map Overlay which can help you understand how to best target campaigns by geographic region

• AdWords Integration which makes it easy to track AdWords campaigns and allows you to use
Google Analytics from your AdWords interface

• Internal Site Search which allows you to track how people use the search box on your site

• Funnel Visualization so that you can optimize your checkout and conversion click-paths


Here’s how Google Analytics works.

When a visitor accesses a page on your site, a request is made to the webserver to display the page.

The page is served and the Google Analytics Tracking Code JavaScript is executed.

The Google Analytics Tracking Code, which is a snippet of code that you place on each page of your
site, calls the trackPageView() method.

At this point, the Google Analytics first- party cookies are read and/or written.

The webpage then sends an invisible gif request containing all the data to the secure Google
Analytics reporting server, where the data is captured and processed.

Data is processed regularly throughout the day and you can see the results in your reports.


Google Analytics uses only first-party cookies, which are considered safe and non-intrusive by most
internet users today.

Although many people block third-party cookies from being set by their web browsers, this won’t
affect Google Analytics.

Someone who blocks all cookies, however, won’t be tracked by Google Analytics since all the data is
passed to the Google Analytics servers via the first-party cookies.

Someone who deletes their cookies will still be tracked, but they’ll be identified as a new visitor to
the site and Google Analytics won’t be able to attribute their conversions to a prior referring
campaign.

People delete cookies for many reasons, one of which is to prevent personal data from being
captured or reported. But, note that Google Analytics does not report on personally identifiable
information. You’ll learn more about cookies as they relate to Google Analytics in a later module.

A much less common scenario is that a visitor to your site has disabled JavaScript on his or her
browser. A visitor who disables JavaScript won’t be tracked since the Google Analytics Tracking Code
cannot be executed.

Cached pages are saved on a visitor’s local machine and so they’re not served by the webserver.
Google Analytics will still track visits to cached pages as long as the visitor is connected to the
internet.

JavaScript errors occur when an element of a web page’s script contains an error or fails to execute
correctly. If an error occurs before the Google Analytics Tracking Code is executed, the visit to the
page won’t be tracked.

In general, no reporting tool can ever be 100% accurate. You’ll get the most out of web analytics if
you focus on trends. Knowing that 20% more visitors converted following a marketing campaign is
more powerful than knowing that exactly 10 people visited your site today.


All data collected by Google Analytics is anonymous, including where visitors comes from, how the
visitors navigate through the site, and other actions they may perform.

No personally identifiable information is collected.

Google does not share Analytics data with any 3rd parties.

Furthermore, Google optimization, support, and sales staff may only access a client’s data with the
client’s permission.

You may elect to share your Google Analytics data “with other Google products”, and Google will use
the data to improve the products and services we provide you. Electing to share your data
“Anonymously with Google and others” allows you to receive a benchmarking report. To provide
benchmarking, Google removes all identifiable information about your website, then combines the
data with hundreds of other anonymous sites in comparable industries and reports them in an
aggregate form.

If you select "do not share my Google Analytics data", you will not receive a benchmarking report
and may not have access to specific ads-related features such as Conversion Optimizer.

Again, regardless of your Data Sharing settings, Google does not share Analytics data with any 3rd
parties.


1.2. Installing the Google Analytics Tracking Code

 here to place the Google Analytics Tracking Code
 about website setups that require customization
 how to verify installation

Get started with Google Analytics in three simple steps.

First, sign up for a Google Analytics Account.

Second, install the provided code across all pages of your site.

Third, if you are using Google AdWords, link it to your Google Analytics account to report on cost and
click data.


There are two places you can sign up for a Google Analytics account.

You can go to google.com/analytics and click the “Sign up now” link.

Or, if you are already an AdWords user, you can create a new account via “Google Analytics” under
Reporting.


Google Analytics uses a combination of JavaScript and first party cookies to gather anonymous data
about your visitors.

As you set up your Google Analytics account, you will be provided with a tracking code. You’ll need to
install this tracking code across all pages of your site.


You’ll then see a table listing all the web properties for that account. Click the desired web property.

On the next page, click the Tracking Code tab.

This page gives you the asynchronous version of the Google Analytics Tracking Code. The
asynchronous version of the tracking code allows your site to run at its fastest, so we recommend
that you always use this version. Throughout this course, we use the asynchronous tracking code
whenever we illustrate a tracking technique. Traditional ga.js tracking is still used on many sites. To
see the traditional ga.js syntax, navigate to the URL shown on the slide.

Be sure to replace the "x"s in the code with your unique Google Analytics account number and
property index, which will be explained in the next slide.

Let’s look at the tracking code. This section of the code tells Google Analytics which account this
traffic belongs to. The number immediately following the “UA dash” is your unique Google Analytics
account number, and the number following the last dash is the property index. Review the lesson
on accounts and profiles to learn about the property index. This section of the tracking code
automatically detects secure versus non-secure pages. So, you can use the same tracking code on
both https and http pages.


The tracking code that is provided to you is designed to work with most site setups. In some cases,
however, you’ll need to make small updates to the tracking code on each of your pages.
For example, if you need to:
• Track multiple domains in one profile,
• Track more than one subdomain per profile, or
• Track multiple domain aliases, you should review the module on tracking domains and subdomains
-- and customize your code before adding it to your pages.

To install the JavaScript, copy your tracking code--either the code provided during setup, or your
customized snippet--and paste it into your page.


One of the main advantages of the asynchronous snippet is that you can position it at the top of the
HTML document. This increases the likelihood that the tracking beacon will be sent before the user
leaves the page. It is customary to place JavaScript code in the <head> section, and we recommend
placing the snippet at the bottom of the <head> section for best performance.

Here’s a sample.

To maintain tracking consistency, it is important that the code is installed across all pages of your
site.

If you buy keywords on Google AdWords, you can use Google Analytics to see how well your paid
keywords perform in terms of conversion rates, revenue, and ROI. You can compare search result
positions for each keyword and you can compare ad performance.

To do these things, you'll need to link your AdWords account to your Analytics account. Review the
module on Campaign Tracking and AdWords Integration for detailed instructions.

Urchin Software from Google is similar to Google Analytics, but Urchin runs on your own servers,
whereas Google Analytics is a service hosted by Google.

If you’ve licensed Urchin, you can run both Urchin and Google Analytics together on your site.
Running Urchin and Google Analytics together gives you a great deal of flexibility and analysis
capability.

You’ll need to make modifications to your tracking code. While this isn’t covered in the course, you
can learn how by following the link shown in the slide.


VERIFYING INSTALATION

Once you’ve installed your tracking code, it usually takes about 24 hours for data to appear in your
reports. The best way to verify that you are receiving data is to simply look at your reports.

You can also view your webpage’s source code to verify that the tracking code is installed.

Navigate your browser to any page on your site. Right click within the browser window and select
the “View Page Source” or “View Source” option in your browser.

This will open a new window that contains the source code for that page.


Now search for ga.js. (From the source code menu, select “Edit” and click the “Find” option.)

If you find the Google Analytics tracking code on your page, then it is likely that Google Analytics has
been successfully installed on your site.

Repeat this process across several pages on your site to make sure that your installation is complete.


1.3. Working with Report Data

 how to set date ranges and comparison date ranges
 how to graph data and access report views
 how to quickly filter and sort data in reports
 when to use annotations
 how to identify metrics and dimensions
 how to segment data using Advanced Segments

Use the Calendar to set your active date range – the time period for which you want to look at data.

Select date ranges by clicking on the day and month within the calendar or you can type dates in the
“Date Range” boxes.

Once you set a date range, it stays active until you change it, or log out.


