Introduction to Google Analytics
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 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
• Benchmarking so that you can see whether your site usage metrics underperform or outperform those of your industry vertical.
• Funnel Visualization so that you can optimize your checkout and conversion click-paths
How GA Works?
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 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.
What happens if?
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.
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.
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 can give permission verbally, over email or through a support ticket that asks for help with a problem or asks a question about your data.
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 use benchmarking.
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 be able to use benchmarking and may not have access to specific ads-related features such as Conversion Optimizer.
Again, regardless of your Data Sharing selections, Google does not share Analytics data with any 3rd parties.
Understanding the Google Analytics interface will help you find and analyze information more effectively.
When you first login to your Google Analytics account, you’ll see a screen similar to the one on the slide.
In this example, the user has access to three Google Analytics accounts.
Click on the name of the account you would like to access.
This takes you to the account-specific page where you manage the set-up and configuration of your account and profiles.
You can toggle to your other Analytics accounts using the drop-down menu at the top right of the page.
Each profile for the selected account is displayed under “Website Profiles”.
From this screen you can access reports for each profile.
You an also edit configuration settings, add filters, add or change user permissions, and add or remove profiles altogether.
Click the “View Reports” link for a profile, and you’ll be taken to the dashboard for that profile.
A sample dashboard is shown on the slide.
We’ve called out the user interface features that are available on all reports.
Your report navigation, scheduled email settings, Help links, data export options, and the calendar.
Note that there are several places to find help information. The Help link on the top right of the page takes you to the Google Analytics Help Center.
Also, on the left margin of the page, you’ll see a Help Resources box with links.
The dashboard is where you put all the summary information about your site that you want to see at a glance.
To add a report to the dashboard, just go to the report you want to add and then click Add to Dashboard.
On the dashboard itself, you can position the report summaries however you like and delete the ones you don’t need.
In the left hand navigation, you’ll see that your reports are organized into categories: Visitors, Traffic Sources, Content, Goals, and Ecommerce.
If you don’t have an ecommerce site or don’t have ecommerce reporting enabled, you won’t see the ecommerce section in your navigation.
To view reports, click on any of the categories and the reports available within that category will appear.
Some reports contain additional sub-reports, like the AdWords report under Traffic Sources.
Click the arrow to see the sub-reports.
Setting The Active Date Range
To change your date range, click the arrow next to the active date range displayed at the upper right of all reports.
You can then use the Calendar or the Timeline to select a new date range.
The “Calendar” tab allows you to select date ranges by clicking on the day and month within the calendar or you can type dates in the “Date Range” boxes.
The “Timeline” tab has a date slider that you can resize and move to cover any range of dates.
You can see your site’s traffic trends in the Timeline.
Setting A Comparison Date Range
You can select a date range to compare to the current selected date range.
When using the Timeline to set a comparison date range, you’ll see two date sliders instead of just one.
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.
Graphic By Day, Week And Month
Most reports include an over-time graph at the top. You can make this graph display data by day, week, or month.
You can also compare two metrics on the same graph to see how they are correlated.
Click the arrow in the top left of the graph.
Then, click the Compare Two Metrics link and select which two metrics you want to compare.
In this example, we’re graphing visitors versus average time on site.
You can roll your mouse over the graph and see actual numbers.
Exporting Report Data
You can export data from any report. There are four formats: PDF, XML, CSV and tab-separated.
Simply click on the Export button at the top of any report page and select the format you want.
Next to the Export button, you’ll see an Email button.
Click it and you’ll see a screen with two tabs: Send Now, and Schedule.
You can schedule reports to be delivered daily, weekly, monthly or quarterly.
You also have the option to select what format to send them in, such as PDF or CSV.
The email scheduling feature provides an easy way to automatically distribute specific report data to the people who need it.
The Overview reports in each section contain a set of Curriculum links. You can use these links to quickly find information that you need.
In some cases, these links access reports that are not available from the left report navigation.
Title And Breadcrumbs
You can always see where you are in a report hierarchy by looking at the title and the breadcrumbs at the top of the report.
Look at the example on the slide.
From the title, you can see that you are in the Referring Link report and that you’re looking at traffic from the link blogger.com/home.
From the breadcrumbs, you can see that you are in the Referring Sites report hierarchy.
You can click on any of the breadcrumb links to go back to that report.
Narratives And Scorecards
Nearly every report contains a short narrative that summarizes the traffic that’s included in the report.
The scorecard below the narrative provides metric aggregates and averages for the traffic.
Each box in the scorecard contains a question mark button. Clicking it opens a small window that explains how the metric is calculated.
Most reports provide tabs that show different sets of data.
The Site Usage tab shows metrics such as the number of pages viewed per visit, the average time on site, and the bounce rate.
The Goal Conversion tab shows the conversion rates for each of your goals.
If you’ve enabled ecommerce reporting on your Profile Settings page, you’ll also see an Ecommerce tab.
This tab shows metrics such as Ecommerce revenue, number of transactions, and average value.
The AdWords Campaigns reports have an additional tab called Clicks. This tab contains AdWords related metrics such as clicks, cost, revenue per click and ROI.
You can segment table data in different ways using the Dimension pulldown menu.
So, for example, if you want to see the traffic in your keywords report broken out by City, you just select City from the pulldown menu.
In the Keywords and Search Engines reports, you have the option to analyze just paid, just non-paid traffic, or all search traffic.
Simply click on the links above the scorecard to make your selection.
Some reports allow you to view results by hour.
On these reports, you can select the view you want by clicking on the clock button in the top right corner next to “Graph By”.
There are five different Views available in most reports. The first icon organizes your report data into a table. This is the default view for many reports.
The second icon allows you to create a pie-chart based on any one of the metrics in the report.
The third icon shows a bar-graph based on any metric you select.
The fourth icon is the comparison bar graph view. It allows you to quickly see whether each entry in the table is performing above or below average.
The fifth icon allows you to instantly see a summary report with graphs for the traffic you’re analyzing.
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.
Expanding Numbers Of Results Desplayed
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.
You can use the Find box at the bottom left of your reports to narrow or refine your results.
For example, if you are looking at the All Traffic Sources report and you want to only see traffic from the Google domain, you can type in Google and select “containing”.
Or, to exclude all traffic from the Google domain, you would select “excluding”.
Contextual Help Resources
You can get information about any report you’re looking at by clicking one of the Help Resources.
About this Report offers a brief description of the report.
Conversion University provides insight into how you might use and interpret the data.
Common Questions links to Help Center articles that are related to the report.
