Google Analytics IQ Lessons Notes (2012) New Version 5 – Part 2

Part 2 of the Notes from the Google Analytics IQ Lessons. Remember, you will need to also watch the presentations for visual clarity. The notes on their own won’t be adequate

•Account Administration


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

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.


A profile is a set of rules that defines the data you see for a web property. For example, you might have web property for which you have three profiles.
One of the profiles might show all the data for all the traffic that comes to
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 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


Here is the Profiles tab for the “ 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.

Campaign Tracking and AdWords Integration


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.


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 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.


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.


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.


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, both placed on

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 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.

•Analysis Focus – AdWords

– Site Usuage Metrics
– Goal conversions
– Eccomerce Activity
– Revenue Metrics

Visits = # of visits received from Adwords keyword campaigns
Impressions = # of times ad shown
clicks = # clicks from which you paid or received
CTR – click thru rate – how many times your ads were displayed
(impressions, clicks, cost, CTR)

Revenue per click / return on investment & margin can help you access keywrod profitability
set match types to compare diffetrent types of data

Effective time of day?
Day Parts
Visits vs Transactions then view the data hourly



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, 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.



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.


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.


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.




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.


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.


/downloads/casestudy/ .*


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.



Google Analytics provides a tool that makes it easier to generate a regular expression that matches a range of IP addresses.

It’s called the Regular Expression Generator and you can find it at the URL shown in the slide.

Or, you can search for Regular Expression Generator in the Google Analytics Help Center.


You’ll find a number of useful applications for regex as you use Google Analytics.

But, it’s important that you think through all the implications of each expression that you use when you set up a filter or a goal.

It’s easy to make a mistake and not get the data or the result you’re looking for.

Set up a duplicate profile to test your regex statements. After enough data has been collected, check your results and make sure they’re what you expect.

Remember to always maintain a backup profile that includes all your data.

There are lots of regex resources on the web. To get started, just search for regex



Some web sites store information about you or your computer in a small file called a cookie. The cookie is stored on your hard drive.

Sites that run Google Analytics issue first party cookies that allow the site to uniquely, but anonymously, identify individual visitors.

So, when a visitor returns to a site that runs Google Analytics, the site is able to remember that the visitor has been to the site before and Google Analytics will only count that visitor once in unique visitor calculations.

There are two types of cookies. First-party cookies are set by the domain being visited. Only the web site that created a first-party cookie can read it. This is the kind of cookie used for Google Analytics tracking.

Third-party cookies are set by third party sites — basically sites other than the site being visited.

Users can choose whether to allow some, none, or all types of cookies to be set on their computers.

However, if a user does not allow cookies at all, they may not be able to view some Web sites or take advantage of customization features.


Cookies can be set with or without an expiration date. This detail is important in order to understand how Google Analytics tracks visits and unique visitors.

Persistent cookies have an expiration date, and remain on your computer even when you close your browser or shut down. On return visits, persistent cookies can be read by the web site that created them.

Temporary cookies do not have an expiration date, as they are only stored for the duration of your current browser session. As soon as you quit your browser, temporary cookies are destroyed.


While it’s impossible to determine the exact number of web visitors who have cookies enabled or disabled, available statistics suggest that the vast majority of visitors enable cookies.
Many kinds of sites require that visitors have cookies enabled.

For example, you need to have cookies enabled in order to login to many online shopping carts and to use web mail.

First party cookies, which are the kind used for Google Analytics, are allowed by a majority of visitors.

Cookie tracking makes it possible to correlate shopping cart transactions with search campaign information, and perform other visitor analysis.

Remember — websites only have access to the information that you provide. Websites can’t get your email address or access to any information on your computer unless you provide it. And since Google Analytics only uses first party cookies, Google Analytics cookies can only be read by the website that created them.


Google Analytics sets the five first-party cookies shown in the slide.

The __utmv cookie is optional, and will only be set if the _setVar() method is called. You will learn about _setVar() in the module on Custom Visitor Segmentation.

All of the Google Analytics cookies are persistent except for one. The __utmc cookie is a temporary cookie that is destroyed when the visitor quits the browser.

Each of the other Google Analytics cookies has an expiration date set in the future, meaning that the cookie will persist on the user’s computer until it expires, or until the user deletes it from their computer.


Here’s an example of the cookies set by the Google Store. You can see that __utma, __utmb, __utmc, and __utmz have been set. We’ll learn more about each cookie shortly.