You can use a comparison date range to see how your site is performing month over month, year
over year or even from one day to another.

The date range and comparison date ranges you select will apply to all your reports and graphs.

Most reports include an over-time graph at the top. You can make this graph display data by day,
week, or month.


You can attach short notes or annotations to specific dates. Annotations are especially useful when
you’re looking at historical data and wondering whether certain campaigns or outside events had
some effect on your traffic.

To add an annotation, just click the date on the graph and select “Create new annotation”.

You can allow anyone with access to the profile to see the annotation, or make it private so that only
you see it.


A metric is a measurement. Examples of metrics are “number of visits”, “pages viewed per visit”, and
“average time on site”.

Metrics appear in scorecards and as columns in tables.

Metrics can also be graphed.


You can graph any metric in a scorecard, simply by clicking it. Here, we’ve graphed Average Time on
Site.

You can compare two metrics on the same graph to see how they are correlated.

Click Compare Metric and select from the drop down.

In this example, we’re adding Average Time on Site to the graph.


Groups of metrics are organized into tabs.

The Site Usage tab shows metrics such as the number of pages viewed per visit, the average time on
site, and the bounce rate.

Goal Set tabs shows the conversion rates for each of your goals.

If you’ve enabled ecommerce, you’ll also see an Ecommerce tab.

The AdWords reports have an additional tab called Clicks. This tab contains AdWords related metrics
such as clicks, cost, revenue per click and ROI. The AdSense tab contains AdSense metrics such as
revenue from AdSense and AdSense ads clicked.


Many reports contain tables. These tables usually break out your data by a single dimension.

Each row in the table shows the data for a different value of the dimension.

In this example, the dimension being shown is City. Each row contains the data for a different city.

Each row in this table corresponds to a kind of browser – Internet Explorer, Firefox, Chrome and so
on. So, this table is showing data for different values of the dimension “Browser”.


The Viewing option above the table lets us change the dimension. If we click Operating System as the
Viewing Option, the table shows data for each kind of operating system.

We can also add a secondary dimension. This lets us see data for each combination of two
dimensions. In this example, the table shows data for each operating system.

Let’s look at what happens if we select Browser as a secondary dimension.


Now we can see data for each Operating System and Browser combination.

So, we can see data for Windows and Firefox, Windows and Chrome, Macintosh and Safari,
Macintosh and Chrome, and so on.

To filter the data that appears in a table, click the Search option above the table. In this example,
we’re excluding visits from London and New York and also excluding any visits in which there were
fewer than 2 pages viewed.


The View option lets you visualize data in different ways. The Data view organizes your report data
into a table. This is the default view for many reports.

The Percentage view creates a pie-chart based on any one of the metrics in the report.

The Performance view shows a bar-graph based on any metric you select.


The Comparison view allows you to quickly see whether each entry in the table is performing above
or below average.

Term Cloud helps you visualize your keywords.

Pivot creates a pivot table in which both rows and columns can break out dimension values.


In this example, we can see how many visits were referred by each combination of keyword and
search engine.

Keywords are shown as rows and search engines are shown as columns.

You can select the metrics you want to display in the table and the dimensions.

Columns within tables can be sorted in both ascending and descending order simply by clicking on
the column heading.

The arrows next to the heading title indicate the order in which the results are listed.

A down arrow indicates descending order and an upward arrow indicates ascending order.


By default, all reports with tables display ten rows.

To display more than ten rows, go to the bottom of your report and click the dropdown menu arrow
next to “Show rows”.

You can display up to 500 rows per page.


An advanced segment is a subset of your data.

For example, by selecting Visits with Transactions, you can limit your analysis to just the visits during
which a person bought something.

If you apply a single advanced segment, all your reports are limited to the data in that segment until
you select a different segment. You can always go back to seeing all your data y selecting the All
Traffic segment.

You can select up to four segments at a time. This allows you to compare data for each segment side
by side as you go through your reports.

In this case, we’ve selected three segments: Visits with Transactions, Search Traffic, and Paid Search
Traffic.


The Advanced Segment pulldown shows two kinds of segments: Default Segments and Custom
Segments.

Default Segments are predefined and available to anyone using Google Analytics.

Custom Segments are segments that you define. We’ll learn how to create custom segments in later
lesson.


2. Interpreting Reports

2.1. Pageviews, Visits, and Visitors

 the differences between Pageviews, Visits, and Visitors
 how Pageviews, Visits, and Visitors are calculated
 the difference between Pageviews and Unique Pageviews

In Google Analytics, a pageview is counted every time a page on your website loads.

So, for example, if someone comes to your site and views page A, then page B, then Page A again,
and then leaves your site -- the total pageviews for the visit is 3.


A visit -- or session -- is a period of interaction between a web browser and a website. Closing the
browser or staying inactive for more than 30 minutes ends the visit.

For example, let’s say that a visitor is browsing the Google Store, a site that uses Google Analytics. He
gets to the second page, and then gets a phone call. He talks on the phone for 31 minutes, during
which he does not click anywhere else on the site.

After his call, he continues where he left off. Google Analytics will count this as a second visit, or a
new session.

Note that throughout these modules, the words “visit” and “session” may be used interchangeably.


A visitor is uniquely identified by a Google Analytics visitor cookie which assigns a random visitor ID
to the user, and combines it with the timestamp of the visitor’s first visit.

The combination of the random visitor ID and the timestamp establish a Unique ID for that visitor.

You’ll learn more about the visitor cookie in a subsequent module.


Generally, the Visitors metric will be smaller than the Visits metric which in turn will be smaller than
the Pageviews metric.

For example, 1 visitor could visit a site 2 times and generate a total of 5 pageviews.

A pageview is defined as a view of a page that is tracked by the Google Analytics Tracking Code.


If a visitor hits reload after reaching the page, this will be counted as an additional pageview.

If a user navigates to a different page and then returns to the original page, an additional pageview
will also be recorded.

A unique pageview represents the number of visits during which that page was viewed--whether one
or more times. In other words, if a visitor views page A three times during one visit, Google Analytics
will count this as three pageviews and one unique pageview.

“Total Visitors” counts each visitor during your selected date range only once. So, if visitor A comes
to your site 5 times during the selected date range and visitor B comes to your site just once, you will
have 2 Visitors. Remember, a visitor is uniquely identified by a Google Analytics visitor cookie.

The “New vs. Returning” report classifies each visit as coming from either a new visitor or a returning
visitor. So when someone visits your site for the first time, the visit is categorized as “Visit from a
new visitor.” If the person has browsed your website before, the visit is categorized as “Visit from a
returning visitor.”

A high number of new visits suggests that you are successful at driving traffic to your site while a high
number of return visits suggests that the site content is engaging enough for visitors to come back.

You can look at the Frequency and Recency report to see how recently visitors have visited. And you
can look at the same report to see how frequently they return. The report is under Behavior in the
Visitors section.


The Visitors metric -- in other words the number of visitors who came to your site -- is found in the
Visitors section.

The Visits metric is found in the Visitors section and the Traffic Sources section.

The Pageviews metric can be found in the Visitors Overview and in the Content section reports. Most
of the other reports show Pages Viewed per Visit instead of Pageviews.

Unique Pageviews is only found in the Content section.


2.2. Time Metrics

 how Time on Page and Time on Site are calculated
 how Avg. Time on Page and Avg. Time on Site are calculated

To calculate Time on Page, Google Analytics compares the timestamps of the visited pages.

For example, in the slide, the visitor saw page A, then page B, and then left the site.

The Time on Page for page A is calculated by subtracting the page A timestamp from the page B
timestamp.