Create Context For Your Data
When analyzing your traffic, avoid focusing on just a single metric. This pageviews result by itself isn’t actionable because you don’t know what the number really means.
But, when you look at pageviews in the context of other metrics, you start to get clearer picture.
For example, look at the bounce rate. Half of the time that people entered the site through this page, they left the site without looking at any other pages. This page is very important. By comparing the pageviews to the site average, we can see that this page accounts for over 28% of all the pageviews.
How has the performance of this page changed over time?
This page is receiving 20% fewer visits than it did last week and people are spending 10% less time on it. And last week, the bounce rate was only 24% — now it’s double that number.
So, putting data into context can help us ask the right questions and decide on a course of action.
Let’s look at another example.
Creating Context With Visualizations
Here we are looking at the Content by Title report.
We’re using the Compare to Site Average visualization to see which pages have significantly higher bounce rates than the site average.
The bounce rate for the first title is nearly 20% higher than the site average. The red bar shows that it’s performing worse than the site average.
Looking For Trends
Analyzing trends is another useful way to bring context into your analysis.
The graph on the slide shows us that pageviews peaked in May. Did visits increase or did each visitor look at more pages?
Investigating Changes In Trends
Using the Graph Mode to compare Visits and Pageviews, we see that Visits and Pageviews have increased proportionally.
Data Driven Decision Making
Now let’s identify which traffic sources led to the increase in traffic and revenue. We do this by looking at the All Traffic Sources report and clicking on the Ecommerce tab.
Comparing two days of traffic, we find that — although several sources sent an increasing number of visitors to the site — only Google organic and Google referral had a significant impact on revenue.
Therefore, we know that although other campaigns increased overall traffic, they did not bring in purchasers.
This kind of information can help you decide where to focus your promotion and site content resources.
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.
Pageviews, Visits, And Visitors – The Basics
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.
Pageviews Vs. Unique 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.
“Absolute Unique” Vs. “New vs. Returning”
The “Absolute Unique Visitors” report 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 Absolute Unique 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 Recency report to see how recently visitors have visited. You can look at the Loyalty report to see how frequently they return. Both the Recency and Loyalty reports are under Visitor Loyalty in the Visitors section.
Pageviews, Visits, And Visitors In Your report
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.
Almost all of the reports show Visits.
The Visitors metric — in other words the number of visitors who came to your site — is found in the Visitors section.
Time On Page
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.
Time On Site
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.
“Avg. Time On Page” VS “Avg. Time On Site”
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.
Flash Based Sites
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.
“Length Of Visit” VS “Avg. Time On Site”
The Length of Visit report 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.
The Length of Visit report can be found under Visitor Loyalty in the Visitors section.
Traffic Sources Reports
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.
Traffic Sources Explained
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.
What Makes A Good Source Of Traffic?
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.
All Traffic Sources
The All Traffic Sources 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.
Revenue And Conversion Drivers
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 Conversion or Ecommerce tabs to view which sources are driving conversions and purchases.
The Keywords report is 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.
You can isolate your paid search engine traffic by clicking the Paid link.
By doing this, you’ll limit the report to just showing your AdWords traffic and paid traffic from other search engines.
If you have paid keywords with a high bounce rate, you should evaluate whether your landing pages are relevant enough and you might also want to consider whether you should continue to buy those keywords.
Remember, you can use the Goal Conversion and Ecommerce tabs to compare the performance of keywords in terms of conversions and revenue.
For example, in the slide example, the ‘google kids’ phrase has a 86% bounce rate. Let’s find out what landing page is being used.
We start by clicking on the ‘google kids’ entry in the table.
This takes us to the Keyword report for ‘google kids’.
To find out which landing page is being used for this keyword, we’ll select Landing Page from the Dimension pulldown menu.
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.
As long as you have defined goals and track ecommerce transactions, you can use the metrics on the Goal Conversion and Ecommerce tabs to assess the performance of any campaign.
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.
Top Content, Content By Title, Content Drilldown
The first three reports listed in the Content section all show the same information, but each report organizes it differently.
The Top Content report lists each page that received traffic.
The Content by Title 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.
Why Am I Seeing “/” In My Reports?
Pages in your Content reports are represented by their “request URIs”, which is the part of the URL after the domain name.
So, a forward slash represents your home page.
When you create your profile, you should specify the name of your homepage as the Default page.
That way, instead of having forward slash show up in your reports, you’ll see your homepage URI instead.
Top Landing Pages
The Top 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.
Navigation Analysis reports can help you understand how people move through your site.
The reports are listed on the Content Overview page.
They’re also available from a pulldown menu when you drill down to a page detail report.
The first of these — Navigation Summary — can help you see how people arrived at a specific page and where they went afterwards.
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.
The list of pages that were viewed immediately after the page or pages is shown in the right column.
Why Are “Previous Page” And “Next Page” The Same?
Sometimes the Previous Page, the Next Page, and the page you are analyzing are all the same page. This can be caused by visitors hitting the refresh button multiple times and generating “self-referring” hits.
It can also be caused, for example, if the page has graphics that the visitor can click to enlarge.
Here’s what happens. The visitor views the page and Google Analytics registers a pageview. Then the visitor clicks on a graphic and views the enlarged graphic file.
This does not result in a pageview because the enlarged graphic file doesn’t have the Google Analytics Tracking Code. The visitor then clicks the back button, which registers another pageview.
If there are many images on the page, it’s possible that the visitor will click on each graphic.
This scenario will cause the Previous, current, and Next page to all be identical.
Entrance Path Reports
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 Top Landing Pages report and click the landing page you want to analyze.
Once you are on the Content Detail report for the page, click the Entrance Paths link as shown in the slide.
Analyzing A Landing Page Using Entrance Paths
You’ll now see the Entrance Paths report for your landing page.
In the middle 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.
Additional Content Reports And Drill-Down
You can use the “Analyze” drop-down menu to view additional reports such as Entrance Sources and Entrance Keywords.
The “Content” drop down menu allows you to select — or search for — specific pages to analyze.
If you have access to multiple Analytics accounts, you can access each account from the My Analytics Account drop-down list.
For example, if other administrators have added you to their accounts, you’ll see a list of those accounts in the drop down.
Creating A New Account
The last option in the drop-down is “Create New Account” - this 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.
If you’re using Analytics from your AdWords account, you won’t see this drop-down. You’ll only see it if you are signed in from google.com/analytics.
To give other users access to your Google Analytics account, you use the User Manager, which you can access here from the Analytics Settings page. Inside the User Manager, you can view all of the users who currently have access to your account.
