GoogleAnalyticsby Justin Cutroni

Copyright 2007 O'Reilly Media, Inc.

ISBN: 978-0-596-51496-9

Released: September 11, 2007

Web analytics is the process of measur-ing your web site, analyzing the data,and making changes based on the anal-ysis. Many businesses are just starting tolearn how they can increase the perform-ance of their web site by using web ana-lytics. For many people, their firstexposure to web analytics is Google An-alytics, a free tool available to everyone.Although analysis is vital to web analyt-ics, you can't do analysis without gooddata. Configuring Google Analytics cor-rectly is the key to collecting good data.This Short Cut provides a thorough de-scription of how the Google Analyticssystem works, information about manydifferent types of implementations, andways to avoid common pitfalls. It alsoshares some best practices to get yoursetup correct the first time.

Contents

Getting Setup Correct ..................... 2How Google Analytics Works ........ 2Profiles and Profile Settings .......... 11Filters .......................................... 15Goals and Funnels ........................ 29Common Web SiteConfigurations ............................. 35Marketing Campaign Tracking ..... 47E-Commerce Tracking ................. 56Custom Segmentation .................. 63CRM Integration .......................... 66Tips and Tricks ............................ 69Reference ..................................... 81Conclusion .................................. 89

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Getting Setup CorrectI wrote this Short Cut for one primary reason: to help you configure Google Ana-lytics correctly. If it's not configured correctly, then you may have incorrect data,leading to incorrect analysis. In my experience, getting Google Analytics config-ured correctly is the biggest roadblock to using it effectively.Throughout the Short Cut, I try to explain how Google Analytics works so youcan understand the impact of various configuration choices. Remember, GoogleAnalytics is used to analyze business data, which means each business will con-figure it differently. You need to identify what's best for your business andconfigure Google Analytics accordingly.I believe this Short Cut can be used in two distinct ways. First, it can be used as areference manual. If you're already using Google Analytics, and you have a ques-tion about filters, just flip to the "Filters" section. Or, if you're not sure you've setup cross-domain tracking correctly, read "Tracking Across Multiple Domains"in the "Common Web Site Configurations" section. My goal is to make eachsection a resource that can be used without reading the entire Short Cut.Second, you can view this Short Cut as a complete work. I've tried to structure thesections to follow a typical implementation. One of the tips I include in the "Tipsand Tricks" section is a short implementation process. If you're just getting startedwith Google Analytics, you may want to review that process first and keep it inmind as you progress through this Short Cut.

How Google Analytics WorksUnderstanding the architecture of the Google Analytics systemhow it collectsdata, identifies visitor and creates reportsis the key to understanding many ofthe advanced topics that will be discussed later in this Short Cut. Before we begindiscussing filters, goals, and advanced implementations, let's review the funda-mentals of how the system works.Data Collection and ProcessingI'm going to explain how Google Analytics collects, processes, and displays datausing Figure 1. The data collection process begins when a visitor requests a pagefrom the web server. The server responds by sending the requested page back tothe visitor's browser (step #1). As the browser processes the data, it contacts otherservers that may host parts of the requested page. This is the case with the GoogleAnalytics Tracking Code (GATC).The visitor's browser requests the code from a Google Analytics server (step #2),and the server responds by sending the code to the visitor's browser. All of the code

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is contained within one file named urchin.js. Once the browser receives the code,the GATC begins to execute while the rest of the page loads.During execution, the code identifies attributes of the visitor and his browsingenvironment, such as how many times he's been to your site, where he came from,etc.After all the appropriate data has been collected, the GATC sets (or updates, de-pending on the situation) a number of cookies (step #3), which are discussed laterin this Short Cut. The cookies are used to store information about the visitor. Afterwriting the cookies, the tracking code sends the data back to the Google Analyticsserver. The data is transmitted to the server via a request for an invisible GIF file(step #4).When the Google Analytics server receives this request, it stores the data in a largetext file called a logfile (step #5). There is one line in the logfile for each pageviewcreated by Google Analytics.Each line in the logfile contains numerous attributes of the pageview. This in-cludes: When the pageview occurred (date and time) Where the visitor came from (referring web site, search engine, etc.) How many times the visitor has been to the site (number of visits) Where the visitor is located (geographic location) Who the visitor is (IP address)

Figure 1. Google Analytic processing flow

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After the pageview is stored in the logfile, the data collection process is complete.The next step is data processing.At some regular interval, usually every few hours, Google Analytics processes thedata in the logfile. During processing, each line is split into pieces, one piece foreach attribute of the pageview. Here's a sample logfile line (note that this is not anactual logfile line from Google Analytics. It is a representation.)65.57.245.11 www.epikone.com - [21/Nov/2006:19:05:06 -0600] "GET/__utm.gif?utmwv=1&utmn=323703347&utmcs=utf-8&utmsr=1600x1200&utmsc=32-bit&utmul=en-us&utmje=1&utmfl=8.0&utmcn=1&utmdt=EpikOne%20-%20Google%20Analytics%20Support%2C%20Training%20-%20Urchin%205%20Software%2C%20Analytics%20Consulting&utmhn=www.epikone.com&utmr=-&utmp=/ HTTP/1.1" 200 35 "http://www.epikone.com/" "Mozilla/4.0 (compatible; MSIE 6.0;Windows NT 5.1; SV1; .NET CLR 1.1.4322; .NET CLR 2.0.50727)""__utma=100957269.323703347.1164157501.1164157501.1164157501.1; __utmb=100957269;__utmc=100957269;__utmz=100957269.1164157501.1.1.utmccn=(direct)|utmcsr=(direct)|utmcmd=(none)"

While most of this data is difficult to understand, a few things stand out. The dateand time (Nov 21, 2006 at 19:05:06) and the IP address of the visitor(65.57.245.11) are easily identifiable.Google Analytics turns each piece of data in the logfile line into a data elementcalled a field. For example, the IP address becomes the "Visitor IP" field. It's im-portant to understand that each pageview has many, many attributes, and thateach one is stored in a different field.After each line has been broken into fields (step #6), filters are applied to the data(step #7). Filters are business rules that you add to Google Analytics. They controlwhat data appears in your reports and how it appears.Finally, after the filters have been applied, the reports are created (step #8) andstored in the database (step #9). Each report in Google Analytics is created bycomparing a field, like the Visitor City, to a piece of integer data (Visits, Pageviews,Bounce Rate, Conversion Rate, etc.).Once the data is in the database, the process is complete. When you (or any otheruser) request a report, the appropriate data is retrieved from the database and sentto the browser.