So, the Time on Page for page A is 1 minute and 15 seconds.

In order for this calculation to take place, the Google Analytics Tracking Code must be executed on
both pages.

The Time on Page for page B is 0 seconds, because there is no subsequent timestamp that Google
Analytics can use to calculate the actual Time on Page.


Now, suppose the visitor continued on to a third page before exiting.

The second page now has a Time on Page of 1 minute 10 seconds.

The Time on Site is now calculated as 2 minutes and 25 seconds.


For Average Time on Page, bounces are excluded from the calculation. In other words, any Time on
Page of 0 is excluded from the calculation.

For Average Time on Site, bounces remain a part of the calculation.

To calculate Average Time on Site, Google Analytics divides the total time for all visits by the number
of visits.

Some sites make extensive use of Flash or other interactive technologies.

Often, these kinds of sites don’t load new pages frequently and all the user interaction takes place on
a single page.

As a result, it’s common for sites like this to have high bounce rates and low average times on site.

If you have such a site, you may wish to set up your tracking so that virtual pageviews or events are
generated as the user performs various activities.

You can learn how to do this in the module on Event Tracking and Virtual Pageviews.


Visit Duration categorizes visits according to the amount of time spent on the site during the visit.

The graph allows you to visualize the entire distribution of visits instead of simply the ‘Average Time
on Site’ across all visits.

You can see whether a few visits are skewing your ‘Average Time on Site’ upward or downward.

Visit Duration can be found in the Engagement report under Behavior in the Visitors section.


2.3. Traffic Sources

 about the different kinds of traffic sources
 how to identify quality traffic
 how to identify revenue and conversion drivers
 what kinds of information to look for in keyword reports
 how campaign attribution works in Google Analytics

The reports in the Traffic Sources section show you where your traffic is coming from on the internet.

You can compare your traffic sources against each other to find out which sources send you the
highest quality traffic.


Direct Traffic represents visitors who clicked on a bookmark to arrive at your site, or who typed the
URL directly into their browser.

Referring Sites include any sites that send traffic to you. These could be banner ads or links featured
on blogs, affiliates, or any site that links to your site.

Search Engine traffic represents visitors who click on a search results link in Google, Yahoo, or any
other search engine.

Search Engine traffic can be organic -- in other words, free search results -- or paid.

Paid search engine traffic is pay per click or cost per click traffic that you purchase from a search
engine -- for example on Google AdWords.

Understanding which search engines send you qualified traffic can help you select the search engines
on which you want to advertise.


Looking at the highest traffic drivers is a start, but it doesn’t tell you whether the traffic was
qualified.

In other words, did the traffic help you achieve the goals you’ve set for your site?

One easy indicator of quality is Bounce Rate -- the percentage of visits in which the person left
without viewing any other pages.

In the slide, although blogger.com sent the most traffic, it has an 88% bounce rate. A bounce rate
this high suggests that the site isn’t relevant to what the visitor is looking for

By clicking the “compare to site average” icon and selecting a comparison metric, you can see which
sources outperform and underperform the site average.

So here, for example, if we select Bounce Rate as our comparison metric. we can see that the two
most popular sources of traffic underperform the site average.

One note about bounce rate, if your site is a blog, bounce rate may not be relevant. With blogs, it’s
common for people to look at a single page and then leave.


The All Traffic report lists all of the sources sending traffic to your site -- including referrals, search
engine traffic, and direct traffic

This report is particularly helpful because you can identify your top performing sources, regardless of
whether they are search engines or sites.

For example, in the report, we see that blogger.com referred more traffic than any other source. It
has a medium of referral because it is a referral from a site.

The second most popular source of traffic was direct. Direct traffic always has a medium of (none).

Free Google search engine traffic was the fourth largest referrer.

The medium of organic tells us that this traffic came from clicks on unpaid search engine results.

The medium of cpc on this entry -- for cost per click -- tells us that this traffic came from paid search
results.

You may sometimes see _referrals_ from google.com. These can come from Google Groups posts or
static pages on other Google sites.


If you have goals or ecommerce set up on your site, you have a much wider range of metrics with
which to assess performance.

Click on the Goal Set or Ecommerce tabs to view which sources are driving conversions and
purchases.

In this case, we’re looking at metrics on the Ecommerce tab and comparing each traffic source’s
revenue with the site average.


To see the keywords that people used to find your site, go the Search Overview under Incoming
Sources in the Traffic Sources section.

Then, in the Search Overview report, click Keyword as the viewing option.


Looking at keywords is a very useful for understanding what visitors were expecting to find on your
site.

Keywords with a high bounce rate tell you where you failed to meet that expectation.

For example, in the slide example, the ‘google games’ phrase has a 84% bounce rate. Let’s find out
what landing page is being used. We start by clicking on the ‘google games’ entry in the table.


This takes us to the Keyword report for ‘google games’.

To find out which landing page is being used for this keyword, we’ll click Other as the Viewing Option
above the table, and select Landing Page.

We can now see which landing page is being used and evaluate it’s relevance to the keyword.


This report can be particularly helpful if multiple landing pages are being used.

You can find out which landing pages are responsible for the poor performance and send the
keyword traffic to the most effective landing page.

Be sure to also check the bounce rates for organic, non-paid keywords. This information can offer
insights into how to best focus your search engine optimization efforts.

By default, Google Analytics attributes a conversion or sale to the campaign that most recently
preceded the conversion or sale.

For example, if a visitor clicks on an AdWords ad (Campaign 1 in the first session) and then later
returns via a referral to purchase something (Referrer 1 in the second session), the referral will get
credit for the sale.

However, if instead the visitor returns directly, then the AdWords ad (Campaign 1) will still get credit
for the sale.

To prevent a specific referral or campaign from overriding a prior campaign, simply append
“utm_nooverride=1” to all referring campaign links as shown in the slide. This ensures that the
conversion is always attributed to the original referrer (or first campaign the user clicked on).

Therefore, in the example above, the original campaign will continue to get credit for the conversion.

If a visitor returns via a link without the utm_nooverride, as in the third example, that campaign will
get credit for the sale since it overwrites all previous referring campaigns.


2.4. Content Reports

 how to use the Pages and Content Drilldown reports
 how to use the Landing Pages report
 how to use and interpret the Navigation Summary report
 how to use and interpret the Entrance Paths report

Two reports in the Content section focus on page traffic, but each report organizes it differently.

The Pages report lists each page that received traffic.

The Page Title viewing option on the Pages report groups your pages according to Title tag. You can
click on a title to see the pages that share that title.

The Content Drilldown report groups pages according to directory. You can click on a directory to see
the pages in the directory.


The Landing Pages report lists all of the pages through which people entered your site.

You can use this report to monitor the number of bounces and the bounce rate for each landing
page.

Bounce rate is good indicator of landing page relevance and effectiveness.

You can lower bounce rates by tailoring each landing page to its associated ads and referral links.
The more relevant the page, the less likely a visitor will be to bounce.


The Navigation Summary can help you understand how people move through your site.

It shows how people arrived at a specific page and where they went afterwards. The report is
available from the Pages report.

Here’s the Navigation Summary report.


Percent Entrances shows how frequently the page was a landing page.

Percent Previous Pages shows how frequently visitors came to the page after viewing another page
on the site.

Percent Exits shows how frequently visits ended on this page.

Percent Next Pages shows how frequently visitors continued on to another page on the site.

The list of pages that were viewed immediately before the page or pages is shown in the left column,
under Previous Page Path.

The list of pages that were viewed immediately after the page or pages is shown in the right column,
under Destination Page.