“Administrators” And “Users”
There are two types of Google Analytics users. “Administrators” have access to all reports and they can also modify Analytics 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.
Add/Delete Users And Edit User Info
You use the User Manager to add new users, remove users, and edit user information.
Adding A New User
After clicking “Add Users” 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. Use the access type dropdown to select the level of access you want to give 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.
Granting Access To A User
If you select the “View Reports Only”, 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 edit the access settings for an existing user, go to the User Manager and click Edit next to the user. You can change their Access Type, and you can add or remove access to specific profiles.
Select the profiles you would like to remove report access to and click on the “Remove” button.
Managing Access And Accounts
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.
Changing Your E-mail Login Address
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.
On your Analytics Settings page, you can see a list of the profiles that belong to the account you’ve selected. You’ll generally have a separate profile for every domain that you track.
You might also have profiles that correspond to subdomains. Or you might set up a profile that only includes data for a filtered subset of traffic of one of your domains.
Profiles are very flexible — they are basically just a set of rules that define what data is to be included in the reports.
Here are some typical examples of profiles you might set up:
You might have a profile that only contains traffic data for a specific subdomain.
You might have a profile that tracks only a certain part of a site or that only tracks a certain kind of traffic.
And you might have profiles each of which has a separate set of reports. You could give some users access to one of these profiles and other users access to another profile.
The result would be that each user would only see reports that apply to them.
A profile consists of settings that define the reports that you see. These include user access, goals, and filter settings.
When you create a profile, you have the option of creating a profile for a new domain or an existing domain.
Here is a schematic showing an Analytics account with three profiles. The first two profiles are tracking domain A, and the third profile is tracking domain B.
Notice the tracking code number for each profile. The longer number, represented by Xs, is the Google Analytics account number–all three profiles have the same account number.
Next you see that Profiles 1 and 2 each have a “dash 1”, while Profile 3 has a “dash 2.” This smaller number is the property number.
Profiles 1 and 2 are tracking the same domain and have the same property number. They can be referred to as “duplicate profiles.”
Profile 3 is tracking a different domain, and has a different property number.
Now you may wonder, why would I create duplicate profiles?
You might want to apply filters to your duplicate profile so that it contains a subset of data. So, for example, you might filter the data in Profile 2 so that it only includes AdWords visitors to
domain A. In addition, you might want to give certain users access only to Profile 2. This has the effect of only allowing these users to see AdWords traffic to domain A.
Adding A New Profile
You’ll need to be an Administrator to add a new profile.
To add a new profile, go to the Analytics Settings page and click the Add Website Profile link. Then, in the screen that appears, select the Add a profile for a new domain.
Enter the URL for the web property and click Finish.
To edit a profile, click the “Edit” settings link for the profile on the Analytics Settings page. You must be an Administrator in order to edit a profile.
Using the edit link next to “Main Website Profile information,” you can configure various profile settings such as the default page, e-commerce reporting, and site search tracking.
You can also configure the profile to exclude query string parameters such as session IDs from the URLs that appear in the report interface.
To remove a profile, you can simply click the Delete link next to the profile on the Analytics Settings page. 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.
Analyzing All Marketing Campaigns
Google Analytics allows you to track and analyze all of your marketing campaigns — including paid search campaigns, banner ads, emails and other programs.
How To Track Your Campaigns
There are two ways to track your campaigns.
For AdWords campaigns, you can enable keyword autotagging which allows Google Analytics to automatically populate your reports with click, cost, and other data for every keyword you buy.
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.
Linking AdWords To Analytics
By linking Google Analytics to your AdWords account, you can get advanced reporting that measures performance and ROI for your AdWords campaigns.
Within AdWords, click the Analytics 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 can click the Analytics tab and create one.
By default, “Destination URL Autotagging” and “Apply Cost Data” will be selected when you link your accounts. We recommend that you leave both options selected.
The, “Destination URL Autotagging” option allows you to differentiate your paid ads from organic search listings and referrals.
You can choose to tag your AdWords keywords manually if you decide not to take advantage of this feature.
However, note that if you manually tag your AdWords campaigns, you won’t see Ad Group data in your reports.
The, “Apply Cost Data” option imports cost data into your AdWords reports so that you can see metrics such as clicks, impressions and ROI in your Analytics reports.
By leaving both options selected, you get the AdWords performance data you need to analyze and optimize your AdWords campaigns.
When you apply cost data from AdWords to Analytics - by default, every profile within that Analytics account will receive ALL AdWords data.
Be aware that you can only link one Analytics account to one AdWords account.
For administration purposes, you will want to create a new Analytics account for each associated AdWords account.
Note that once you have linked an Analytics and AdWords account – the time zone in Google Analytics will automatically take that of the AdWords Account (if they are different).
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.
How Does Autotagging Work?
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. Consequent values are separated using ampersands.
How To Enable Autotagging
To enable autotagging, go to the “My Account” tab within your AdWords interface.
Under “Account Preferences” you’ll see the “Tracking” option. Make sure that this 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 import cost data into your Analytics account, go to the “Analytics” tab within your AdWords interface.
Under “Profile Settings”, select “Edit Profile Information”. At the bottom of the screen you’ll see an “Apply Cost Data” checkbox.
Make sure that this box is checked.
Currently, it’s only possible to import cost data from AdWords.
Make sure both your AdWords and Analytics accounts are set to the same currency so that ROI data is accurately calculated.
View AdWords Data In Your Reports
Applying cost data to your Analytics account allows you to view your AdWords click, cost, and impression data in your Google Analytics reports.
This data is found on the “Clicks” tab of your AdWords Campaigns reports.
Data Discrepancies: Expected Behavior
You may notice differences between the data in your Google Analytics and AdWords reports. There are several reasons for these differences.
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. Stay on the lookout for these common issues.
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.
If you have disabled autotagging, make sure that you manually tag your Destination URLs with campaign tracking variables. Otherwise, visits will be marked as Google Organic instead of Google CPC.
Finally, 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.
Tracking Online Marketing
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 URL of your ads.
Adding a tag allows you to attach information about the campaign that will show up in your Analytics reports.
Again, adding tags is not necessary in AdWords if you have enabled autotagging.
If you have not enabled autotagging, you can add tags, but keep in mind that even if you add your own tags, you won’t see any Ad Group information from AdWords.
Manual URL Tagging
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 manually tagging paid keyword 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.
Example 1: Tag VS NoTag
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, whereas traffic from the first link will be grouped under (not set).
Example 2: Paid Keywords (PPC)
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 there will be no Ad Group information.
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 Campaign, Ad Group, and keyword information.