WarningOnce Google Analytics has processed the data and it is in the database, itcan never be changed. This means historical data can never be altered orreprocessed. Any mistakes made during setup or configuration can perma-nently affect the quality of the data.

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This also means that any changes made to the configuration of Google An-alytics will not alter historical data.

About the GATCGoogle Analytics uses a very common web analytics technology called Page Tag-ging to identify visitors, track their actions, and collect the data. Each page on yourweb site that you want to track must be "tagged" with a small snippet of JavaScript.If the tracking code is not on a page, then that page will not be tracked. The fol-lowing JavaScript snippet is the standard GATC:

_uacct = "UA-XXXXX-X";urchinTracker();

Google suggests that you place the tracking code immediately before the tag of each page, so if the browser has any problems requesting the urchin.js filefrom a Google Analytics server (shown in Figure 1, step #2), it does not slowdown the page from loading.However, there may be cases where the tracking code must be placed at the be-ginning of the page (these will be discussed later). In these cases you can place theGATC right after the tag or even right before the tag.If your web site uses a content management system or some type of templatingengine, then the tracking code can be added to template files or other mechanismthat automatically generates common HTML elements. This is a fast, effective wayto tag all web site pages.If you cannot place the tracking code before the closing tag, then it ispossible to add it to some other part of the web page. It can be placed in the tag or almost anywhere in the main tag. If you do place the GATCin the tag, make sure it appears inline and not nested within another tag.However, this can have a negative effect on the visitor's experience. If there is anylatency in the Google Analytics server, then the browser will pause while waitingfor urchin.js to download. The visitor will experience a delay while the web siteloads in the browser. Remember, the visitor experience is very important. Anythingthat can degrade the experience should be minimized.

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NoteIt is possible to host the urchin.js file on your own server. To do so, copy thecontents of urchin.js by viewing the file in your browser. Just enter http://www.google-analytics.com/urchin.js into your browser, copy the result-ing code, and place it in a file on your server. Then, update the GATC toreference the new file location on your server and not the urchin.js locatedon the Google Analytics server:

_uacct = "UA-XXXXX-X";urchinTracker();

It should be noted that Google updates urchin.js without notifying users. Ifyou decide to host urchin.js on your own servers, make sure you periodicallycheck for updates.

There are two versions of the Google Analytics Tracking code: a secure versionand a nonsecure version. The secure version should be used on secure web pages.If the nonsecure version is used on a secure page, then the browser will display asecurity warning to the visitor. Security warnings can negatively impact a visitor'sengagement with your web site.To simplify the installation, the secure version can be used on both secure andnonsecure pages:

_uacct = "UA-XXXX-X";urchinTracker();

Customizing the GATCThere are many settings and features that can be enabled or modified by addingor changing variables in the GATC. A complete list of these variables, and howthey can be used to modify Google Analytics tracking, can be found in the "Ref-erence" section of this Short Cut.About urchinTracker()The most important part of the GATC is a JavaScript function named urchinTracker(). This function is used to collect visitor data, store that data in cookies,

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and send the data to the Google Analytics server. urchinTracker() appears in theGATC:

_uacct = "UA-XXXXX-X";urchinTracker();

Every time urchinTracker() executes, a pageview is created, and the data is sentto Google Analytics (Figure 1, step #4). Each pageview has a unique "name" thatcan be found in the URL column of the Top Content report. Figure 2 shows somesample data from the Top Content report.During the data-collection process, urchinTracker() extracts the information fromthe location bar of your browser. It modifies the value by removing the domainname and domain extension. The only things left are the directories, filename, andquery-string variables. This is called the Request URI, and it's one of the fieldscreated during data processing (Figure 1, step #6). Here's an example. The URLhttp://www.epikone.com/pages/index.php?id=110 would appear in the topcontent report as /pages/index.php?id=110. So, in this example, the Request URI

Figure 2. Top Content report showing pageview "names"

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is the part of the URL that comes after www.epikone.com [http://www.epikone.com].That's the default behavior of urchinTracker(). You can override this behavior andspecify how urchinTracker() names a pageview by passing a value to urchinTracker().For example, to change the way the pageview for /index.php appears in GoogleAnalytics you would modify urchinTracker() on the index.php page as follows:urchinTracker('index page')

This modification forces urchinTracker() to name the pageview 'index page'rather than /index.php. The deeper effect of this change is the value for the RequestURI is not index.php, but index page. This will have an impact on other configu-ration settings. I'll discuss this later in this ShortCut.urchinTracker() is just like any other JavaScript function. This means that it canbe executed anywhere a normal JavaScript function can be executed. So, if youplace urchinTracker() in the onClick attribute of an image, a pageview will becreated in Google Analytics when a visitor clicks on the image. How will the pa-geview appear in Google Analytics? By default, it will use the Request URI.However, if you pass a value to the function you can name the click anything youwant.This technique can be used to track visitor clicks, actions, and other browserevents. For example, to track clicks on links to other web sites (called outboundlinks), simply add the urchinTracker() function to the onClick attribute of theappropriate anchor tags. Don't forget to pass urchinTracker() a value so the visitorclick is identifiable. There are more tricks on using urchinTracker() to track Flash,JavaScript and non-HTML files in the "Tips and Tricks" section, later in this ShortCut.About the Tracking CookiesGoogle Analytics uses up to five, first-party cookies to track a visitor and storeinformation about that visitor. These cookies, set by the urchinTracker() function,track attributes of the visitor, such as how many times she has been to the site andwhere she came from. Note that the cookies do not store any personally identifiableinformation about the visitor. Here is a list of all the tracking cookies, their format,and other information:__utma

Expiration: 2038Format: domain-hash.unique-id.ftime.ltime.stime.session-counter

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The __utma cookie is the visitor identifier. The unique-id value is a number thatidentifies this visitor. ftime, ltime, and stime are all used to compute visit length(along with the __utmb and __utmc cookies). The final value in the cookie is thesession counter. It tracks how many times the visitor has visited the site and isincremented every time a new visit begins.