First, let’s try a brief experiment. Which of the sites that you’ve visited are using Google Analytics?

To find out, open your browser’s cookie window. You’ll usually find it under your browser’s “Options” or “Preferences”.

Now, in the cookies window, search for underscore underscore u-t-m. You should see all the different Google Analytics cookies set by all the sites that you’ve visited that use Google Analytics.

All cookies are browser-specific. So, if you’ve already been to a site, but you open a different browser to visit that site again, another set of Google Analytics cookies will be set.

Now, before we continue, search for the Google Store cookies by typing the domain name “” into the Cookies search box.

If you’ve never visited the Google Store, go to now so that cookies are created.


Select the Google Store __utma cookie. In the cookie information, note the “Content” and expiration date for the cookie.

The first number in the content of every Google Analytics cookie is called the “domain hash.” It represents the domain that you visited and that set these cookies. Google Analytics applies an algorithm to the domain and outputs a unique numeric code that represents the domain. Each Google Analytics cookie set by the domain will begin with this number.

The next number is a random unique ID.

The three subsequent numbers are timestamps. They represent the time of the initial visit, the beginning of your previous session, and the beginning of your current session. The timestamps represent the number of seconds since January 1, 1970.

Notice that the last three timestamps are the same. What does this tell you?

The last number, the session counter, can give you the answer. The last number tells you the number of times you have visited this site. This number will increment each time you visit the site. The session counter here is “1”, and the last three timestamps are all the same because this is your first visit to the site.

The random unique ID combined with the first timestamp make up the visitor ID that Google Analytics uses to identify unique visitors to the site. These details allow Google Analytics to calculate the number of unique visitors and number of visits.

Look at your Google Store __utma cookie.

How many times have you visited the Google Store? If you think you’ve visited more times than is indicated by the cookie, remember that the cookie only includes the number of times you visited from this computer using this browser.

Also, if you have cleared your cookies at some point, it is only counting from the last time you cleared your cookies.

When does this cookie expire? You should see that the date is two years from last the time you visited.

_utmb & _utmc – Session IDENTIFIERS

The __utmb and __utmc cookies together identify a session.

The content of the __utmc cookie is simply the domain hash.

The content of the __utmb cookie will also be the domain hash plus, if the site is using ga.js, some additional values.

The key difference between the two cookies is that __utmb is a persistent cookie with an expiration date that is set 30 minutes after it is created. While __utmc is a temporary cookie that is destroyed as soon as the visitor quits the browser.

Let’s review what you know about a session, or visit, in Google Analytics. First note that the terms “session” and “visit” are used interchangeably. A session is defined by 30 minutes of inactivity or if a visitor quits the browser.

Each time the visitor navigates to a new page and the JavaScript in the Google Analytics Tracking Code is executed, the __utmb cookie is refreshed and set to expire in 30 minutes.
This is how a session can be 2 hours long. As long as the visitor remains active on the site, the session remains active.

But if the visitor stays on a page for more than 30 minutes, the __utmb cookie will be destroyed. The next time the visitor loads a page, Google Analytics won’t find a__utmb cookie. Instead, a new __utmb cookie is created and, from the standpoint of tracking, this is a new session.

So, why is the __utmc cookie needed? Let’s say a visitor quits and starts the browser and comes back right away to the same site. Since the __utmc cookie was destroyed, Google Analytics will know that this is a new session.

So, to summarize, when the visitor loads a page, the JavaScript in the Google Analytics Tracking Code checks for both the __utmb and __utmc cookies. If either one is missing, it notes this as a new session, and creates whichever cookie– __utmb, __utmc, or both– was missing.

Note that it is possible to adjust this behavior. With a small customization to the Google Analytics Tracking code, you can make the session timeout length anything you want. You’ll learn about this in the Code Customizations module.


The __utmz cookie stores the campaign tracking values that are passed via tagged campaign URLs.

So, for example, if a visitor comes to your site on a link tagged with campaign variables utm_source, utm_medium, and utm_campaign, the values for these variables will be stored in the __utmz cookie.

Preceding the campaign tracking values, you will see four numbers stored in the __utmz cookie.
The first number is the domain hash, as with the other Google Analytics cookies.
The second number is a timestamp.
The third and fourth numbers are the “session number” and “campaign number”, respectively.
The “session number” increments for every session during which the campaign cookie gets overwritten.
The “campaign number” increments every time you arrive at the site via a different campaign or organic search, even if it is within the same session.