Where Is The Campaign Information Reflected?
Let’s look at where information from each of the tags shows up in your reports.
You can see all the sources in the All Traffic Sources report. This report will include not only all the sources you tagged, but also sources like “direct” and website names.
You can see also see traffic by medium in the All Traffic Sources report. In addition to all the mediums you tagged, you’ll also see mediums such as “referral” and “organic”.
Campaigns will appear in the Campaigns report. You’ll also see manually tagged AdWords campaigns in the AdWords Campaigns report.
In order for a campaign to show up in AdWords Campaigns, you’ll need to have tagged the associated links with a medium of cpc and a source of google.
Terms that you’ve used will show up in the Keywords report and — for any links that were tagged with a medium of cpc and a source of google — also in the AdWords Keywords report.
You access the AdWords Keywords report by drilling down from the AdWords Campaigns report.
Note that the AdWords keyword that *triggered* the ad will display in your Analytics report, rather than the original search query entered by the user.
For example, if your paid keyword is “shoes” and a visitor arrives at your site by searching for “men’s shoes,” the AdWords keyword report will only display “shoes” since the broad match or phrase is not captured.
Your content tags will show up in the Ad Versions report, along with the ad headlines from autotagged AdWords traffic.
You can also segment on any of these variables.
For example, to see all of the campaigns in California from which you received traffic, you could to to the Map Overlay report, drill down to California, and segment by Campaign.
The URL Builder
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.
Best Practices For Tagging Links
Stick to these best practices when tagging your advertising campaigns.
If you’ve enabled autotagging, don’t manually tag your AdWords destination URLs.
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.
AdWords Campaigns Report
AdWords-related reports are listed under AdWords in the Traffic Sources section.
The AdWords Campaigns report, which is the first one listed, contains performance metrics for your AdWords keyword ads. This report is actually the top level of a hierarchy of reports.
By clicking one of of the Campaigns in the table, you drill down to the Ad Groups report which lists all of the Ad Groups in that Campaign.
Click one the Ad Groups and you drill down to the AdWords Keywords report which lists all of the keywords in that Ad Group.
The AdWords Campaigns reports are unique in that they provide an extra tab labeled Clicks. The Click metrics are extremely useful for optimizing AdWords spending.
Let’s look at the first three.
Visits is the number of visits your site received from AdWords keyword campaigns.
Impressions is the number of times your ads were displayed.
Clicks shows the number of clicks for which you paid and which your ads received.
It’s normal for Visits and Clicks to show different numbers. In this case, we have fewer Clicks than Visits. The reason is that some visitors clicked on the ad, and then later, during a different session, returned directly to the site through a bookmark. The referral information from the original visit was retained, so some clicks resulted in multiple visits.
How Many Times Were Ads Displayed And Clicked?
Impressions, Clicks, Cost, and CTR — or Click Through Rate — all relate to how many times your ads were displayed and how frequently people clicked on them.
These metrics can help you understand how visible and compelling your ads are to searchers on these keywords.
For example, if you want a higher clickthrough rate, you might consider bidding for a higher position or rewriting your ad so that it is more relevant to the searcher.
If you are getting all zeros in the cost column, make sure you’ve linked to your AdWords account and that you’ve enabled autotagging.
Which Keywords Are Profitable?
Revenue per Click, Return on Investment, and Margin can help you assess keyword profitability.
For example, ROI is useful because it provides a single-metric comparison of how much you spent versus how much you made.
An ROI of 0% means that you earned in revenue the same amount of money you spent.
An ROI of 100% means that you spent, say $5, and made $10.
In other words, you spent X and received 2X in revenue.
It’s not uncommon to get 500% or even 1000% ROI. High ROIs simply indicate that your Revenue is many times greater than your Cost.
If your RPC numbers are all 0 and your ROI numbers are all -100%, it’s because you have 0 Revenue.
Make sure that you’ve set goal values or that you’ve enabled e-commerce tracking.
ROI And Short Date Ranges
Before you delete or pause negative ROI keywords, consider how much you’ve spent and whether you have enough data yet to make a decision.
In particular, watch out for short date ranges. It’s generally not a good idea to make keyword changes on the basis of a few days worth of data.
Consider return customers — those that find the site via an AdWords ad and then return later to buy again. You’ll miss repeat conversions if you set too short of a date range.
Also, it may take days or longer for many of visitors to become customers. So, set a date range that is at least as long as your expected sales cycle.
How Does Ad Position Affect Performance?
If you want to see how ad position affected keyword performance, you can use the Keyword Positions report to find out.
The keywords are listed on the left and you can use the dropdown menu above the list to sort them.
Then, select the keyword you want to analyze and you’ll see how it performed in each ad position for the metric you select.
For example, in the slide, we’re comparing ad positions based on pages viewed per visit.
The Side 1 position for this keyword referred visitors who looked at an average of between 20 and 21 pages, and the Side 8 position referred visitors who looked at an average of between 17 and 18 pages.
You can upload your TV ad—a video file—to your AdWords account and start a campaign on nationwide TV.
You specify the time of day and week, audience demographic, and type of program you’d like to target.
Once you’ve set up your TV campaign, you can track it using the TV Campaigns report.
You can drill down into specific TV campaigns and see the impressions delivered, number of ad airings, cost and CPM alongside your metrics like visits, time on site, and conversions.
For example, this screenshot shows website visits plotted against impressions delivered — the number of active TVs tuned to your commercial.
Looking at your web traffic metrics alongside your TV campaign metrics can help you optimize your TV campaigns.
With Google Audio Ads, you can buy and manage both local and national radio campaigns on over 1600 radio stations — all from your AdWords account.
Once your Audio Ads campaigns are running, you can use the Audio Campaigns report to track them.
You can drill down into specific Audio campaigns and also Audio DMA’s — Designated Market Areas.
You can see the impressions delivered, number of ad airings, cost and CPM alongside metrics like visits, time on site, and conversions.
You can conduct a before and after campaign analysis to see incremental lift and assess whether certain campaigns or markets are impacting better than others.
This screenshot shows website visits plotted against impressions delivered.
By looking at website metrics alongside your Audio campaign metrics, you can learn what is working best and optimize your campaigns accordingly.
How Well Does Each Ad Perform?
Although it’s not listed under AdWords, The Ad Versions report can help you optimize your keyword ads.
Assuming that you’ve enabled autotagging, you’ll see an entry in the table for each of your ad headlines.
You can compare site usage, goal conversions, and ecommerce performance for each ad — although there is no Clicks tab, so you won’t be able to see metrics like ROI and clickthrough rate.