__utmb

Expiration: End of sessionFormat: hashcodeThe __utmb cookie, in conjunction with the __utmc cookie, is used to computethe visit length.

__utmc

Expiration: End of sessionFormat: hashcodeThe __utmc cookie, in conjunction with the __utmb cookie, is used to computevisit length.

__utmz

Expiration: By default, six Months, but it can be customizedFormat: domain-hash.ctime.nsessions.nresponses.utmcsr=X(|utmccn=X|utmctr=X|utmcmd=X|utmcid=X|utmcct=X|utmgclid=X)The __utmz cookie is the referral-tracking cookie. It tracks all referral informa-tion regardless of the referral medium or source. This means that all organic,CPC, campaign, or plain referral information is stored in the __utmz cookie.Data about the referrer is stored in a number of name-value pairs, one for eachattribute of the referral:utmcsr

Identifies a search engine, newsletter name, or other source specified in theutm_source query parameter See the "Marketing Campaign Tracking"section for more information about query parameters.

utmccnStores the campaign name or value in the utm_campaign query parameter.

utmctrIdentifies the keywords used in an organic search or the value in theutm_term query parameter.

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utmcmdA campaign medium or value of utm_medium query parameter.

utmcctCampaign content or the content of a particular ad (used for A/B testing)The value from utm_content query parameter.

utmgclidA unique identifier used when AdWords auto tagging is enabled This valueis reconciled during data processing with information from AdWords.

The expiration date for the campaign cookie can be set in the GATC. See the"Reference" section for more information about how to change the defaultvalue. Also, there is more information about referral tracking in the "MarketingCampaign Tracking" section.

__utmv

Expiration: 2038Format: domain-hash.valueCustom Segmentation cookie. This cookie is not present unless custom seg-mentation has been implemented. The cookie is created using the __utmSetVar() function, which will be discussed in a later section. The value passed to__utmSetVar() is stored in the value section of the __utmv cookie.

In this Short Cut, I don't discuss any of the issues regarding cookies and visitortracking. There are numerous studies, white papers, and blog posts estimating therate at which cookies are blocked by browsers and deleted by users. Eric Petersenfirst wrote about the pitfalls of cookies in a 2005 study for Jupiter Research. Asummary of his work can be found in this press release:http://www.jupitermedia.com/corporate/releases/05.03.14-newjupresearch.htmlIn my opinion, the best course of action to mitigate visitor behavior, and its effectson your data, is to look for trends and patterns in your data and to avoid absolutenumbers.

Note

Google Analytics does not track any visitor who has configured his browserto block first-party cookies or any visitor that has disabled JavaScript. Thereis no way to circumvent this limitation.

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Also, if a visitor deletes his cookies, he will appear as a new visitor the nexttime he visits the web site.

Profiles and Profile SettingsThe data for each web site that you track is stored in a profile. Most documentationdescribes a profile as data for a web site. But a profile is more than just data. Eachprofile has a number of settings that can affect the data within the profile.A more accurate way to describe a profile is a collection of data and business rules.The business rules modify the data in the profile. In Google Analytics, the businessrules are called filters (I'll discuss them more in a later section), and each profilecan have different filters.Multiple profiles can be created for the same web site. Each profile can have dif-ferent filters, thus changing the data in each additional profile created for a website. So, even though you may have two profiles for http://www.epikone.com, thedata in the reports could be dramatically different because of the different filtersapplied to each profile.Why would you create multiple profiles for a single web site? To create differentsets of data for different types of analysis. I'll discuss this more in the "Filters" and"Tips and Tricks" sections, where I suggest some profiles that you should considercreating.In addition to filters, there are other settings that are common to each profile.Understanding how each setting alters the data in the profile is important whenyou're setting things up.Website URLThe Website URL is used for two simple tasks. First, it is used to check the instal-lation of the tracking code. After a profile has been created, Google Analytics willspider the Website URL value and search for the tracking code to insure that it hasbeen installed correctly.The Website URL is also used to create the Site Overlay report. When the SiteOverlay report is generated Google Analytics adds the web site URL value anddisplays the page in a separate window. It then adds the appropriate data to eachlink on the page.Profile NameThe Profile Name identifies each profile in a list. There are no restrictions on howto name a profile. You can even create two profiles with the same name, but I don't

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recommend this (how would you differentiate them in a list?). I suggest namingprofiles something descriptive that all users will understand. If a profile has a filterapplied to it, then include a small description explaining how the filter changesthe data in the profile.For example, I might name a profile "www.epikone.com [http://www.epikone.com] - vermont traffic only". When viewing it in a list, I can easily understandthat the profile is for the www.epikone.com [http://www.epikone.com] domainand the profile contains traffic only from Vermont.

TipWondering when a profile was created? Add the "start date" to the profilename; that way, you'll always know how much data exists in the profile.

Time ZoneThis setting can be changed only if your Google Analytics account is not linked toan AdWords account. If you've linked your Analytics account to an AdWordsaccount, then the Time Zone setting will be the time zone you defined in yourGoogle AdWords account. Applying the AdWords time zone to the Analytics dataensures that the Google AdWords reporting in Google Analytics is accurate.Default PageSetting the default page for a website is a simple configuration step that ensuresthe quality of your Google Analytics report data. The default page for a web siteis the page shown to a visitor when they enter just the web site domain into thebrowser's location bar. If you type http://www.epikone.com/ into your browser,the web server returns index.php. You won't see index.php in the browser's locationbar, but that's the page the server returns. This is the same for directories withinyour web site. http://www.epikone.com/blog also returns index.php.Why does this matter? When the GATC executes, it creates pageviews using thepage name that the visitor requested. What if there is no page name, as is the casein this example? Google Analytics creates a pageview and names it /. However,when the user types http://www.epikone.com/index.php, Google Analytics cre-ates a pageview for index.php. Although the visitor sees the same page, GoogleAnalytics creates a pageview for / and a pageview for index.php: two pageviews forthe same page. Pageviews for a content page should be summarized as a single lineitem, not two. Figure 3 illustrates how two pageviews can exist for a single page.To remedy this problem, enter the default page for your web site in the "DefaultPage:" field in the Main Website Profile Information configuration section. Be sure

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to enter only the page name. Do not include a slash before the page name, and donot use regular expressions. As Figure 4 shows, just input the name of the page,nothing else.