The Entrance Paths report is a powerful tool for analyzing navigation paths.

For example, let’s say that you want to find out whether people clicked the Purchase button on your
landing page and actually completed the purchase. To find out, go to the Landing Pages report and
click Entrance Paths.


Select the landing page you want to analyze.

In the left column, you’ll see all the possible clicks people made on the page. Choose the link that
represents the Purchase page.


In the right hand column, you’ll now see all the pages visitors went to after the Purchase page. By
looking at this list, you’ll be able to see how many visits ended up on the Purchase Completion page.

This report can show you if the landing page is doing the job you designed it for.


3. Fundamentals

3.1. Account Administration

 how to create, manage, and delete accounts
 best practices for managing accounts
 the differences between Administrators and Users
 when to create profiles
 how to create, manage, and delete profiles

Click the Account Administration icon to manage your accounts, web properties, profiles, and user
access. (You can find the icon at the top right of any screen in Google Analytics.)

You’ll be taken to the Account Administration screen which lists all of the Analytics accounts to which
you have access.


The ”Plus New Account” button is how you would create a new analytics account under the login
that you are currently using.

So, when should you create a new account? If you manage the analytics services for several websites
which belong to different organizations, you’ll generally want to create a new account for each
organization. We’ll discuss this best practice in a few minutes.

You are permitted to create up to 25 analytics accounts per Google username. However, you can be
added as an administrator to an unlimited number of accounts.

To administer an account, just click on it in the table.


To give other users access to a Google Analytics account, click on the account name in the Account
Administration screen.

You’ll be taken to a screen similar to the one shown in the slide.

Click the User tab.

All of the users who currently have access to the account will be listed in the table.

There is a settings link for each user in the table. Click this link to edit the user’s name, email address,
or to change their Role – either administrator or user.


There are two Roles. “Administrators” have access to all reports and they can also modify settings.

So, Administrators can create profiles, filters, and goals, and they can add users.

Users only have read access to your reports and they can’t modify analytics settings. Also, “Users”
can be restricted to viewing only specific profiles.


To add a user, click the Plus New User button.

A screen that looks like this will appear. Enter the user information in the form.

In order for you to add a new user, they must have a Google Account.


If they don’t have a Google Account, ask them to create one at google.com/accounts.

Select a Role for the new user.

You can either grant read-only access to certain reports or you can make them an administrator.
Remember that administrators can view all reports and modify account settings.


If you select User as the role, the interface will show you a list of all profiles associated with your
account.

Select the profiles you would like this user to have access to and click the “Add” button to apply your
changes.

To modify access for an existing user, find the user on the Users tab and click settings.

You can change the user’s role or change the profiles he or she can access.

Select the profiles you would like to remove report access to and click the “Remove” button.


Remember that an administrator has full administrative access to all profiles within the account.

If you manage the analytics services for several websites which belong to different organizations, the
best practice is to create a separate Analytics account for each organization.

Otherwise, if you were to group all the websites of all the different organizations into a single
account, any Administrators you created on the account would have access to all the reports for all
the websites.

Not only would the administrators be able to see the reports of other organizations, they’d also be
able to change analytics settings on profiles that don’t belong to them.

This raises the potential for an Administrator to accidentally edit -- or even delete -- another
organization’s settings and data.


If you want to change your e-mail login, create a new Google account. Add your new login as an
administrator to your Google Analytics account.

PROFILES


A profile is a set of rules that defines the data you see for a web property. For example, you might
have web property example.com for which you have three profiles.

One of the profiles might show all the data for all the traffic that comes to example.com.

Another profile might use filters to only show the data for traffic to a certain subdirectory.

Still another profile might use a different set of filters to show only another subset of data.

To see a list of the profiles that belong to a specific web property, navigate to that web property
from the Account Administration screen.

Once you are on the screen for the web property, click the Profiles tab. On the Profiles tab, you’ll see
a Profile selector menu that lists all the profiles.

Profiles are very flexible -- they are basically just a set of rules that define what data is to be included
in the reports.

Here is a schematic showing an Analytics account with one web property and two profiles.

Both profiles contain traffic data for the example.com web property.

One profile might contain all the traffic data.

The other profile might be filtered so that it contains only traffic from AdWords visitors.

In addition, you might want to give certain users access only to the filtered profile. This has the effect
of only allowing these users to see AdWords traffic to example.com.


Here is the Profiles tab for the “example.com test 1” profile.

If you are an administrator on the account, you’ll see the sub-tabs that list the Assets, Goals, Users,
Filters, and Profile Settings that are associated with the profile.

You’ll also see the “Plus New Profile” button – which you can use to create a new profile.

But, if you are not an administrator, you’ll only see the Assets tab.

That’s because you need to be an admnistrator to add new profiles or to edit a profile’s goals, users,
filters, and settings.

However, you don’t need to be an administrator to add or edit assets.

This includes advanced segments, annotations, and custom alerts.


Each profile has its own goals, which you set on the goals sub-tab.

You control who has access to the profile via the Users sub-tab.

And, you can use the Filters sub-tab to control what data is included in the profile.


The Profile Settings sub-tab is where you enable e-commerce and site search reports, set your
preferred time zone, and other settings.

To remove a profile, you can simply click Delete this profile on the Profile Settings sub-tab. You’ll
need to be an Administrator to do this.

Be careful that you are deleting the correct profile, because you won’t be able to recover the
historical data for the profile once it’s been deleted.


3.2. Campaign Tracking and AdWords Integration

 how to track campaigns using tagged links
 how to track AdWords campaigns
 when to use autotagging and how it works
 how to enable autotagging
 where to find AdWords data in your reports
 the expected kinds of data discrepancies between AdWords and Analytics data
 when and how to manually tag URLs
 how to use the URL Builder
 best practices for tagging links

Google Analytics allows you to track and analyze all of your marketing campaigns -- including paid
search campaigns, banner ads, emails and other programs.


There are two ways to track ad campaigns.

For AdWords campaigns, you should enable keyword autotagging. This allows Google Analytics to
automatically populate your reports with detailed AdWords campaign information.

In order to enable autotagging, you’ll need to link your AdWords and Google Analytics accounts; we’ll
look at this in more detail in the next slide.

The second way to track campaigns is to manually tag links. So, for example, you could tag the links in
an email message with campaign-identifying information. You may also choose to manually tag
AdWords links if you do not wish to enable autotagging.

The tags are campaign variables that you append to the end of your URLs.

INTEGRATION WITH ADWORDS: LINKING GOOGLE ANALYTICS WITH ADWORDS


By linking Google Analytics to your AdWords account, you can get advanced reporting that measures
performance and ROI for your AdWords campaigns.

Within AdWords, select Google Analytics under the Reporting tab to link your accounts. The
AdWords login that you’re using will need administrator privileges in Analytics in order to link the
accounts.

If you don’t already have an Analytics account, you’ll be able to create one.

When you link your accounts, you should enable "Destination URL Autotagging”. This option allows
you to differentiate your paid ads from organic search listings and referrals and allows you to see
detailed campaign information in the AdWords section of your Traffic Sources reports.

Your cost data -- the information about clicks and keyword spending -- will be applied once you link
your accounts. If you don't want cost data imported into a particular profile, you can edit the profile
settings and de-select the cost data option -- after you've completed the linking process.


AUTOTAGGING LINKS

Autotagging your links is important because it helps Analytics differentiate the traffic coming from
Google paid listings, outlined in green on the slide, and traffic coming from Google organic listings,
which are outlined in red.

If autotagging is not enabled, your Analytics reports will show that the clicks from the sponsored
listings and the organic listings are both coming from the same source: google organic.