The __utmz cookie has a six month timeout, meaning that a visit will be attributed to a particular campaign for up to six months, or until the __utmz cookie is overwritten with another value.

You can modify the six month timeout and you can change the rules which govern when the __utmz cookie value is overwritten. You’ll learn how in the Code Customizations module.

The __utmz data shown here would show up in your All Traffic Sources report as coming from the source / medium “google / organic”.

Now, in your browser’s cookie window, select the __utmz cookie from your visit to Assuming that it was a direct visit, you’ll see “utmcsr=(direct)” and “utmcmd=(none)”. Your visit will show up in the Google Store’s Google Analytic’s account as coming from the source / medium “direct / none”.


The slide shows how the values in the __utmz cookie map to campaign variables.

For example, the utmcsr value in the __utmz cookie is the source, or the value that was assigned to utm_source in the tagged link.

utmcsr in __utmz is the Source (utm_source)
utmccn in __utmz is the Campaign (utm_campaign)
utmcmd in __utmz is the Medium (utm_medium)
utmctr in __utmz is the Keyword (utm_term)
utmcct in __utmz is the Ad Content (utm_content)


So, if you reached “” via a tagged URL that looks like this, then the __utmz cookie would look like this.

If the URL looks like this…..


…then the cookie will look like this:



The __utmv cookie is for custom visitor segmentation. You’ll only see this cookie if the site calls the _setVar() method. This cookie contains the domain hash, and one other value: the value you assign using _setVar().

For example, suppose all site visitors who log in get set to “Member”, while those who do not log in remain unassigned. The Google Analytics account owner would then be able to compare “Members” to those who are “(not set)” and see whether, for example, Members convert more often or spend more money on the site.

The __utmv is a persistent cookie that expires after 2 years.

Try searching your browser cookies for “utmv”. Any sites that appear will be those that use the Google Analytics custom segmentation feature.

Refer to the module on Custom Visitor Segmentation to learn more about _setVar() and the __utmv cookie.



If your site sells products or services online, you can use Google Analytics e-commerce reporting to track sales activity and performance.

The Ecommerce reports show you your site’s transactions, revenue, and many other commerce-related metrics.


Some examples of the kind of information you can get from the e-commerce reports include:
– the products that were purchased from your online store
– a list of transactions, and
– the number of times people visited your site before purchasing


E-commerce metrics are also available on the Ecommerce tab which appears in many reports.

For example, on the Ecommerce tab of the AdWords Campaigns report, you can see how much revenue is associated with your AdWords campaigns.


In order to use e-commerce reporting, you’ll need to do three things.

FIRST, enable e-commerce reporting within your Analytics website profile.

SECOND, add or make sure that you’ve added the Google Analytics Tracking Code to your receipt page or “Transaction Complete” page.

FINALLY, you’ll need to add some additional e-commerce tracking code to your receipt page so that you can capture the details of each transaction.

Let’s take a look at each step.


Step 1 is simply to enable the E-commerce selection for the profile.

Click the Account Administration icon. Navigate to the desired account and web property.
Select the desired profile and click the Profile Settings tab.

You’ll then see the screen shown here.

Select “Yes” next to E-commerce Website and save your changes.


For Step 2, add the Google Analytics Tracking Code to your receipt page. In Step 3, you’ll be adding some ecommerce tracking code to the basic tracking code.


Here’s an example of what the ecommerce tracking code on your receipt page might look like. Remember, you’ll be sandwiching this code into the basic Google Analytics Tracking Code.

In the first part of the code, there is a call to the _addTrans() method. The call to _addTrans() tells Google Analytics that a transaction has occurred.

The arguments to _addTrans() provide details about the transaction — for example an Order ID, the total order amount, and the amount of tax charged.

After the call to _addTrans(), there must be at least one call to the _addItem() method. This call provides Google Analytics with details about the specific item purchased.

Finally, there is a call to the trackTrans() method which sends all the data to Google Analytics.

Let’s look at each method in more detail.


The _addTrans() method establishes a transaction and takes the arguments shown here.

Your code will need to dynamically retrieve the values from your merchant software to populate these fields.

You can type single-quote single-quote to leave an optional field blank, but note that Order ID and Total are required.