Exclude URL Query ParametersSee the "Dynamic Websites" section for coverage of this setting. Even if you do nothave a dynamic web site, you should still read this section.

Figure 3. In the above image, /index.html and / are the same page and should beconsolidated into a single line item

Figure 4. "The Default page" setting

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E-Commerce SettingsThere are two e-commerce settings in the profile information section, shown inFigure 5. The first, E-Commerce Website, is a switch. When set to Yes, a seriesof e-commerce reports will be added to the reporting interface. By default, thissetting is set to No. So, if you have an e-commerce web site, make sure you changethis setting when creating a profile.The second e-commerce setting formats the currency in your Google Analyticsreports. Google Analytics can display currency in eight different formats. Changingthe currency setting will alter the way e-commerce data is displayed in the reportinginterface.Apply AdWords Cost DataMost profile settings can be configured in the Google Analytics interface. However,one profile setting, "Apply Cost Data" (Figure 6) can only be activated via theAdWords interface. This setting is used to automatically import cost data fromAdWords into Google Analytics.You can read more about the Apply Cost Data setting in the "Marketing Cam-paign Tracking" section.

Figure 5. Profile e-commerce settings.

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NoteRemember, making changes to any profile settings will not alter the datathat has already been processed by Google Analytics. It will affect the dataprocessed after the settings have been changed.

FiltersThere is no Google Analytics concept that is more important, but less understood,than filters. Functionally, filters are business rules. They are added to a profilebecause there is a business need to modify the data in a profile. For example, it isvery common to exclude web site traffic generated by internal employees. Thisdata can skew the data generated by actual customers, thus causing incorrect anal-ysis.I believe the key to understanding filters is understanding how web site data isstructured in Google Analytics. I discussed this earlier in "How Google AnalyticsWorks." If you have not read that section please do so.There are two types of filters in Google Analytics: predefined filters and customfilters. Predefined filters are common filters that most people use. Google has bun-dled these common filters together and simplified the implementation.

Figure 6. How to apply Google AdWords cost data to a Google Analytics profile

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Custom filters are different. You need to do all the configuration work when cre-ating a custom filter. While it can be challenging, custom filters truly offer youadvanced control over the data in your profiles.In general, custom filters and predefined filters work off of the same premise.How Profile Filters WorksFigure 7 displays the three common components of a filter: Filter field Filter pattern Filter typeI find it easy to define filters as a process involving these components. As GoogleAnalytics processes site data, it executes the filters that have been applied to theprofile. It compares the filter field against the filter pattern and, if the field matchesthe pattern, the filter performs an action. When the action occurs, the data in theprofile is changed.Multiple filters can be applied to a profile. When more than one filter is appliedto a profile, they are executed sequentially, in the order they are listed. The outputfrom one filter is used as the input to the next filter.

Figure 7. The three primary parts of a filter

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WarningFilters forever modify the data in the profile. This means an incorrect filterwill forever alter profile data. Be careful when applying filters to your pro-files. Test your filters on a "test" profile to ensure they work as expected (seethe "Reference" section for more information about test profiles).

Filter fieldThe first part of a filter is the filter fields. These data elements are created whenGoogle Analytics processes the data in the logfile (Figure 1, step #6). Each filterfield is an attribute of a pageview in Google Analytics. There are 37 different filterfields in Google Analytics, each of which can be used to create a filter. A completelist of filter fields, and what they represent, can be found in the Google Analyticssupport documents: http://www.google.com/support/googleanalytics/bin/answer.py?answer=55588Some of the most common filter fields are listed in Table 1.Table 1. The most commonly used filter fieldsFilterField DescriptionRequestURI

The request URI is created using the information in the location barof the browser. Google Analytics removes the subdomain, the host-name, and the extension. Everything remaining becomes the requestURI.

Hostname This is the primary and subdomain (if present), listed in the locationbar of the visitor's browser.

Visitor IPAddress

The IP address of the person visiting your web site. While this field isavailable for use in filters, it is not visible. The value of the IP address,which is protected by the Google Analytics Privacy Policy, cannot bedisplayed in reports.

TipEach field represents a piece of data that can have many values. For example,the field for Visitor City can contain Boston, San Francisco, Seattle, etc. Tofind the different values stored in a field, use the Google Analytics reports.As mentioned previously, each report is constructed using a field, so all thevalues in a field are displayed in the report built from that field. At the timeof writing, there is no reliable documentation about which reports are cre-

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ated from each filter field. This information should appear in the GoogleAnalytics support documentation soon.

Filter PatternThe second part of a filter is the filter pattern. The pattern is applied to the filterfield, and if the pattern matches any part of the field, the pattern returns a positiveresult, thus causing an action to occur. The patterns used in Google Analytics arecalled regular expressions.A regular expression is a set of characters that represents a larger set of data. Thesecharacters may be standard alphanumeric characters (like letters or numbers) orspecial characters (like the * or +).Rather than begin a discussion on regular expression in this section, I feel that itis better to continue the conceptual discussion of filters. If you understand thebasic concept of a regular expression, that it is a pattern applied to data, then youwill be able to follow this section.(More information about regular expressions can be found in the Reference sectionof this Short Cut.)Filter TypeThe final part of a filter is the filter type. The filter type is what happens to the dataif the filter pattern matches the filter field. There are seven different types of filters,each with a distinct function. They are all custom filters, and their descriptionsfollow next.Include/Exclude filtersInclude/exclude filters are the most common custom filters in Google Analytics.They're also the easiest to understand. The action for an include filter is "inclusion."This means that if the regular expression matches the filter field, the data is inclu-ded in the profile.Exclude filters operate in the opposite manner. If the filter pattern matches thefilter field, then the data will be excluded from the profile.Include/Exclude filters are extremely powerful because they can be used to seg-ment your data in different ways. For example, to analyze the visitation habits ofvisitors from California, create an Include filter as shown in Figure 8.The result of this filter would be that all data stored in the profile, and displayedin the reports, would be for visitors from "california." (The Visitor Region fieldstores the U.S. state name.)