By default, Analytics considers them both to be from Google organic search results.

So, enabling autotagging allows you to see which referrals to your site came from your paid Google
campaigns and which ones came from Google organic search results.


Autotagging works by adding a unique id, or g-c-l-i-d, to the end of your destination URLs.

This unique id allows Analytics to track and display click details in your reports.

It is important to note that 3rd party redirects and encoded URLs can prevent autotagging from
working properly.

You should test these cases by adding a unique parameter to the end of your URL --- for example you
could add ?test=test.

Test to make sure that the parameter is carried through to your destination page and that the link
doesn’t break.

Notice that the first query parameter is always preceded with a question mark. Subsequent values
are separated using ampersands.


Here’s an example of a gclid appended to the end of a URL.

To enable autotagging, select “Account Preferences” under “My Account”.

Make sure that the Tracking option reads “yes”. If it says “no”, click the edit link, check the box for
“Destination URL Autotagging”, and click “Save Changes”.


When linking your AdWords account to Analytics for the first time, you’ll be prompted to
automatically select “Destination URL Autotagging” and “Cost Data Import”.

If you want to change your autotagging settings later, you can do so by editing your AdWords
account preferences.

IMPORTING COST DATA FROM ADWORDS TO GOOGLE ANALYTICS

All AdWords cost data from an account will be imported into any profile in which the Apply Cost Data
checkbox is selected.

Make sure both your AdWords and Analytics accounts are set to the same currency so that ROI data
is accurately calculated.

Recall that when linking your AdWords account to your Analytics account, your cost data will be
applied to all of your profiles.

If you don't want cost data imported into a particular profile, you can edit the profile settings. Within
the "Edit Profile Information" screen, find the "Apply Cost Data" checkbox. De-select this checkbox.

And finally, note that Google Analytics is only able to import cost data from AdWords, and not from
other ad networks.


DATA DISCREPANCIES BETWEEN ADWORDS AND GOOGLE ANALYTICS

You may notice differences between the data in your Google Analytics and AdWords reports. There
are several reasons for these differences.

First, AdWords tracks clicks, while Analytics tracks visits. Second, some visitors who click on your
AdWords ads may have JavaScript, cookies, or images turned off.

As a result, Analytics won't report these visits, but AdWords will report the click.

You’ll also see differences between Analytics and AdWords if the Google Analytics Tracking Code on
your landing page doesn’t execute.

In this case, AdWords will report the click but Analytics will not record the visit.

Invalid clicks may also cause reporting differences because while Google AdWords automatically
filters invalid clicks from your reports, Google Analytics will still report the visits.

Finally, keep in mind that AdWords data is uploaded once a day to Analytics so the results for each
may be temporarily out of sync.


Make sure that your landing pages contain the Google Analytics Tracking Code. If they don’t,
campaign information will not be passed to Analytics, but clicks will register in AdWords.

Make sure that you have autotagging enabled. Otherwise, visits will be marked as Google Organic
instead of Google CPC. While we strongly recommend that you use autotagging instead of manual
tagging, if you do manually tag your destination URLs, you must make sure that all of them are
tagged, otherwise data discrepancies will occur.

Be aware that campaign data can be lost if your site uses redirects. As a result, Analytics won’t show
the visits as coming from AdWords, but your AdWords report will still report the clicks.


MANUAL CAMPAIGN URL TAGGING

Google Analytics automatically tracks all of the referrals and search queries that send traffic to your
website.

However, if you are running paid advertising campaigns, you should add tags to the destination URLs
of your ads.

Adding a tag allows you to attach information about the campaign that will show up in your Analytics
reports.


Although it’s possible to manually tag your AdWords ads, you should enable auto-tagging instead.

If you manually tag your AdWords ads, the AdWords reports will only show you information by
Campaign and Keyword.

If you enable auto-tagging, you’ll be able to see much more detail. The AdWords reports will show
you results by ad group, matched search query, placement domain and many other AdWords
attributes.


There are five variables you can use when tagging URLs. To tag a URL, you add a question mark to the
end of the URL, followed by your tag, as shown in the slide.

The variables and values are listed as pairs separated by an equals sign. Each variable-value pair is
separated by an ampersand.

Let’s look at each variable.

You should use utm_source to identify the specific website or publication that is sending the traffic.

Use utm_medium to identify the kind of advertising medium -- for example, cpc for cost per click, or
email for an email newsletter.

Use utm_campaign to identify the name of the campaign -- for example, this could be the product
name or it might be a slogan.

You should always use these three variables when tagging a link. You can use them in any order you
want.

If you're tagging paid CPC campaigns, you should also use utm_term to specify the keyword.

And, you can differentiate versions of a link -- for example, if you have two call-to-action links within
the same email message, you can use utm_content to differentiate them so that you can tell which
version is most effective.


To illustrate, let’s look at a two versions of a link to mysite.com, both placed on yoursite.com.

The first link in the slide does not have a tag. Traffic from this link will show up in your reports as a
referral from yoursite.com. There won’t be any campaign information.

The second link has a tag. Traffic from this link will show up with a source of yoursite, and it will show
as a banner, instead of a referral.

Also, you’ll see this traffic reflected under summerpromo in your Campaigns report.


Let’s look at a destination URL from an AdWords ad.

In the first example, no tag has been provided and autotagging is disabled. In this case, you won’t see
this traffic in your AdWords reports.

The second example shows how to manually tag an AdWords link. This traffic will show up in your
AdWords reports, but information will be limited to campaign and keyword.

You must specify cpc as your medium and google as your source in order to see this traffic in your
AdWords reports. You should also specify cpc as your medium when tagging paid search campaigns
from other search engines.

The third example shows what an AdWords autotagged URL might look like once AdWords has
appended the g-c-l-i-d variable to the end of the URL.

This traffic will show up in your AdWords reports and you’ll see complete AdWords information.

You can select any of these variables as a dimension in most reports.

For example, to see all of the sources in California from which you received traffic, you could go to
the Map Overlay report, drill down to California, and select Source as a dimension.


You can use the URL Builder in the Google Analytics Help Center to construct your URLs.

You enter in the destination URL and the values for each campaign variable. You should always use
source, medium and campaign name.

The URL Builder can be found via the link displayed here on the slide, or you can search for “URL
Builder” in the Analytics Help Center.

The URL builder can only construct one URL at a time, so you probably won’t want to use it to
construct every URL for every campaign.

If you have a large number of URLs to tag, you can use spreadsheets to automate the process.


Generate a sample URL in the URL Builder and create a simple spreadsheet formula.

Spreadsheets can make it much easier to generate thousands of tagged URLs.

Stick to these best practices when tagging your advertising campaigns.

If you use AdWords, be sure to enable auto-tagging. Otherwise, you’ll miss out on important
information that can help you optimize your AdWords campaigns.

Second, for each campaign, use the URL Builder to create a template URL. Then, copy and paste
from the template to create the rest of the URLs for the campaign.

Third, use consistent names and spellings for all your campaign values so that they are recorded
consistently within your Analytics reports

Finally, use only the campaign variables you need. You should always use source, medium, and
campaign name, but term and content are optional.


3.3. Analysis Focus – AdWords

 how to use the Clicks tab metrics in AdWords reports
 how to analyze the effect of search result position on performance
 how to track audio and TV campaigns in AdWords
 how to track ad performance

YouTube Video: http://goo.gl/xNfwu

3.4. Goals in Google Analytics

 the purpose of using goals, goal values, and goal funnels
 the different kinds of goals
 when to use each Goal URL Match Type
 how to assign meaningful values to goals
 how goal conversions differ from transactions

Defining site goals and tracking goal conversions is one of the best ways to assess how well your site
meets its business objectives. You should always try to define at least one goal for a website.