A limitation of this report is that it can only differentiate ads based on the headline. But if each of your ads has a distinct headline, you’ll see an entry for each ad.
Also, note that if you’ve any tagged links with the utm_content variable, you’ll see traffic from those links in this report as well.
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? A goal can be any activity on your website that’s important to the success of your business.
For example, an account signup is a goal. A request for a sales call is another example of a goal.
To define a goal in Google Analytics, you specify the page that visitors see once they have completed the activity.
For an account sign-up, you might set the “Thank You for signing up” page as a goal.
Goals In Reports
Each time that a visitor sees the page you defined as a goal, a conversion is recorded.
You can see total conversions and conversion rates for each of your goals in your reports.
For each 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.
Why Define Funnels?
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.
Setting Up Goals
To set up a goal, first go the Analytics Settings page and edit the the profile for which you want to configure a goal.
Goal And Funnel Set-Up
Once you are on the Profile Settings page, look for the “Conversion Goals and Funnel” section.
Select a goal and click Edit. You can create up to 4 goals for each profile.
Entering Goal And Funnel Information
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.
Goal URL Match Types
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.
”Case Sensitive” Settings
Check “Case Sensitive” if you want the URLs you entered into your goal and funnel to exactly match the capitalization of visited URLs.
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.
Goal Conversions VS Transactions
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.
Profiles And Goal Tracking
You can have up to 4 goals for each profile. If you want to track additional goals, just set up duplicate profiles.
Filters And Goal Settings
If you are using a filter that manipulates the Request URI, make sure that your 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 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.
Reverse Goal Path Reporting
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 96 of the conversions — or about 15% of them — 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.
Funnel Visualization Report
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.
Finding The Report And Selecting A Goal
To find the Funnel Visualization report, look in the Goals section.
Once you are in the report, you can select the goal you want to analyze from the Select Goal drop-down menu.
Funnel Entrance Pages
The boxes along the left side of the funnel show the pages from which visitors entered the funnel.
(entrance) shows the number of times that the funnel page was a landing page.
In this example, 11,514 visitors came to the View Product Categories page from the home page.
Funnel Exit Pages
The boxes on the right show where visitors went when they abandoned the funnel.
For each step, you can see the pages that visitors went to.
(exit) means that the person not only abandoned the funnel but also left your site. In this example, there were 1,423 funnel exits from the View Product Categories page that went to the software.asp page.
Progressing Through The Funnel
In this example, only 29% of visits to the View Shopping Cart page actually proceeded to the login page.
The remaining 2,418 times, the person either left the funnel for another page or left the site entirely.
This data is valuable because you can use it to see what pages of your site may need to be altered.
For instance, in this example, you might want to improve the design of the the “View Shopping Cart” page so that more visitors log in and continue.
You can also see that only 41% of visits to the Login page continue on to the Place Order page. So, the Login page may also need improvements.
Understanding The Numbers
Let’s look at all the numbers in the report.
Here is the number of funnel entrances to the first step of the funnel.
Here is the number of funnel abandonments that occurred from this step.
Here is the number and percentage of funnel entrances that continued on to the next step.
Here is the number of funnel entrances to the second step of the funnel.
Here is the number of visits to the second funnel step. It includes those who proceeded from the first step and those who entered the funnel at this step.
Here is the number and percentage of visits to the second funnel step that continued on to the next step.
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.
You can use the Filter manager to create new filters, to edit their settings, and to delete them.
To apply filters to a profile, you edit the profile.
How Do Filters Work?
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.
How To Set-Up 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.
Google Analytics provides three commonly used predefined filters — you’ll see these filters under the “Filter Type” drop-down when you are creating your filters.
The first filter called “Exclude all traffic from a domain” excludes traffic from the domain that you specify in the Domain field directly below the Filter Type dropdown. 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 all traffic from an IP address”, 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 only traffic to a subdirectory”, 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.
Best Practices For Filters
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 type called “Exclude all traffic from an IP address”. You will need to enter your IP address or range of addresses into the ‘IP address” field.
Creating Custom Filters
In addition to the three pre-defined filters that Analytics offers, you can also create custom filters for your profiles.
Custom filters offer you greater control over what data appears in your profiles.
To create a custom filter, select “Custom filter” from the “Filter Type” drop-down. 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.
Example: 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 Top Content reports, 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 Top Content 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.
Filters And Profiles
So, for example, in the slide, the graphic shows a single Analytics account 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.
Again, you use the Filter manager to create and manage filters. To apply filters to a profile, you edit the profile.
Customize data Views
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.
How To Include Only Google AdWords Traffic?
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 profile for each subdomain.
To do this, 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 at 3 duplicate profiles. 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.
Best Practices For Filters And Profiles
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.
Best Practices For Include And Exclude Filters
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.
One AdWords Account, Multiple URLs
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.
Filters For Cost Sources
In your profile settings, select “edit 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.
Regular Expressions (RegEx)
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.
Character Sets And Ranges 
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.
Quantifiers And Repetitions ? + *
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.
Grouping ( )
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.
Anchors ^ $
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.
Shorthand Character Classes /d /s /w
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.
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.
Common Uses For Regular Expressions
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.
RegEx And Tracking Equivalent Pages
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.
RegEx Within The Report Interface
And, here’s an example of using regular expressions within your reports.
We’re using the Find box to display all the rows in the table that contain Google or Yahoo.
RegEx Generator For IP Address Ranges
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.
Points To Remember
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.
What Are Cookies?
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.
Persistent VS Temporary Cookies
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.
Cookie-Based Visitor Tracking
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.
The utm First Party Cookies
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.
Example: Google Analytics Cookies
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 “googlestore.com” into the Cookies search box.
If you’ve never visited the Google Store, go to googlestore.com now so that cookies are created.
_utma – Visitor Identifier
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 And _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.
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.
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.
_utmz – Campaign Cookie
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 googlestore.com. 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”.
_utmz – Campaign Values
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.
utmv – Visitor Segmentation
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.
The Overview report and the top level navigation are shown here.
Many of the reports allow you to drill down and segment for in-depth analysis.
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
• your sales revenue
• your e-commerce conversion rate, and
• the number of times people visited your site before purchasing
The E-Commerce Tab
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.
On the Ecommerce tab of the Referring Sites report, you can see how many transactions are associated with site referrals.
And, on the Ecommerce tab of the All Traffic Sources report, you can see the per visit value across all traffic sources.
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: Enable E-Commerce Reports
Step 1 is simply to enable the E-commerce selection on the Edit Profile Information page. Here’s how you find it.