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A similar example (Figure 9) would be an Include filter for visitors from New York.Now, how would you include visitors from New York and California? The answeris not as easy as applying two filters to the profile. Remember, filters are appliedsequentially during processing, and the output from filter #1 is used as the inputfor filter #2. Applying two filters to a profile, one to include visitors from New

Figure 8. An include filter that "lets in" data from California

Figure 9. A filter to include visitors from New York

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York and one to include visitors from California would not work. The reason isthe first filter would naturally exclude the filter pattern of the second filter. Tocombine the functionality of these two filters, a single filter, shown in Figure 10,must be used.This filter uses a regular expression to indicate that the Visitor Region must be"New York" or "California".

Search and Replace filterThe Search and Replace filter is a simple way to replace one piece of data with adifferent piece of data. This is most often used to replace long, unreadable URLswith more "human-readable" information.Search and Replace filters are slightly different than other filters because they donot have a filter pattern. Instead they have a search string, which is the same as afilter pattern.When a Search and Replace filter is applied to a profile, the filter searches the filterfield for the search string. If the search string is found in the filter field, then thefilter replaces the entire search string with the replace string. Figure 11 illustratesa common Search and Replace filter.

Figure 10. A filter to include visitors from New York and California

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This filter searches the Request URI field for the pattern "category_id=1234". Ifthe search string is found in the Request URI, then the entire Request URI will bereplaced with the string "Chairs".

WarningThe Replace String is standard text. It is not a regular expression. Also notethat the entire Filter Field is replaced with the Replace String. So, if there areany other filters attached to a profile, and those filters use the same filterfield as the Search and Replace filter, then those filters may not work if theSearch and Replace filter first modifies the value in the filter field.

Lowercase/Uppercase filtersLowercase/Uppercase filters (see Figure 12), are different than other filters in thatthey do not require a filter pattern, only a filter field. Simply put, a Lowercase orUppercase filter changes the selected filter field to all lowercase characters or alluppercase characters, respectively.

Figure 11. A common Search and Replace filter

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Why is this filter needed?Some web servers, particularly Microsoft IIS servers, create pageviews with mixed-case URLs. This means that Google Analytics creates multiple line items for thesame physical page in various reports.As an example, the URLs http://www.epikone.com/default.asp and http://www.epikone.com/Default.asp will generate the same page for the visitor. How-ever, Google Analytics will create two line items in the Top Content report, onefor default.asp and one for default.asp. Obviously these are the same page andshould be tracked as a single line item. A Lowercase filter forces the filter fieldin this case, the request URIto a consistent case, thereby consolidating all ver-sions of the same page into a single line item in the Google Analytics reports.

TipAnother good use of the Lowercase/Uppercase filter is for keywords. Manyusers want to see "EpikOne", "epikone", and "EPIKONE" as the same key-word, not three different keywords. An Uppercase or Lowercase filter,applied to the Campaign Term field, will change the keyword case.

Figure 12. Lowercase filter setup form; Uppercase filters have the same settings

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Lookup Table filtersLookup Table filters are not currently active in Google Analytics. If you were pre-viously an Urchin on Demand customer, and were using Lookup Table filters, thenthey will still work. However, new Lookup Table Filters cannot be created.The idea behind lookup tables is that they are an automated search-and-replacefilter. A lookup table is a text file that can be uploaded to Google Analytics. GoogleAnalytics then applies the information in the lookup table to a specific filter field.For example, a lookup table could be used to replace product IDs in Google An-alytics with the name of the actual product.This filter can be extremely useful. Hopefully it will be added to Google Analyticssoon.Advanced filtersAdvanced filters can alter data fields by combining elements from multiple filterfields, removing unnecessary parts of filter fields, or replacing one filter field withanother.Unlike most filters, advanced filters have two filter fields: Field A and Field B. Alongwith each filter field, there is an Extract field. The Extract field is synonymous withthe filter pattern; it is the regular expression that is applied to the filter field. Itshould be noted that you don't have to use both fields.So, Extract A is applied to Filter A, and Extract B is applied to Filter B. The reasonwhy the filter patterns are named Extract for advanced filters is that certain partsof Field A and Field B can be removed, or extracted, from each field. The part ofthe filter field that is extracted is specified using a regular expression.In Figure 13, two fields are referenced: the Request URI for Field A and the Host-name for Field B. The pattern applied to filter Field A means "capture all thecharacters in the Request URI and retain those characters." The pattern applied tofilter Field B means "match all the characters in the Hostname and retain thosecharacters."What happens to those characters that are captured in the Extract Fields? GoogleAnalytics allows you combine the extracted pieces of data and output them toanother field, called a Constructor. The Constructor is simply a field. After a partof the field has been captured, Google Analytics stores it in a variable. Data ex-tracted in Extract A starts with $A, and data extracted from Extract B starts with$B.You can configure Google Analytics to permanently change the value of the con-structor using the Override Output Field setting (see Figure 13). When you select

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Yes, the data in the constructor overwrites the Output Field value. So any reportsthat are created using that field will be modified. Table 2 breaks down the processof combining two extracts and exporting them to a constructor.

Table 2. How two extracts can be combined to modify an existing field

Captured part ofRequest URI [$A1]

Captured part ofHostname [$B1]

Output to Constructor: Re-quest URI [$B1$A1]

/pages/index.html www.epikone.com[http://www.epikone.com]

www.epikone.com/pages/in-dex.html [http://www.epikone.com/pages/index.html]

If this filter is applied to a profile, then all the reports based on the Request URIchange; they will include the hostname as well as the directory path, filename, andquery-string variables.

WarningModifying the Request URI field using an advanced filter can affect othersettings in Google Analytics, most notably goal settings. (Goals can be cal-

Figure 13. Advanced filters have two filter fields, Field A and Field B; a portion of eachfilter field can be extracted using regular expressions

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culated using the Request URI.) So, if an advanced filter changes the RequestURI, make sure to check the effect on your goal settings.

There are two other settings specific to an Advanced filter: Field A Required andField B Required. These settings control the logic of an Advanced filter. When youset either one of these options to Yes, Google Analytics will place some constraintson when the filter takes action. If Field A does not match the pattern in Extract A,then the filter will not "execute." The same goes for Field B.Here's one way an Advanced filter can be used to gain more insight into the actionsof visitors. In some applications it may be useful to identify which organizationsare consuming content on your web site. Maybe it's a competitor or potential client.An Advanced filter can be used to attach the ISP or network name to the RequestURI. This will display the name of the organization's network along with the pagerequested. Figure 14 shows the settings for such a filter.The above filter would modify the Constructor (Request URI in this case) by add-ing the Visitor's ISP or organization's name to the Request URI. So, all reportscreated using the Request URI would be modified. The result of this filter is shownin Figure 15.Now, let's take the above example one step further. Let's say we'd also like to seethe keyword the visitor used to find the web site along with the network name.