So what is a goal? In Google Analytics, a goal represents an activity or a level of interaction with your
website that’s important to the success of your business.

Some examples of goals are an account signup, a request for a sales call, or even that the visitor
spent a certain amount of time on the website.


There are four types of goals in Google Analytics.

A URL Destination goal is a page that visitors see once they have completed an activity. For an
account sign-up, this might be the “Thank You for signing up” page. For a purchase, this might be the
receipt page. A URL Destination goal triggers a conversion when a visitor views the page you've
specified.

A Time on Site goal is a time threshold that you define. When a visitor spends more or less time on
your site than the threshold you specify, a conversion is triggered.

A Pages per Visit goal allows you to define a pages viewed threshold. When a visitor views more
pages --or fewer pages --than the threshold you've set, a conversion is triggered.

An Event goal allows you to attach a conversion to an event that you have defined. We’ll learn about
events in a subsequent lesson.


You can see total conversions and conversion rates for each of your goals in your reports.

For each URL Destination goal that you define, you can also define a funnel. A funnel is the set of
steps, or pages, that you expect visitors to visit on their way to complete the conversion.

A sales checkout process is a good example of a funnel. And the page where the visitor enters credit
card information is an example of one of the funnel steps.

So, the goal page signals the end of the activity -- such as a “thank you” or “confirmation” page -- and
the funnel steps are the pages that visitors encounter on their way to the goal.


Defining a funnel is valuable because it allows you to see where visitors enter and exit the conversion
process.

For example, if you notice that many of your visitors never go further than the “Enter shipping
information” page, you might focus on redesigning that page so that it’s simpler.

Knowing which steps in the process lose would-be customers allows you to eliminate bottlenecks and
create a more efficient conversion path.

To set up a goal, first go the Account Administration page. Click the account and web property for
which you want to configure a goal.


Select the profile to which you want to add the goal. Then, click the goals tab and click the plus-Goal
link in one of the Goal sets. You can create up to 4 sets of 5 goals each.


To define a URL Destination Goal, select URL Destination as the goal type. Next, enter the URL of the
goal page. You don’t have to enter the entire URL. You can simply enter the request URI - that’s what
comes after the domain or hostname.

So, if the complete URL is www.googlestore.com/confirmation.php, you only need to enter
/confirmation.php.

Make sure that the URL you enter corresponds to a page that the visitor will only see once they
complete the conversion activity. So, pick something like the Thank You page or a confirmation page
for your goal.


You can also enter a name for the Goal -- here we’ve entered “Completed Order”. This name will
appear in your conversion reports.


Defining a funnel is optional. To define your funnel steps, you add the URLs of the pages leading up
to the goal URL. Just as with goals, you don’t have to enter the entire URL of a funnel step -- just the
request URI is fine.

Provide a name for each step in the funnel -- here we’ve entered “Select gift card “ for Step 1. The
names you enter will appear in your reports.


Next, we’ll talk about the Match Type setting.


The match type defines how Google Analytics identifies a goal or funnel step. You have three choices
for the Match Type option.

“Head Match” is the default. It indicates that the URL of the page visited must match what you enter
for the Goal URL, but if there is any additional data at the end of their URL then the goal will still be
counted. For example, some websites append a product ID or a visitor ID or some other parameter to
the end of the URL. Head Match will ignore these.

Here’s another example, illustrated on this slide: If you want every page in a subdirectory to be
counted as a goal, then you could enter the subdirectory as the goal and select Head Match.

“Exact Match” means that the URL of the page visited must exactly match what you enter for the
Goal URL. In contrast to Head Match, which can be used to match every page in a subdirectory, Exact
Match can only be used to match one single page. Also notice that Exact Match does not match the
second pageview, “/offer1/signup.html?query=hats” because of the extra query parameter at the
end.

“Regular Expression Match” gives you the most flexibility. For example, if you want to count any sign-
up page as a goal, and sign-up pages can occur in various subdirectories, you can create a regular
expression that will match any sign-up page in any subdirectory. Regular Expressions will be covered
in a later module.

When you use Regular Expression Match, the value you enter as the goal URL as well as each of the
funnel steps will be read as a Regular Expression.

Remember that regardless of which option you choose, Google Analytics is only matching Request
URIs. In other words, the domain name is ignored.

Check “Case Sensitive” if you want the URLs you entered into your goal and funnel to exactly match
the capitalization of visited URLs.


To define a Time on Site goal, select Time on Site as the goal type. Next, select "Greater than" or
"Less than" and enter an amount of time, for example 15 minutes. We'll discuss goal value shortly.

To define a Pages per Visit goal, select Pages per Visit as the goal type. Next, select "Greater than",
"Equal to", or "Less than" and enter a number of pages.

Threshold goals are useful for measuring site engagement, whereas URL Destination goals are best
for measuring how frequently a specific activity has been completed. If your objective is for visitors
to view as much content as possible, you might set a Pages per Visit goal. Or, if you have a customer
support site and your objective is for visitors to get the information they need in as short a time as
possible, you might set a Time on Site goal with a "Less than" condition.


The “Goal Value” field allows you to specify a monetary value for goal. You should only do this for
non-ecommerce goals.

By setting a goal value, you make it possible for Google Analytics to calculate metrics like average
per-visit-value and ROI. These metrics will help you measure the monetary value of a non-
ecommerce site.

Just think about how much each goal conversion is worth to your business. So, for example, if your
sales team can close sales on 10% of the people who request to be contacted via your site, and your
average transaction is $500, you might assign $50 or 10% of $500 to your "Contact Me" goal.

Again, to avoid inflating revenue results, you should only provide values for non-ecommerce goals.


There is an important difference between goal conversions and e-commerce transactions. A goal
conversion can only happen once during a visit, but an e-commerce transaction can occur multiple
times during a visit.

Let’s say that you set one of your goals to be a PDF download and you define it such that any PDF
download is a valid goal conversion. And let’s say that the goal is worth $5.

In this case, if a visitor comes to your site and downloads 5 PDF files during a single session, you’ll
only get one conversion worth $5. However, if you were to track each of these downloads as a $5 e-
commerce transaction, you would see 5 transactions and $25 in e-commerce revenue.

You’ll learn how to set up ecommerce tracking and how to track PDF downloads in later modules.

If you are using a filter that manipulates the Request URI, make sure that your URL Destination goal is
defined so that it reflects the changed Request URI field. For example, in the slide, we have a profile
that defines /thankyou.html as a URL Destination goal. But we have another profile with a filter that
appends the hostname to the Request URI. So, for this profile, we need to change the goal definition
accordingly.


If you define a funnel for a goal, Google Analytics populates the Funnel Visualization report, shown
here in the slide.

On the left, you can see how visitors enter your funnel. On the right, you can see where they leave
the funnel and where they go.

The middle shows you how visitors progress through the funnel -- how many of them continue on to
each step.

In this example, we can see that there were 9,283 entrances at the top of the funnel and 187
completed orders, at the bottom of the funnel.

This report is very useful for identifying the pages from which visitors abandon your conversion
funnel.


Here’s another report in the Goals section. It’s the Reverse Goal Path report. You can see this data
even if you haven’t defined a funnel. It lists the navigation paths that visitors took to arrive at a goal
page and shows you the number of conversions that resulted from each path.

In this example, we can see that 97 of the conversions resulted from the first navigation path that’s
shown.

This is a great report for identifying funnels that you hadn’t considered before and it can give you
great ideas for designing a more effective site.