For each item that a visitor purchases, call _addItem(). If more than one item is purchased, you’ll call _addItem() multiple times.

As with _addTrans(), you can leave some of the fields blank, but note that Order ID, SKU or Code, Price and Quantity are required arguments.

Use the same Order ID that you used in the call to addTrans().

If you’re not sure how to write this code, contact your merchant software provider.


Finally, after the calls to _addTrans() and _addItem(), you’ll need to call _trackTrans() to send the transaction information to Google Analytics.

Remember that all of the e-commerce code must appear after the Google Analytics Tracking Code calls _trackPageview().


Generally, you’ll be placing ecommerce tracking code on a secure shopping cart page.

The standard Google Analytics Tracking Code automatically detects when an https protocol is being used.

So you won’t need to add any special tracking code for secure pages.


For many e-commerce websites, the checkout process occurs on a separate domain or subdomain.

For example, if you send customers from to, you’re sending them to a subdomain.

If either of these scenarios applies to your site, you’ll need to add some code to some of your pages so that you can track activity across domains and subdomains.

The specific methods you’ll use are listed on the slide and you can learn how to use them in the module on tracking domains and subdomains.



So far in this course, we’ve focused on tracking within a single domain. Before we learn how to track across multiple domains, let’s understand why we might want to do this.

A domain is a hostname that represents a numeric IP address on the internet. It allows us to easily identify a website by a name instead of having to use a long string of numbers.

For example, and are both domains owned by Google.


You may sometimes need to track activity across multiple domains.

A common example of this is when you send visitors from your site to a separate shopping cart site to complete their purchases

However, since Google Analytics uses exclusively first party cookies, it can’t automatically track whether those visitors actually complete a purchase or not, because the purchase is taking place on another site.

Phrased more generally, if a session spans multiple domains, it would not be possible to track the session as a single visit attributed to one visitor. So, you’ll need a way of sharing the cookie information between the two domains.

THE _link() METHOD

By calling the _link() method, you can send this cookie information across domains.

This allows Google Analytics to track a user across multiple domains by sending cookies via URL parameters.


To track across domains, you’ll need to follow two steps.

First, add a few lines to the Google Analytics Tracking Code on all pages of each site. The lines you need to add are shown here, in blue.

Call _setDomainName() with an argument of “none”.

And call _setAllowLinker() with an argument of “true”


The second step involves the _link() method. Use this method in all links between domains.

In this example, we’re updating all links from to and vice versa. We update each link to call the _link() method as shown here.

Now, when a user clicks on a link that takes them to the other domain, the session information is preserved and the user is identified as being the same visitor across both domains.

FORMS AND _linkByPost() Method

If you use a form to transfer your visitors from one domain to another, you will need to use the _linkByPost() method instead of the _link() method.

This situation occurs most often with third party shopping carts.

To use forms to transfer from one domain to another, you must modify all the appropriate forms with the code shown here.

The _linkByPost() method will change the form action by adding query-string parameters to the value in the action attribute when the visitor submits the form.


You may also sometimes need to track across multiple subdomains. A subdomain is part of a larger domain and frequently each subdomain contains the pages for a specific department or offering.

For example, has several subdomains such as,, and

Since Google Analytics uses first-party cookies, cookies set on a subdomain can not automatically be read on the main domain, and vice versa.

As with multiple domains, you need to explicitly share the cookie information between subdomains or you’ll lose session information. If you don’t share cookie information between your subdomains, it may appear as though your own site is a referrer since only one domain is recognized as the main domain.


To track across multiple subdomains, call _setDomainName() and specify your parent domain name as the argument. This will allow the Google Analytics Tracking Code to use the same cookies across the subdomains.

For example, to track across Google’s various subdomains, you would call _setDomainName() with an argument of “dot google dot com” .

A side effect of using this method is that your reports may not differentiate between visits to identically named pages within the various subdomains.

So, for example, visits to and would be interpreted as visits to a single page. To correct this, you’ll need to set up an advanced filter. We’ll explain this in a minute.


There are a few best practices for setting up your Analytics account to track across multiple subdomains.

First, create separate profiles for each subdomain. This way, you’ll be able to see reports for each subdomain.

Set up duplicate profiles – one master profile, plus one profile for each subdomain. In this example, we’re looking at two subdomains.
Your master profile has no filters, and each of the other two has an Include filter.
Profile 1 includes all traffic to both subdomains.
Profile 2 includes only traffic to subdomain A.
Profile 3 includes only traffic to subdomain B..