Figure 14. Filter to concatenate the visitor's ISP organization and Request URI

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We can use a series of filters, passing data from one filter to the next, to modify aconstructor.This first filter would be an Advanced filter to add the keyword to the ISP or net-work name. While this filter is similar to the previous filter, there is one maindifference. The constructor used in this filter, shown in Figure 16, is Custom Value1. This field is a temporary field that is not used by any reports. It is meant to beused when you need to pass data from one filter to another.The second filter, shown in Figure 17, must add the Request URI to the value wepreviously created and stored in Custom Value 1. I've used a comma in the con-structor to separate the Campaign Term from the Visitor ISP Organization. I likeusing the comma to separate values because if I export the data from Google An-

Figure 15. The Top Content report displaying the modified Request URI

Figure 16. Advanced filter that stores data in a temporary variable

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alytics into Excel, I can easily import it as a comma-separated file, and Excel willplace each value in a new column.I've chosen to use the "Visitor Java Enabled?" field as the constructor for the secondfilter. The reason is I want to place the data in a field that is not used by any "major"reports. If I used the Request URI as the constructor, then all of the reports thatare based on the Request URI would break. So, by using "Visitor Java Enabled?",I break only the Java Enabled report. In my experience, this report is not used veryoften and can be sacrificed. If you use the Java Enabled report, then consider usinga different field for the constructor or create an additional profile for this filter.Remember, for the above filters to work correctly, they must be in a specific order.Google Analytics does not limit the number of extracts for each field. Multipleparts of Extract A and Extract B can be captured. If more than one part of an extractis captured, then Google Analytics will retain multiple variables for that extractfield. Figure 18 shows multiple values extracted from a filter field.The above filter will capture all of Extract A (the entire hostname) and two partsof Extract B. The first part of Extract B that will be captured is "v" followed by anycharacter. The second extract from Field B will be everything after "/23/".Then, the filter will combine the hostname with the two extracts from Field B. Itwill separate the values in $A1, $B1, and $B2 with slashes. The characters entered

Figure 17. Advanced filter that modifies a value stored in a temporary variable

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in the Output To field are literal characters, meaning that they appear exactly asyou type them, except for those that begin with $A or $B.Predefined filtersBefore we conclude our discussion on filters, I should mention that Google Ana-lytics has three predefined filers. These filters are the three most commonly usedfilters. Google has simplified the implementation by removing the filter field andfilter pattern. With a predefined filter, just choose the filter type and enter theappropriate data into the form. Figure 19 lists the three predefined filters.The "Exclude all traffic from a domain" filter uses a reverse lookup to identify thedomain of the site visitors. The visitor domain excluded is the domain associatedwith the visitor's IP address."Exclude all traffic from an IP address" removes all data coming from the addressedentered into the filter pattern. This filter is primarily used to exclude internal com-pany resources.The "Include only traffic to a subdirectory" filter isolates data for a specific direc-tory on the web site. This filter is usually used to create profiles that focus on onepart of the web site.

Figure 18. Multiple extracts using an Advanced filter

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Goals and FunnelsAnother common profile configuration is the creation of goals and funnels. Whileit is not necessary to create any goals or funnels, it is highly recommended. GoogleAnalytics automatically segments report data and displays the goal conversion ratefor each line item in the report. So, if you're looking at a report containing key-words, Google Analytics will display the goal conversion rate for each keyword.This type of segmentation is extremely valuable when analyzing data but is onlypossible if you set up goals for your profiles.GoalsGoogle Analytics goals are a way to measure conversion activities on your website.A goal is simply a pageview that indicates the visitor has completed some type ofhigh-value process. This process could be filling out a contact form, purchasing aproduct, or downloading a file. Each process usually concludes with some type of"thank you" page. In Google Analytics, this is called the goal page.A goal is defined by the URL of the goal page. As Google Analytics processes sitedata, it increments the goal counter each time a goal page is found. If the goal pageis found multiple times during a single visit, the goal counter is incremented onlyonce. This is important because it means that a visitor can convert only once duringa visit.

Figure 19. Predefined filters

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There are multiple ways to define a goal, depending on the complexity of your website. The easiest way to create a goal is to paste the URL of your goal page into theGoal URL field. Figure 20 shows the goal setup form and the Goal URL field.So, if your checkout process ends with http://www.epikone.com/thankyou.php, enter "http://www.epikone.com/thankyou.php" in the Goal URLfield.If the URL of the goal page is http://www.epikone.com/thankyou.php?submit=true then enter http://www.epikone.com/thankyou.php?submit=true intothe Goal URL field.A goal can also be defined using a regular expression. Rather than enter an exactURL in the Goal URL field, you can enter a regular expression. This is particularlyhelpful if the web site is dynamic and contains query-string parameters that maydiffer from one visitor to the next. If the goal page contains a unique identifier,then you can't copy and paste a URL into the Goal URL field; every goal URL willbe different. You'll need to use a regular expression for the Goal URL. I'll discussthis later in the "Additional Settings" section.The Goal setup form also includes a field for "Goal name." The "Goal name" willbe used to identify the goal in the reports. The Activate Goal setting is an on-offswitch. Switching the setting to Off will stop tracking for the goal. Why would youwant to turn a goal off? Google Analytics will calculate an overall web site con-version rate using all of the goals you define for the site. If you create a goal that istemporary, say for a specific campaign, then it could artificially skew the overallsite conversion rate if you leave the goal on after the campaign ends.

Figure 20. Paste the URL for a goal page in the Goal URL text field

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NoteIn reality, Google Analytics uses only the Request URI when calculatinggoals. So, even if you specify the entire URL as a goal page, Google Analyticswill use only the Request URI. This also means that if you modify the RequestURI using a filter (like an Uppercase filter or Lowercase filter), you may needto change your Goal URL.For example, if a goal is defined as /pages/html/thankyou.html, but an ad-vanced filter has been applied to the profile and changes the request URIto /pages/thankyou.html, then the goal will not work.