On the Analytics Settings page, click Edit next to the profile for which you want to enable e-commerce tracking. This will take you to the Profile Settings page. At the top of the page, you’ll see a section called “Main Website Profile Information”. Click “edit” in the top right corner.
You’ll then see the screen shown here.
Select “Yes” next to E-commerce Website and save your changes.
Step 2: Add Google Analytics Tracking Code
For Step 2, check to make sure that the Google Analytics Tracking Code is on your receipt page.
You should probably place it near the top of the page because the code you add in Step 3 needs to appear after the Google Analytics Tracking Code.
As with the other pages on your site, you can use a server-side include or other template driver for dynamically generated pages.
Or, you can simply copy and paste the code into your HTML for static pages.
The slide shows an example of the standard Google Analytics Tracking Code.
Step 3: Add Code To Track Transactions
For Step 3, you’ll need to add code to track transaction details. This is an example of what the code on the receipt page might look like.
The Google Analytics Tracking Code is at the top.
Then, 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.
Creating The Transaction: _addTrans ( )
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 leave some of the fields blank by keeping the extra comma as a placeholder. But note that Order ID and Total are required arguments.
Providing Product Details: addItem ( )
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.
Recording The Transaction: _trackTrans ( )
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.
As long as you use ga.js, 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.
However, if you are using urchin.js, you should review the Help Center article referenced in the slide.
Shopping Carts On Other Domains Or Subdomains
For many e-commerce websites, the checkout process occurs on a separate domain or 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.
The Goal Conversion tab displays a metric called Per Visit Goal Value.
This metric is calculated based on the goal values that you set on the Goal Settings page.
The Ecommerce tab displays three revenue related metrics: Revenue, Average Value, and Per Visit Value .
These metrics are calculated using the revenue that is recorded by your Google Analytics e-commerce code.
So, what is the difference between Per Visit Value and Per Visit Goal Value on the Goal Conversion tab?
Per Visit Value is calculated using e-commerce revenue. Per Visit Goal Value is calculated using static goal values.
Goal Value + Revenue
There are a few places where Goal Value and Ecommerce Revenue are summed.
On the Clicks tab, the Revenue per Click, ROI, and Margin are based on the sum total of Goal Values and Ecommerce Revenue.
In the Content reports, the $ Index metric is also based on the sum total of Goal Value and Ecommerce Revenue.
We’ll look at $ Index next.
What Is $ Index?
The $ Index metric appears in most of the Content reports and it allows you to identify the pages that have the most impact on site profitability.
A single $ Index value by itself doesn’t tell you much — it’s most useful as a way of ranking pages.
By sorting your pages from highest $ Index value to lowest $ Index value, you’ll be able to identify your most important pages.
Let’s look at how $ Index is calculated.
$ Index Calculation
The calculation for $ Index assigns the highest values to pages that are frequently viewed prior to high value conversions or transactions.
In contrast, pages that aren’t viewed prior to conversions or transactions will have the lowest $ Index values.
To calculate the $ Index for a page, total ecommerce revenue and goal value is divided by the number of unique times the page was viewed prior to the conversion or transaction.
For example, let’s say that there were 4 visits to your site and 2 visits resulted in a $100 purchase. So, you made a total of $200 from these four visits. If on every one of these visits, the visitor entered your site through the home page, the $ Index value for your home page would be $200 divided by 4 page views. So the $ Index value would be $50.
On the 2 visits that included a purchase, the visitor also went to your Features page before purchasing. So, the $ Index value for your Features page would be $200 divided by 2 page views. The $ Index for your Features page would be $100.
Important Points About $ Index
You’ll notice that the calculation for $ Index uses unique pageviews.
This means that a page is only counted once per visit, even if a person views the page multiple times before converting.
Also, only pageviews that precede the conversion or transaction are counted.
If you aren’t tracking ecommerce revenue in Google Analytics and you haven’t assigned values to your goals, all of your $ Index values will be zero.
Finally, $ Index is most useful as a point of comparison or a ranking metric, not as a standalone number. It’s designed to help you identify the pages on your site that are most valuable.
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, Google.com and YouTube.com are both domains owned by Google.
Tracking Across Domains Doesn’t Happen Automatically
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.
Tracking Across Domains : Step 1
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”.
Tracking Across Domains : Step 2
The second step involves the _link() method. Use this method in all links between domains.
In this example, we’re updating all links from Google.com to YouTube.com 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.
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.
Tracking Subdomains Using _setDomainName ( )
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.
page. To correct this, you’ll need to set up an advanced filter. We’ll explain this in a minute.
Best Practice #1 For Tracking Subdomains
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.
Best Practice #2 For Tracking 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 — maps.google.com — is stored in the Hostname data field in Google Analytics.
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.
Multiple Domains With Subdomains (Step 1)
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().
Multiple Domains With Subdomains ( Step 2 )
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 Google.com and YouTube.com.
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.
Designing A Custom Report
You can create reports that show exactly the information you want to see, organized in the way you want to see it.
When creating a custom report, think of a table.
Dimensions are the rows of the table and metrics are the columns in the table. This report has two dimensions — in green — and four metrics in blue.
So, the report will show pageviews, bounces, visits, and revenue for each source and keyword.
Creating A Custom Report
Click on the Custom Reporting menu to get started.
If you have no reports defined, your Custom Reporting Overview will look like this.
You’ll see a help article link and links to sample reports.
Click ‘Create new custom report’ to build a new report.
Adding And Title And Tabs
To name your report, just click on the title field, enter a report name and click Apply.
Make your name simple and easy to identify so that you’ll be able to quickly find it in a list.
You can also provide a name for the report tab. This is particularly useful if you add multiple tabs to your report.
The next step is to select the metrics and dimensions you want.
Use the search box to find metrics and dimensions.
You can also click a menu item to expand it and browse all of the available metrics or dimensions.
To add a metric or dimension to the report, simply drag and drop it into the table.
Here is a report with four metrics. Now, let’s add dimensions.
You can add up to 5 dimensions for each custom report – one top level dimension, and up to four sub dimensions. The sub-dimensions allow a user to drill down to more detailed data.
Some combinations of metrics and dimensions aren’t allowed. If you see a metric or dimension greyed out, it’s because the combination isn’t available.
Review the chart available in the Google Analytics Help Center for an overview of permitted combinations:
Viewing And Accessing The Report
You’ll see the new report listed under the Custom Reporting menu. Now that the report has been saved, you can access it anytime.
In this custom report, we can click any of the Sources to see the keywords for that source. Lets click ‘google’.