Second, if you track across several subdomains within one profile, your reports may not differentiate between visits to identically named pages within the various subdomains.

This is because the reports only show the Request URI — which, in this example, is /home.html.

The hostname — — is stored in the Hostname data field in Google Analytics.

So, once you’ve called _setDomainName() to set your primary domain name, visits to and would be interpreted as the same page–”/home.html”.

To correct this, you can set up an advanced filter to include the subdomain in your reports. Set up your filter as shown in the slide.

Note that the constructor must match exactly what is shown in the slide, starting with the forward slash.

The filter works by appending the Hostname to the Request URI. As a result, you’ll be able to distinguish between identically named pages on your subdomains.


Second, if you track across several subdomains within one profile, your reports may not differentiate between visits to identically named pages within the various subdomains.

This is because the reports only show the Request URI — which, in this example, is /home.html.

The hostname — — is stored in the Hostname data field in Google Analytics.

So, once you’ve called _setDomainName() to set your primary domain name, visits to and would be interpreted as the same page–”/home.html”.

To correct this, you can set up an advanced filter to include the subdomain in your reports. Set up your filter as shown in the slide.

Note that the constructor must match exactly what is shown in the slide, starting with the forward slash.

The filter works by appending the Hostname to the Request URI. As a result, you’ll be able to distinguish between identically named pages on your subdomains.


If you want to track across both multiple domains and subdomains, you’ll need to ensure that the Analytics cookies are set across the subdomains and that the cookies are being passed between the parent domains.

There are two steps.
For the first step, add the lines of code shown in blue to Google Analytics Tracking Code on every page of of one of Domain 1 and each of its subdomains.
Make sure that _setAllowLinker() has an argument of true and _setAllowHash() has an argument of false.

Then, to each page of Domain 2 and each of its subdomains, add the same code — but with a different argument to _setDomainName().


For step 2, call _link() or _linkByPost() in all links and forms that cross between the two parent domains.

For example, the code shown in the slide shows how you’d do this to track across and

Note that you don’t need to use _link() or _linkByPost() in links between subdomains within the same domain.

Again, you should create separate profiles in your account for each primary domain and/or each subdomain.

You can easily do this by using an Include filter based on the hostname field.



With Advanced Segments, you can quickly isolate and analyze subsets of your traffic.
You can create an advanced segment that only includes visits that meet a specific set of criteria.

So, for example you can create an advanced segment that only includes visits from a certain geographic region or visits during which more than $100 was spent.


While it’s possible to create filtered profiles that segment traffic data, there are some differences between filtered profiles and advanced segments.

Advanced segments can be applied to historical data, but a filtered profile will only filter traffic going forward. When you create an advanced segment, that segment is available across all of your accounts and profiles. But, a filtered profile is only useful for a specific web property. You can compare up to four advanced segments side by side in your reports. In contrast, filtered profiles can only be viewed one at a time. It is much easier to create an advanced segment than it is to create a filtered profile.

If you want to permanently affect the data that a profile shows, you should use a filtered profile. So if you want a profile that only shows CPC data, you should set up a filtered profile to do this.

And if you want to restrict user access to only a subset of data, the best way to do this is to set up a filtered profile and restrict the users’ access to only that profile


To apply an advanced segment, simply Click Advanced Segments and select the segments you want.

The Default Segments are predefined, so you don’t have to do anything to use them except to select them

Once you’ve applied one or more advanced segments, you can see the data for the segments throughout all of your reports.

You can also change your date range and see the segments applied to historical data.
The segments remain applied until you deselect them or you logoff.


Let’s create an advanced segment that only includes visits during which more than $100 was spent.
Begin by clicking the Advanced Segments pulldown.

Next, click Create a new advanced segment.

Now you’ll see a screen that looks like this.
Using this screen, you can combine one or more logical statements to define a segment.

To include only visits during more than $100 was spent, first look for the metric Revenue.
It’s usually easiest to type what you are looking for into the search box, but you can also browse the complete list of metrics and dimensions.

Select the condition Greater than and specify 100.

Click Preview Segment and you can see the percentage of total visits that are included in the segment.

You can add as many conditions to the segment as you like. When you’ve finished, click Save Segment.