3.5. Filters in Google Analytics

 when to apply filters in Google Analytics
 how filters act on data
 how to create custom filters
 the differences between the different kinds of filters (i.e. exclude, include, etc)
 how to filter Google AdWords traffic
 how to use filters and profiles together to track certain kinds of traffic
 best practices for using filters

Google Analytics filters provide you with an extremely flexible way of defining what data is included
in your reports and how it appears.

You can use them to customize your reports so that data that you deem useful is highlighted in
interesting ways. Filters can also help you clean up your data so that it is easier to read.

There are two types of filters in Google Analytics – predefined filters and custom filters.


Filters process your raw traffic data based on the filter specifications. The filtered data is then sent to
the respective profile.

Once data has been passed through a filter, Google cannot re-process the raw data.

That’s why we always recommend that you maintain one unfiltered profile so that you always have
access to all of your data.


To set up a goal, first go the Account Administration page. Click your desired account.

You can use the Filters tab to create new filters, edit their settings, and apply them to profiles.

To create a new filter you will need to complete several fields, including the filter name and type.

If you elect to create a custom filter, you will need to complete several additional fields.


Google Analytics provides three commonly used predefined filters.

The first filter called “Exclude traffic from domains” excludes traffic from the domain that you specify
in the Domainfield. If you apply this filter, Google Analytics will apply a reverse lookup with each
visitor’s IP address to determine if the visitor is coming in from a domain that should be filtered out.
Domains usually represent the ISP of your visitor although larger companies generally have their IP
addresses mapped to their domain name.

The second filter, “Exclude traffic from IP addresses”, removes traffic from addresses entered into
the IP address field. This filter is generally used to exclude your internal company traffic.

The third filter, “Include traffic subdirectories”, causes your profile to only report traffic to a specified
directory on your site. This is typically used on a profile that is created to track one part of a website.


As a best practice, we recommend that you create a filter to exclude your internal company traffic
from your reports.

To do this you can use the predefined filter “Exclude traffic from IP addresses”. You will need to
enter your IP address or range of addresses into the ‘IP address” field.


In addition to the pre-defined filters that Analytics offers, you can also create custom filters.

Custom filters offer you greater control over what data appears in your profiles.

To create a custom filter, select “Custom filter”. Additional fields will appear when you choose this
option.


Each custom filter has three main parts.

The first part of a custom filter is “Filter Types”. There are six filter types available and each one
serves a specific purpose. We’ll look at these in a minute.

The second part is the “Filter Field”. There are numerous fields you can use to create your filter.
Examples of some commonly used fields are the “Request URI” and “Visitor Country” fields.

The complete list of fields can be found through the link shown here or you can search for “filter
fields” in the Analytics Help Center.

The third part of a custom filter is the “Filter Pattern”. This is the text string that is used to attempt
to match pageview data. The pattern that you provide is applied to the field and, if it matches any
part of the field, it returns a positive result and causes an action to occur. You’ll need to use POSIX
Regular Expressions to create the filter pattern. Learn more in the module on Regular Expressions.

Here’s a chart that describes the filter types.

Exclude and Include filters are the most common types. They allow you to segment your data in
many different ways. They’re frequently used to filter out or filter in traffic from a particular state or
country.

Lowercase and Uppercase filters do not require a filter pattern, only a filter field. Lowercase and
Uppercase filters are very useful for consolidating line items in a report. Let’s say, for example, that
you see multiple entries in your reports for a keyword or a URL, and the only difference between the
multiple entries is that sometimes the URL or keyword appears with a different combination of
uppercase and lowercase letters. You can use the Lowercase and Uppercase filters to consolidate
these multiple entries into a single entry.

Search and Replace filters replace one piece of data with another. They are often used to replace
long URL strings with a shorter string that is easier to read and identify in your reports.


You can use Advanced filters to remove unnecessary data, replace one field with another, or
combine elements from multiple filter fields. For example, a best practice when tracking multiple
subdomains in a single profile is to append the subdomain name to the page names. You can do this
by creating an advanced filter that appends Hostname to Request URI.

Let's look at an example of a Search and Replace filter.

Here’s an example of how you might use a Search and Replace filter.

Let’s say that your website uses category IDs as an organizational structure. So, in your Pages report,
you’d see a list of Request URIs that indicate the different pages on your site.


The page “/category.asp?catid=5” is actually the Google Store Wearables page. You could make the
Pages report more meaningful by replacing “catid=5” with a descriptive word, like “Wearables”.

Here’s what the Search and Replace filter might look like. This particular filter would overwrite the
entire Request URI with “Wearables.”

This is a simplified example to give you an idea of how you can use filters.


USING FILTRES AND PROFILES

Once you’ve defined a filter, you can apply it to a single profile or across several profiles.

So, for example, in the slide, the graphic shows a single web property with two profiles.

Filter 1 has been applied to both profiles.

Filter 2 has been applied only to Profile 2.

By setting up multiple profiles and applying filters creatively to each of them, you have a great deal
of reporting and analysis flexibility.


You can also use profiles and filters together to create customized data views.

Let’s say that you want to have two different views of your data -- one view includes only traffic to a
subdomain and the other view only includes customers from a specific geographic region.

To do this, you’d set up Profile 2 and Profile 3 as shown here in the chart.

Or, for example, you might want to set up a profile that only inlcudes Google AdWords traffic. We’ll
look at how to do this in the next slide. Remember, you always want to maintain a profile that
contains all of your data. That’s Profile 1 in the chart.


To set up a profile that includes only Google AdWords traffic, you need to apply the two Custom
Include filters shown in the slide.

In filter one, you’ll filter on campaign source for a pattern of google.

In filter two, you’ll filter on campaign medium for a pattern of cpc.

You can apply these two filters in any order.


Let’s look at how you can use profiles and filters to track subdomains.

If your subdomains are totally separate businesses, and you have no need for reports that include
cumulative traffic to both, then you could simply create a unique web property for each subdomain.

Google Analytics creates a unique web property ID for each web property you set up.

The web property ID comprises the letters “U” “A”, followed by the account ID, followed by another
number that distinguishes the web property from other web properties in the account.

In the slide example, web property 1 is distinguished by a dash 1. Web property 2 is distinguished by
a dash 2.

So, you’d install the “dash 1” version of your tracking code on your Subdomain A pages, and the
“dash 2” version of your tracking code on your Subdomain B pages.

But what if you want to analyze the traffic aggregated across both subdomains? In this case, you
could set up 3 duplicate profiles under a single web property.

Then, you’d apply an Include filter to two of the profiles.

Profile 1 includes all traffic to both subdomains.

Profile 2 only includes traffic to subdomain A.

Profile 3 only includes traffic to subdomain B.

In this scenario, you’d install identical tracking code on every page of the site regardless of
subdomain.


When setting up profiles and filters for your Analytics account, you should always create one
unfiltered profile that can be a back-up in case your filters do not function as planned or you need
more data than you originally thought.

Remember, once your raw data has passed through filters, Google cannot go back and reprocess the
data. So, maintaining an unfiltered profile provides you with a backup.


You can apply multiple include and exclude filters to a single profile, but keep in mind that when
more than one filter is applied, the filters will be executed in the same order that they are listed in
your Profile Settings.

In other words, the output from one filter is then used as the input for the next filter.

The example shown here illustrates that if you want to include only users from California and Texas,
you cannot create two separate include filters because they will cancel each other out. The solution
is to create one filter that uses a regular expression to indicate that the Visitor Region should be
California or Texas.