Deleting A report
Click on the Custom Reporting menu link to access the Custom Reporting Overview page shown in the slide.
Here you can see a list of saved custom reports and you can edit or delete any of them.
Click Edit to modify the report.
Editing A Report
By editing a report, you can add, remove or modify metrics and dimensions, add tabs, and change the name of the report.
Creating a new tab allows you to drag a different set of metrics onto the report. To experiment with this, click Add Tab.
Here we’ve created a new tab called Visitors.
What Are Advanced Segments?
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.
Advanced Segments VS Filtered Profiles
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.
Applying And Advanced Segment
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.
The All Visits segment under Default Segments is enabled by default.
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 or view reports on another account or profile.
Creating An Advanced Segment
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 ca 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.
Now, drag the metric into the work area.
Select the condition Greater than and specify 100.
By clicking Test Segment, you can see that 25 visits meet the condition. You can add additional logic, but for now, let’s just name and save the segment. The segment will now appear in the Custom Segments area of the Advanced Segments pulldown.
Modifying An Advanced Segment
Now let’s modify this segment so that it only includes visitors from California.
Click Manage your advanced segments.
The Manage Advanced Segments screen will appear.
This screen lists both the predefined default segments and your custom segments.
If you want to build on an existing segment without changing the original segment, you can click copy next to the segment you want to build off of.
But if you want to change an existing segment, click edit. You can only change Custom Segments.
Let’s click edit next to the custom segment we just created.
Adding Conditions To A Segment
You can either add an Or condition or an And condition.
In this case, we only want to include visits that meet both conditions — revenue that exceeded $100 and coming from California.
So, let’s click “Add And statement.” Now, we can drag Region into the added condition and specify that Region must match California.
By clicking Test Segment, we can see that there were 25 visits with purchases of more than $100,
Over 7000 visits from California, and 6 visits which match both conditions.
Finally, rename the segment if you wish and then save it.
The original segment is replaced by the new one and you can now apply it to your reports.
What Are Motions Charts?
Motion Charts allow you to visualize your data in 5 dimensions.
You select metrics to be represented on the X and Y axis and by the size and color of the dots.
And you can see how the data changes over time.
A Motion Chart can help you identify patterns and relationships in your data that you might otherwise miss.
Accessing Motion Charts
Access Motion Charts by clicking Visualize. The Visualize button is available in most reports that show tables.
What You’ll See On The Motion Chart
Each dot on the Motion Chart will be a data point from the report that launched it.
So, for example, if you click Visualize on a Keyword report, each dot will be a keyword.
You can mouse over each dot to see its label and by clicking it, you can make the label stay visible as we’ve done here for the keyword “google store”.
In this chart, the X axis is Pages per Visit and the Y axis is Visits.
The color of each dot represents the Average Value.
The size of the dots represents the bounce rate.
In this Motion Chart, you can see right away that one keyword is much more valuable than the others.
How To Select Metrics
Menus are available on each axis and for dot color and size so that you can select metrics.
How To View Data Over Time
You can view the data over time by either dragging the slider or by pressing the Play button.
You can also change the scale of the X and Y axis to linear or logarithmic.
Plotting A Data Point’s History
By selecting Trails and dragging the slider, you can plot the history of one or more data points over time.
Saving A Motion Chart
You can save the settings of any Motion Chart so that you can access it later.
To do this, click Link to Chart and copy and save the link that’s provided.
Example Visualizations: Keywords
Let’s use Motion Charts to analyze two keywords from the Google Store.
The slide compares two Motion Charts side by side– one chart for each keyword. The same metrics are used in both charts.
By comparing the size of the dots, we can see that the keyword on the left attracts much less revenue than the keyword on the right. But even though it brings in less revenue, the conversion rates on the left are much higher — indicated by the warmer colors. So, it might be appropriate to try to attract more traffic on this keyword by buying it.
The keyword on the right is clearly valuable because it brings in so much revenue. But the low conversion rate suggests that it would be better to attract this traffic organically rather than through paid search.
Both keywords are attracting mostly new visitors, so it might make sense to create promotional programs for existing customers.
Internal Site Search Reporting
Google Analytics provides internal site search reports that allow you to see how people search once they’ve arrived at your site.
Why Is Internal Site Search Important?
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.
Setting Up Site Search
In order to set up Site Search Tracking for your website, you’ll need to configure your Profile settings.
On the Analytics Settings page, click ‘Edit’ next to the profile for which you want to enable Site Search Tracking.
Once the Profile Settings page appears, look for the Main Website Profile Information section and click Edit.
In the Site Search 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 Google.com, 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’
Identifying Your Query’s Parameter (s)
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”.
Setting Up Site Search
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 in your Main Website Profile Settings
If, in your Site Search settings, 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.
Site Search Categories
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 by following these steps:
First, select the ‘Yes’ radio button under ‘Do you use categories for site search?’
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. You can also 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 Yes, 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 your Main Website Profile Settings so if you choose to strip the category parameters here, you don’t have to exclude them again from your main settings.
Click ‘Save Changes’ to finish.
Where To Find Site Search In Your Reports?
The Site Search reports are located in the Content section of Google Analytics. Click on the Site Search navigation button to see all of the reports.
By analyzing your Site Search reports, you can find out:
• Which products or items visitors are looking for
• Where visitors started their search and where they ended up after searching
• Whether searches resulted in conversions
Site Search Overeview
The Site Search Overview summarizes the search activity on your site.
You can get to more detailed reports by clicking on the embedded links in the Site Search Overview, or by using the left navigation.
Site Search Usage
Here we can see that over half of all visits to this site included some form of internal site search, strongly indicating that internal site search is a popular feature on this website.
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.
Site Search Terms
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 5,720 searches and that 4,410 search terms were used. The terms are listed in the table, and you can see how each term compares in terms of number of searches, percentage of search refinements, conversions 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 cross segment this traffic. For example, if you wanted to see which cities these visitors came from, you could select City from the Dimension dropdown.
Other Analysis Options With Search Terms
You can see how visitors who searched on a specific term refined their searches.
To see this report, click on one of the terms in the table of the Site Search Terms report.
Then, from the Analyze dropdown menu, select Search Term Refinement. You’ll see a report similar to the one shown in the slide.
This report shows you the terms visitors searched on after their original search and which of these “refinements” are most popular.
If many of your visitors search on a common refinement, you might consider modifying the results page to present information related to the refinement.
Where did visitors who search on a specific term start the search from, and where did they go after searching?
To find this information, go to the Search Terms report for a specific keyword, and select Search Navigation from the Analyze dropdown menu.