The segment will now appear in the Custom Segments area of the Advanced Segments pulldown.


– monitors your websites traffic
– sends alerts
automatic or custom
(automatic happen on there own, custom triggered on conditions you set)
– can signal spikes from specific sources
Set to LOW to see all the alerts GA created (sensitivity)
– can link to ADWORDS for auto alerts
– can be notified via text message or email right away


Google Analytics provides internal site search reports that allow you to see how people search once they’ve arrived at your site.


So why analyze how people search your site?

On both large and small sites, visitors frequently use search boxes as a form of navigation.

By looking at what people search for, you can identify missing or hidden content on your site, improve search results for key phrases, and even get ideas for new keywords to use in marketing campaigns.


In order to set up Site Search Tracking for your website, you’ll need to configure your Profile settings.

Click the account administration icon at the top right of any screen in Analytics.
Then navigate to the account, web property, and profile for which you want to enable Site Search reports.


In the Site Search Settings section, select the ‘Do Track Site Search’ radio button.

In the ‘Query Parameter’ field, enter the letter, word or words that designate an internal query parameter.

To find out what the query parameter is, perform a search on your site.

Normally when a user searches on your site, their query can be found in the URL.

For example, if you search on, you will see your search query preceded by ‘q=’. Therefore, Google’s query parameter would be ‘q.’

In the example above, the query parameter is ‘q,’ and the query was ‘Google Analytics’


What is the query parameter in this example?

Look at the URL that’s generated by your search. You should be able to find your query and the query parameter in the URL.

In this case, the search query was “creating a profile” and you can see that the query parameter is “query”.

Your parameter might be different — it could be the word “term” or “search”,

Or it might be just a letter, like “s” or ”p”.


If you have a particularly large site, some sections of your site may use different query parameters.

You may provide up to five parameters, separating each parameter by a comma.

Next, select whether or not you want Google Analytics to strip out the query parameter from your URL.

Stripping out the query parameter has the same effect as excluding URL Query Parameters under Profile Settings General Information.
So, if you choose to strip the query parameters, you don’t have to also exclude them from your main settings.

Note that Google Analytics will only strip out the query parameters you listed, and not any other parameters in the same URL.


If you use ‘Categories‘ on your site – such as the ability to use drop-down menus to narrow a search – you can
include categories in your search analytics.

First, select the “Site search categories” checkbox.

Then, enter your ‘Category Parameter’ in the field provided. Enter only the letters that designate an internal query category such as ‘cat, qc,’.

The same principle that you used to identify the query parameter can be used to identify the category parameter.
Or, you can contact your webmaster to identify the query and category parameters for your site.

Decide if you want to strip out the category parameters that you just provided. If you select the checkbox, only the parameters you provided will be stripped out.

As with the query parameter setting, this has the same effect as excluding URL Query Parameters in the General Information section.

So, if you choose to strip the category parameters here, you don’t have to exclude them again from your main settings.

Click ’Apply’ to finish.


To find the Site Search reports, click Site Search under Content.


The Site Search Usage report compares visitors who used site search to those who did not.
Here we can see that 19% of all visits to this site included a search.

Just above the pie-chart, you’ll notice two dropdown menus. if you select Goal Conversion Rate in the left-most dropdown, you can see how visits that included search compare to visits that did not include search with respect to conversions.

And, you can click the ecommerce tab to see how revenue and other ecommerce metrics differ for visits with and without site search.


The Search Terms report only includes visits during which a search was performed.

From the screenshot on the slide, you can see that there were 76, 331 total unique searches.
The search terms are listed in the table.

You can see how each term compares in terms of number of searches, percentage of search refinements, and other metrics.

Looking at the search terms that people use to search once they are on your site can give you ideas for keywords that might also help drive traffic to your site.

You can look at this traffic by another dimension. For example, if you wanted to see which cities these visitors came from, you could select City from the Dimension dropdown.


Start Pages lists all of the pages from which visitors searched.
To find Start Pages, click Pages under Site Search. Then, select “Start Page” as the viewing option above the table.

Click on a page in the table to learn more about the searches that occurred from that page. A detail report will appear which lists all of the search terms that were used from that page.

You can use this report to find out what visitors are searching for from your landing pages and you can use the information to improve the page content.

For example, if many visitors search on “shipping options” from your shopping cart page, you may want to display shipping information directly on the page.