FILTERING ADWORDS DATA

If you drive traffic from AdWords to multiple sites, each of which is tracked in a separate Analytics
profile, you’ll need to apply a filter to each site’s profile.

Because, when you apply cost data from an AdWords account, data from the entire account is
applied to each profile - Google Analytics doesn’t automatically match campaigns to specific profiles.

To illustrate what would happen if you don’t apply a filter, let’s imagine that you have two sites and
you spend $50 to drive traffic to each of them.

Without a filter, the Clicks tab on each profile would include $100 worth of cost data instead of just
the $50 you spent for that site. So, for each profile that should include a subset of your AdWords
data, you’ll need to create a custom include filter.


Create a custom filter and select the Include filter type.

For the filter field, select “Campaign Target URL”. This field only applies to Google AdWords data.

Use a regular expression to create the filter pattern based on the AdWords destination URL that is
applicable to this profile.

Once you’ve saved this filter, only AdWords data for this profile will be displayed in the reports.


3.6. Regex and Google Analytics

 when to use regular expressions in Google Analytics
 how to use the most common metacharacters: dot, backslash, etc.
 how often your data is updated and how Google stores it
 some examples of common regular expressions in Google Analytics

A regular expression is a set of characters and metacharacters that are used to match text in a
specified pattern.

You can use regular expressions to configure flexible goals and powerful filters.

For example, if you want to create a filter that filters out a range of IP addresses, you’ll need to enter
a string that describes the range of the IP addresses that you want excluded from your traffic.

Let’s start off by looking at each metacharacter. Metacharacters are characters that have special
meanings in regular expressions.


Use the dot as a wildcard to match any single character.

The operative word here is “single”, as the regex would NOT match Act 10, Scene 3. The dot only
allows one character, and the number ten contains two characters -- a 1 and a 0.

How would you write a regular expression that would match “Act 10, Scene 3”?

You could use two dots.

To make your regex more flexible, and match EITHER “Act 1, Scene 3” or “Act 10, Scene 3”, you could
use a quantifier like the + sign.

But we’ll talk about repetition a bit later in this module.


Backslashes allow you to use special characters, such as the dot, as though they were literal
characters.

Enter the backslash immediately before each metacharacter you would like to escape.

“U.S. Holiday” written this way with periods after the U and the S would match a number of
unintended strings, including UPS. Holiday, U.Sb Holiday, and U3Sg Holiday.

Remember that the dot is a special character that matches with any single character, so if you want
to treat a dot like a regular dot, you have to escape it with the backslash.

You’ll use backslashes a lot, because dots are used so frequently in precisely the strings you are
trying to match, like URLs and IP addresses.

For example, if you are creating a filter to exclude an IP address, remember to escape the dots.


Use square brackets to enclose all of the characters you want as match possibilities. So, in the slide,
you’re trying to match the string U.S. Holiday, regardless of whether the U and the S are capitalized.

However, the expression won’t match U.S. Holiday unless periods are used after both the U and the
S. The expression also requires that the H is capitalized.

There is a regex you can write to match all of these variations. The question mark used here is
another “quantifier”, like the ‘+’ sign mentioned earlier.

Again, we’ll talk about repetition in the next slide.

You can either individually list all the characters you want to match, as we did in the first example, or
you can specify a range.

Use a hyphen inside a character set to specify a range. So instead of typing square bracket 0 1 2 3 4 5
6 7 8 9, you can type square bracket 0 dash 9.

And, you can negate a match using a caret after the opening square bracket.

Typing square bracket caret zero dash nine will exclude all numbers from matching.

Note that later in this module, you will see the caret used a different way—as an anchor.

The use of the caret shown here is specific to character sets, and the negating behaviour occurs only
when the caret is used after the opening square bracket in a character set.


Now let’s talk about using quantifiers to indicate repetition.

In earlier examples, we’ve used the plus sign and the question mark.

The question mark requires either zero or one of the preceding character. In the expression “3-1-?” ,
the preceding character is a 1. So, both 3 and 3-1 would match.

The plus sign requires at least one of the preceding character. So, “3-1-+” wouldn’t match just a 3. It
would match 3-1, 3-1-1, and so on.

The asterisk requires zero or more of the preceding character. In the expression, “3-1-*”, the
preceding character is a 1. So it would match 3, 3-1-, 3-1-1, and so forth.

You can also SPECIFY repetition using a minimum and maximum number inside curly brackets.

Recall that a dot matches any single character. What would you use to match a wildcard of
indeterminate length?

Dot star will match a string of any size. Dot star is an easy way to say “match anything,” and is
commonly used in Google Analytics goals and filters.


It is handy to use the parentheses and the pipe symbol (also known as the OR symbol) together.

Basically, you can just list the strings you want to match, separating each string with a pipe symbol --
and enclosing the whole list in parentheses.

Here, we’ve listed four variations of “US” that we’ll accept as a match for US Holiday.

If it’s not in the list, it won’t get matched. That’s why “US Holiday” won’t get matched if one of the
periods is missing.

In our list, we’ve accounted for both periods missing, but not for just one period missing.

Using question marks, the second regex in the slide will match all of the above.


The caret signals the beginning of an expression. In order to match, the string must BEGIN with what
the regex specifies..

The dollar sign says, if there are any more characters after the END of this string, then it’s not a
match.

So, caret US means start with US. US Holiday matches, but “Next Monday is a US Holiday” does not
match.

Holiday$ means end with Holiday. US Holiday still matches, but “US Holiday Schedule” does not
match.

Anchors can be useful when specifying an IP address. Take a look at these examples.

Some character classes are used so commonly that there is a shorthand you can use instead of
writing out the ranges within square brackets.

Let’s look at the example of a simplified regex that could match an addres:

Backslash d means match any one digit zero through nine.

Use curly brackets and a minimum and maximum number to specify how many digits to match.

Backslash d followed by 1 comma 5 in curly brackets means that the address must contain at least
one digit, and at most five digits.

Backslash s means that the number should be followed by one space, backslash w means match any
alphanumeric character and the star means include as many alphanumeric characters as you want.

“345 Embarcadero” matches, but just “Embarcadero” does not, because this regex requires the
string to start with a number.

If you want to make the number optional, group the first part of the regex with parentheses--
including the space--and follow it with the question mark.


Note that an address like “1600 Amphitheatre Parkway” would not match either, because the regex
does not account for the space between Amphitheatre and Parkway.

The slide shows one way you could account for this.

REGEX REVIEW

Let’s review.

In the example on the slide, we’ve created an expression that will match the strings Google or Yahoo,
regardless of whether or not Google and Yahoo are capitalized.


Here, we’ve created an expression that will match URLs for internet and theatrical movie trailers.

The first part of the expression indicates that the URL can begin with anything.

Then the expression specifies that the URL must end with index.php?dl=video/trailers/ and then
either internet or theatrical.

The $ sign ensures that any URLs that are any longer than this won’t get included in the match.


You’ll find lots of applications for regular expressions in Google Analytics.

Some common examples are:

• filtering out internal traffic by specifying a set of IP addresses

• setting up a goal that needs to match multiple URLs

• tracking equivalent pages in a funnel

• and using the filter box that appears on your reports to find specific entries in a table.


Here’s an example of a custom filter that uses a very simple regular expression.

Here’s a regular expression used to define a goal URL.


Here’s how you might use regular expressions to group pages or funnel steps on your site.

Using a regular expression allows you to track them as one funnel step rather than tracking each
page or action individually.

Learn how goals and funnels work in the module on goals.

And, here’s an example of using regular expressions within your reports.

We’re using the Search filter to display all the rows in the table that contain Google or Yahoo.


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