Below the graph, you’ll see three columns. The table on the left shows the pages from which visitors began their searches. The icon in the middle represents the search results page and the table on the right shows the pages people visited immediately after the Search Results page.
To use the report, click one of the entries in the table on the left. You can now see where those people who began their search on the page you selected ended up.
Site Search Start Pages
You can use the Start Pages report to find out how many searches were initiated on each page of your site.
The easiest way to find this report is to click on Start Pages under Site Search in the left navigation.
The Start Pages report lists all of the pages from which visitors searched.
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.
Site Search Destination Pages
Which pages are most commonly found through search on your site?
You are able to see popular destination pages for the search term, as well as additional information on the related search.
Click on a page in the table to see the specific search terms that led to the page.
Site Search Categories
The Site Search Categories report helps you determine which categories your visitors selected when performing a search on your site.
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.
Site Search Trending
There are 7 Trending reports which display search activity over time. To access them, click Trending in the left navigation under Site Search.
Then select the specific report you want using the Trending dropdown menu in the report.
For example, selecting Visits with Search allows you to see how many visits to your site included a search.
Selecting Total Unique Searches shows you how many times people searched your site. And, if a visitor searches for the same thing more than once during a single visit, the search will only be counted once.
You May Wonder…
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.
Web Analytics Transition
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 Top Content 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.
Uses Of Virtual Pageviews
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.
Best Practices For Creating Virtual Pageviews
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” subdirectory.
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 provide labels and even values for each event you track.
All of your events show up in the Event Tracking reports within the Content section.
Call _trackEvent () To Register An Event
To use Event Tracking, you’ll need to use the ga.js based Google Analytics Tracking Code.
So, if your site has urchin.js tags, you’ll need to migrate to the ga.js tags.
Once you’re using ga.js, all you need to do is call the _trackEvent() method each time you want to register an event.
We’ll discuss the arguments to _trackEvent() in a minute.
Example: Tracking A Flash Video Player
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.
Event Tracking Data Model
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.
The Categories report in the Event Tracking section will show you 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.
The Actions report in the Event Tracking section will show you 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.
The Labels report in the Event Tracking section will show you 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 Event Tracking reports.
You’ll also be able to see an average of these values in the Avg. Value column of your Event Tracking reports.
So, you might, for example, specify a dollar value when a specific playback marker is reached on your video player.
Total Events VS Unique Events
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.
Best Practices For Setting Up Event Tracking
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.
Finally, note that a maximum of 500 events per visit will be tracked. So, avoid tracking highly repetitive events such as mouse movements
Advantages Of Event Tracking Over Virtual Pageviews
If you use the urchin.js Google Analytics Tracking Code and you track events using virtual pageviews, you may be wondering whether it’s worth it to switch to ga.js and use trackEvent() instead.
By using ga.js and trackEvent(), you’ll be able 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’ll be able to 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.
“User Defined” VS Predefined Variables
Google Analytics provides over a dozen predefined variables that you can use to analyze different segments of your traffic.
For example, segmenting a report by City allows you to compare how visitors from different cities interact with your site.
But, Google Analytics also provides a custom segmentation variable that you can use to classify visitors any way you like. It’s called User Defined.
Example: Member VS Non-Member Visits
For example, you could use the User Defined variable to divide your visitors into two groups, members and non-members.
Each time a visitor signs in to your site, you could set the User Defined value to “Member”.
A visitor who never signs in would have the default User Defined value of “not set”.
So, your User Defined variable would have two possible values — “Member” and “not set”.
This would allow you to compare, for example, conversion rates for members versus conversion rates for non members.
Example: Returning Member
What happens if a returning visitor is a member but visits the site without signing in? Will the visit still be properly classified as coming from a member?
Yes, because the value of the User Defined variable is set in the visitor’s __utmv cookie which is persistent. The visitor is classified as a ‘Member’ until the __utmv cookie expires after 2 years, or gets overwritten with another value.
A User Defined value is set at a session level, which means that the value applies to the whole session. So, even if you overwrite the value of “Member” with another value, this visit will still be classified as a Member visit.
But the next time the visitor comes back to your site, they’ll be classified according to the overwritten value.
Setting The User Defined Variable
To set a value, just call the _setVar() method and specify your value as the argument.
So, for example, to flag a visitor as a member, you’d add the code shown in the slide to a web page of your site that is accessible only by visitors who are logged in. The call to _setVar() must appear after your Google Analytics Tracking Code on the web page.
Calling _setVar() sets a persistent first-party cookie for the visitor called __utmv. Again, the visitor will be labeled as a ‘Member’ until the __utmv cookie expires after 2 years, or gets overwritten with another value.
Code Example: Form Selection
Let’s say you want to assign a visitor’s User Defined value according to his or her response on a form.
For example, if you have a form that asks the visitor to select one of four job categories, here’s how you could capture the response as a User Defined value.
Practices And Guidelines
Even if you overwrite a previously set User Defined value, the current visit is still classified according to the value that was overwritten. Only subsequent visits will be classified according to the new value.
So, User Defined values are best used to segment your visitors according to characteristics that don’t change frequently — things like profession or age group. You could capture this kind of information from a visitor survey, for example.
You might use the User Defined variable to segment visitors according to site activities they have participated in — for example people who have signed up to receive a newsletter versus those who haven’t.
Another use of the User Defined variable is to filter out your internal traffic. Although you’d generally use an exclude filter to exclude a range of IP addresses, you might have trouble doing this if your company uses dynamic IP addresses.
To get around this, you could call _setVar() on a page that is only accessible internally to label employees as ‘Internal’, and then filter out visits based on this value.
Viewing Report Data
To compare traffic, conversions, and e-commerce for each of your User Defined values, look at the User Defined report in the Visitors section.
For each value that has visits associated with, you’ll see an entry in the table.
Segmenting By User Defined
You can also segment most reports by User Defined values.
How To Change Session Timeout Value
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 _setSessionTimeout() as shown in the slide.
Simply specify a new timeout value in seconds as the argument to _setSessionTimeout().
How To Change Campaign Expiration
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 _setCookieTimeout() and specify your new campaign length in seconds.
For example, let’s say that you want to set a campaign length of 30 days.
To figure out the number of seconds that is, type “30 days in seconds” into Google Search.
The search engine will give you the answer — 2 million, 592,000 seconds — which you can plug into _setCookieTimeout().
How To Change Campaign Precedence
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.
How To Add Search Engines
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.
How To Treat Certain Keywords As Direct
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.
Treat Certain Referring Sites As Direct
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.