FunnelsA funnel is a series of predefined steps, or pages, that a visitor must go throughbefore reaching a goal. Not every goal will have an associated funnel, so defininga funnel is optional. You should set up a funnel if you have a predefined processthat the visitor must go through before reaching the goal. This could be as simpleas specifying the form used on a "Contact Us" page or as complicated as a multistepcheckout process. The funnel is an excellent way to visualize problems in the con-version process.Setting up a funnel is very similar to setting up a goal. Each step in a funnel is apageview. So, to create a funnel, paste the URL for each page in your process intothe setup form (Figure 21).

Figure 21. The funnel setup form

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The "Required step" checkbox can affect the number of goal conversions in theFunnel Visualization report. When selected, visitors who complete the goal with-out starting at the first step in the defined funnel will not be shown as completingthe goal in the funnel visualization report. However, the conversion will be recor-ded in other conversion reports.

WarningGoogle Analytics will "backfill" your predefined funnels. For example, if youhave a four-step funnel, and a visitor completes only the first step and thefinal step, Google Analytics will go back and indicate that the visitor actuallyhit every step in the funnel process.

Additional SettingsEach goal/funnel has an "Additional Settings" section that can aid configurationin unique situations. Any changes made in this section will be applied to both thegoal and the steps in the funnel. Figure 22 shows the options available in the"Additional Settings" section.The "Case sensitive" setting can be used with web sites that have mixed-case URLs.If the Goal URL, or any of the steps in your funnel are case-sensitive, check thischeckbox. Remember, filters can affect this setting. If an Uppercase or Lowercasecustom filter has been applied to the profile, then there will be no mixed-case URLs,and this setting will be irrelevant.The Match Type setting is a powerful setting that can facilitate goal tracking. Forexample, if each goal page contains a unique customer identifier, then it will beimpossible to paste a single URL into the Goal URL field. Because each URL willbe unique, your web site will not have a single URL that represents the goal page.Google Analytics has three different match types that can be used to match multipleURLs and resolve goal setup issues. When selected, each match type will change

Figure 22. The additional settings for a goal and funnel

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how Google Analytics applies the value in the Goal URL field to the data it pro-cesses.Exact Match

The value in the Goal URL field must exactly match the URL in the locationbar of the visitor's browser

Head MatchThe Head Match setting can be used when a small part of the goal URL differsfrom one visitor to another. When using a Head Match, the URL in the visitor'sbrowser must exactly match the value in the Goal URL. However, if there isany additional data at the end of the visitor's URL that does not appear in theHead Match value, the goal will still count. The Head Match will match bothpath data and query-string variables.

Regular ExpressionThis setting defines a goal using a regular expression rather than a static URL.If the regular expression entered into the Goal URL matches any part of theURL in visitor's browser, then the goal is counted. This includes subdomains,primary domains, path information and query-string variables. (To learn moreabout regular expressions, see the "Regular Expressions" section in the "Ref-erence" material.)

WarningThe "Case sensitive" and Match Type settings are applied to the values inboth the Goal URL and funnel steps. It is impossible to use a match type ofExact Match for your funnel steps and a Regular Expression match type forthe Goal URL.

TipYou can define goals and funnels for data created by urchinTracker(). Re-member, if you pass a value to urchinTracker(), then that data becomes apageview in Google Analytics. These pageviews can then be defined as goalsby placing the value passed to urchinTracker() in the Goal URL field.

The final option in the "Additional Settings" section is "Goal value." Use this fieldto monetize non-e-commerce goals. For example, if each contact form submittedby a user is worth $100, enter 100 in the "Goal value" field. Google Analytics willuse 100 to calculate return on investment (ROI) and other revenue-based calcu-

Google Analytics 33

lations. If e-commerce tracking is active for a profile, and you would like to use e-commerce data for your goals, simply leave this field blank. Google Analytics willinsert a zero and use the value from e-commerce transactions as the goal value.You should really try to monetize your non-e-commerce goal values. The reasonis that Google Analytics will use the goal value to calculate an $Index value. Thisis a metric that indicates how much each page on your website is worth. You canuse the $Index value to determine which content is most important to the conver-sion process. The $Index value can be found in the Top Content report.

Using Regular Expressions to Extend GoalsSome users are frustrated by the four-goals-per-profile limit. With a little creativity,the limit can be circumvented by tracking multiple conversion activities in a singlegoal. This can be done with regular expressions. Remember, the Goal URL can bea regular expression. This means that multiple URLs on a web site can match theregular expression defined as a goal.Here's an example. The following two pages represent a conversion on a site:http://www.analyticstalk.com/blog/outbound/rss/googlehttp://www.analyticstalk.com/blog/outbound/rss/rssThe regular expression used for the Goal URL could be /rss/(google|rss)$.Both URLs match the regular expression and would count toward the goal tally.To drill down into the data and differentiate which URL generated more goals, usethe Goal Verification report. This report, shown in Figure 23, segments a goal bythe different pages that contribute to it.

Note

What goals should you configure for your website? That depends on youronline business model. Remember, Google Analytics collects business data,so the goals for an e-commerce business may be very different than those fora lead-generation business. In general, goals usually involve one of the fol-lowing:

Figure 23. The Goal Verification report

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Completing an e-commerce transaction Submitting a "Contact Us" form Subscribing to an email newsletter Viewing certain content on a web site

Common Web Site ConfigurationsThis section of the ShortCut addresses common web site architectures that cancause problems with the Google Analytics tracking. Remember, the technologythat Google Analytics uses to track visitors, called page tagging, is based on Java-Script and cookies. So any web site architectures, like multiple domain names, thataffect cookies or JavaScript can interfere with tracking. Most of the changes re-quired to deal with these web site configurations are usually made to your web siteand not Google Analytics directly.If your web site contains five static HTML pages, then it is very likely that thissection will not apply to you. However, if you have a dynamic web site that crossesmultiple domains and subdomains, then this section will offer you valuable infor-mation about how, and why, you should configure Google Analytics.Dynamic Web SitesA dynamic web site is one that uses query-string parameters, or variables, to de-termine which content the visitor is consuming. As discussed in the section onurchinTracker(), Google Analytics includes query-string parameters when it cre-ates a pageview. Table 3 illustrates how a URL in the browser's location bar wouldappear in a Google Analytics report.Table 3. How Google Analytics creates page "names"URL in Browser Resulting URL in Google Analyticshttp://www.mysite.com/dir/index.php?sess=1234&cat=3&prod=foo&var2=bar