Which pages are most commonly found through search on your site? Destination Pages tells you.
To find Destination Pages, click Pages under Site Search. Then, select “Destination Page” as the viewing option above the table.

The table shows popular destination pages.
Click on a page in the table to see the specific search terms that led to the page.


You can see which categories your visitors selected when searching your site.
Go to the Search Terms report and click “Site Search Category” as the viewing option.

This information helps you understand how visitors use your search engine, which product areas and categories are most popular, and how successfully visitors find what they are looking for in each category.


Why are goal conversions for Site Search reports?

– goal converisons in the Site Search reports are based on visits that include at least one search on your website

– Goal converisons in all other reports are based on all visits

Your Site Search reports will generally show a different number of conversions than what is shown in all of your other reports.

This is because goal conversions in the Site Search reports are based on visits that include at least one search on your website whereas the goal conversions shown in all other reports are based on all visits.

Because Site Search reports only include conversions from visits that included a search, you can see how effectively searches on your site drive conversions.

If you are confused about the difference between “search term” and “keyword”, it’s helpful to remember that Google Analytics reports use “search term” when referring to internal site searches and “keyword” when referring to external searches.




Many websites use technologies such as Flash and Ajax to interact with visitors.

For example, some websites embed video players, games, and other interactive experiences on site pages. However, the basic web analytics model of tracking pageviews doesn’t capture these kinds of interactions. This is because when a visitor interacts with a video player, for example, no pageview is generated.

Some other examples of interactions that don’t generate pageviews are Ajax-based activities, file downloads, and clicks on links that take the visitor to another site.

So how do you track these kinds of activities? There are two ways: virtual pageviews and Event Tracking

You can create a virtual pageview to represent practically any kind of activity or interaction you want.

You simply call _trackPageview() and provide any name you want as the argument.

It’s “virtual” because you’re telling Google Analytics to register a pageview even though no new page has actually been loaded.

You’ll see these virtual pageviews alongside ordinary pageviews in the Pages and Content Drilldown reports.


If you look at the Google Analytics Tracking Code, you’ll notice that it calls _trackPageview().

This lets Google Analytics know that the browser has loaded a page.

When you call _trackPageview(), however, you’ll want to provide an argument that specifies a virtual pagename for the event you’re tracking.


Here are some more examples.

In the first example, we’re tracking a download.

In the second example, we’re tracking a Flash event.

In each of these cases, we’re simply calling _trackPageview() to register a virtual pageview.


It’s a good idea to adopt a clear naming convention for your virtual pageviews. You might, for example, group virtual pageviews into categories by giving them a virtual subdirectory.

Also, since virtual pageviews appear along with standard pageviews in reports, you may wish to create a duplicate profile where you filter out the virtual pageviews.

To make this easy, you might organize all of your virtual pageviews into a “virtual” directory.


The other way to track non-pageview interactions is to use Event Tracking.

One advantage of using Event Tracking is that you won’t generate an extra pageview each time an interaction occurs.

Another advantage is that you can easily organize your events into categories, actions, and labels. And you can even provide values for each event you track.

All of your events show up in the Events reports within the Content section.

CALL _trackEvent() to register an event

Just call the _trackEvent() method each time you want to register an event. The slide shows the full specification of _trackEvent() — which you can also find documented on the Google Code site — and how you would actually call it, assuming that you are using Asynchronous Tracking.

We’ll discuss the arguments to _trackEvent() in a minute..


Here’s an example of how you’d call _trackEvent() from a Flash video player.

In this example, _trackEvent will get called each time the visitor releases the Play button on the video player.

_trackEvent will register an event with a category name of “Videos”, an Action name of “Play”, and a Label of “Movie Drama”.

Let’s look at each of these arguments.


Let’s look at each of the arguments to _trackEvent.

The strings that you provide for the first 3 arguments, Category, Action, and Label, govern how the events will be organized in your reports.

So, you’ll want to think carefully about how you want to structure your events.


Category is a name that you supply as a means to group objects — which are usually user interface elements that you want to track.

So, for example, if you have games and videos on your site, you’d probably want to have a “Games” category and “Videos” category.

Click “Event Category” in the Top Events report to see all the user interface elements with which your visitors interacted.


Action is the name you want to give to the type of interaction you’re tracking.

So, for example, for Videos, you’d probably want to track how many times your visitors pressed Play.

Click “Actions” in the Top Events report to see the interactions that occurred.


The Label argument is optional. A Label allows you to provide additional information for for the event you are tracking.

For example, if you are tracking video plays, you might use the Label argument to specify the name of the movie that was played.

Or, for file downloads, you might use it for the name of the file being downloaded.

Click “Labels” in the Top Events report to see the the Labels of of the events that occurred.


Value is the fourth, and optional, argument to _trackEvent().

Unlike the other arguments which are all strings, Value is an integer. You can use it to assign a numeric value to a tracked page object.

You’ll then be able to see a sum total of these values in the Event Value column of your Events reports.

You’ll also be able to see an average of these values in the Avg. Value column of your Events reports.

So, you might, for example, specify a dollar value when a specific playback marker is reached on your video player. To call _trackEvent() without a value, simply omit the argument.


In your reports, you’ll notice that both Total Events and Unique Events are counted.

Total Events is simply the total number of times an event occurs — really it’s just the number of times _trackEvent was called.

But, for Unique Events, each particular event is only counted once per visit.

So, if during a single visit, a visitor presses Play 5 times on the same movie, Total Events will be incremented by 5.

But Unique Events will only be incremented by 1, because for Unique Events, a particular event is only counted once per visit.


As we mentioned earlier, the arguments you provide when you call _trackEvent will govern how events are organized in your reports.

So, before you add the calls to _trackEvent to your site, consider these best practices.

First, determine in advance all of the kinds of events you’ll want to track.

Try to create a hierarchy of Categories, Actions, and Labels that will grow with your needs. Work with your report users to make sure that the hierarchy makes sense.

And use a clear and consistent naming convention for your Categories, Actions, and Labels.


Using trackEvent() allows you to analyze event based interactions in much greater detail than is possible using virtual pageviews.

For example, instead of just seeing how many times a movie was played on your site, you can analyze how people use your video player, and see how different events correlate with site usage and ecommerce metrics.

Also, by tracking events separately from pageviews, you won’t inflate your pageview count.



In Google Analytics, a visit—or session—is defined by 30 minutes of inactivity, or when a user quits the browser.

You can change the 30 minute default by calling setSessionCookieTimeout as shown in the slide.

Simply specify a new timeout value in milliseconds as the argument to _setSessionCookieTimeout().


By default, a conversion can be attributed to a campaign that is up to 6 months old.

But, if your business has a longer or shorter marketing campaign timeframe, you can change this value.
Just call _setCampaignCookieTimeout() and specify your new campaign length in milliseconds.

For example, let’s say that you want to set a campaign length of 30 days.
To figure out the number of milliseconds that is, type “30 days in milliseconds” into Google Search.
The search engine will give you the answer which you can plug into _setCampaignCookieTimeout().


Google Analytics attributes conversions to the campaign that most recently referred the visitor.
For example, let’s say that someone discovers your site by clicking one of your AdWords ads.
Then, they come back to your site by clicking a banner ad that you’ve tagged with campaign variables. This time, they convert to one of your goals.

By default, the banner ad will get the credit for the conversion, not the AdWords ad that originally referred them.
To change this behavior, you can tag all of your campaign links with utm_nooverride=1.

If you do this consistently with all of your campaigns, Google Analytics will attribute conversions to the first referring campaign, instead of the most recent one.

Note that the utm_nooverride setting can be used in conjunction with autotagging.


Google Analytics automatically tracks referrals from over 30 search engines.
But, if you want to add a search engine, you can do it by calling _addOrganic() in your Google Analytics Tracking Code.

First, perform a search in the search engine and look at the URL of the search results page.
In the URL, look for the keyword you searched — it should be preceded by a letter and an equal sign. This letter is the query variable for the search engine.

In the example, the query variable is “p”.

Add a call to _addOrganic in your Google Analytics Tracking Code. The first argument is the name of the search engine. The second argument is the query variable


You may wish to treat traffic that results from certain search keywords as Direct.

For example, if someone searches for the exact name of your site, you might want to treat that visit as a Direct visit instead of a search.

To do this, simply add a call to _addIgnoredOrganic() in your Google Analytics Tracking Code. Specify the keyword as the argument.


You can also treat referrals from certain sites as Direct traffic instead of as referrals.

For each site that you want to exclude as a referral and treat as Direct, add a call to _addIgnoredRef() in your Google Analytics Tracking Code.

Specify the name of the site as the argument.


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