/dir/index.php?sess=1234&cat=3&prod=foo&var2=bar

http://www.mysite.com/dir/index.php?sess=4567&cat=6&prod=bar&var2=foo

/dir/index.php?sess=4567&cat=6&prod=bar&var2=foo

However, not all query-string parameters are created equal. Some query-stringparameters indicate the content that a visitor is viewing. These parameters arenecessary for analysis. Other query-string parameters are used by your web serveror web application and provide no insight into the visitor's actions or the content

Google Analytics 35

she views. These variables are not needed and should be eliminated from GoogleAnalytics.To configure Google Analytics to remove query-string parameters during process-ing, simply list the unwanted parameters in the Exclude URL Query Parametersfield in the "Profile Information" section (see Figure 24). List multiple query-stringparameters as a comma-separated list.In the above example, the query-string variables sess and var2 would be removedfrom Google Analytics during processing. Table 4 indicates how a URL will appearafter certain parameters have been excluded.

Table 4. How a URL looks after removing unnecessary query-string parameters

URL in Google Analytics Resulting value after excluding parameters/dir/index.php?sess=1234&cat=3&prod=foo&var2=bar

/dir/index.php?cat=3&prod=foo

/dir/index.php?sess=4567&cat=6&prod=bar&var2=foo

/dir/index.php?cat=6&prod=bar

Figure 24. Enter unwanted query-string variables in the Exclude URL Query Parameterstext box

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Excluding query-string parameters from Google Analytics will affect other partsof the application. Once a query-string variable has been added to the list, it willbe completely removed from the system. This means that the parameter data willnot be accessible via filters, goal settings, or funnel settings.So, if a filter utilizes a particular query-string variable, and the variable is excluded,then the filter will break. This also holds true for goal settings and funnel settings.What parameters should you eliminate? Any parameter that does not provide in-sight into what the visitor is doing, or what the visitor is viewing, should beremoved. How will you know which query-string parameters to exclude and whichones to include? I've found the easiest way is to let Google Analytics collect somedata. Then use the Top Content report to identify all query-string parameters.Create a list of the parameters and check with your IT staff to learn what each onesmeans. This process is not easy, but it is important.

WarningIt is very common for web sites to use a query-string variable called a SessionID to identify each individual visitor. Session IDs are unique strings that mayappear in the query string of every page. A session ID will make every pa-geview unique because each session ID is unique. Session IDs should beeliminated from Google Analytics in the method described above.If every page comes through as unique, because of the unique session ID,every page will have only one pageview. Only when you aggregate the data,by removing the session ID from the query string, will you fix the problem.Some web sites add the session ID as a directory in the file path. In this case,an Advanced filter must be used to restructure the Request URI field. Pleasesee the section "Advanced filters" for more information.

As usual, changing the Exclude Query String Parameters setting will not affect datathat has already been processed by Google Analytics. Only data processed in thefuture will reflect this change.

WarningIt is against the Google Analytics Privacy Policy to store any personally iden-tifiable information in Google Analytics. If your web site uses query-stringparameters to pass personal information about your visitors, then that in-formation will be stored in Google Analytics, thus violating the privacy

Google Analytics 37

policy. You must exclude all query string variables that may containpersonally identifiable information.

Tracking Across Multiple DomainsGoogle Analytics has the ability to track visitors across multiple domains. Thisfunctionality is primarily used on web site that have a third-party shopping cart,but it can be used for other purposes. If your web site traverses multiple domains,then you will want to track your visitors as they move from one domain to another.If you do not track them across domains, then each visitor will appear as a newvisitor (i.e., a new person) each time they move from one of your web sites toanother.However, tracking across multiple domains should be implemented only if thereis some functional connection between the web sites. If there is no business rela-tionship between the sites, then there may not be a need to track visitors betweendomains. Only you can decide if you need to track visitors between multiple do-mains.Critical to cross-domain tracking is the concept of first-party cookies. First-partycookies are cookies whose domain is the same as the web site that the visitor iscurrently visiting. For example, cookies for a user visiting www.epikone.com[http://www.epikone.com] have a domain of epikone.com [http://epikone.com].Google Analytics uses first-party cookies. Therefore, the GATC on www.epi-kone.com [http://www.epikone.com] can interact only with cookies that have adomain of epikone.com [http://epikone.com]. If the visitor leaves www.epi-kone.com [http://www.epikone.com], as is the case when a web site uses a third-party shopping cart, then the tracking cookies cannot be accessed by the GATCon the shopping cart pages.How it worksWhen a visitor arrives at the web site for the first time, the Google AnalyticsTracking code sets a number of cookies that uniquely identify the visitor. No matterwhere the visitor goes on the web site, he can always be identified by the cookies.Things change if the visitor leaves the web site. The tracking cookies are first-partycookies, which means they can be used only by the web site that sets them. If thevisitor leaves the site to use a shopping cart located on a different domain, thenthe tracking cookies will no longer work. There needs to be some mechanism totransfer the cookies, along with the visitor, from one domain to another. This isshown in Figure 25.

Google Analytics 38

Google Analytics provides two functions to transfer the tracking cookies betweendomains: __utmLinker() and __utmLinkPost(). Both functions operate in the samemanner. They extract the tracking cookie values from the cookies and place thedata in the destination-page URL as query-string parameters. The tracking cookiesin Figure 25, colored green, are passed in the query string as the visitor movesfrom epikone.com [http://epikone.com] to cutroni.com [http://cutroni.com].The name of each tracking cookie is highlighted in orange.When the visitor lands on cutroni.com [http://cutroni.com], the GATC removesthe cookie values from the query string and resets the tracking cookies on cutro-ni.com [http://cutroni.com]. When the process is complete, the visitor has twosets of cookies with the same values. One set of cookies is for epikone.com [http://epikone.com], and one set is for cutroni.com [http://cutroni.com].There are two critical conditions that must be met for this technique to work: Both domains must have the GATC installed. The third-party domain must accept query-string parameters.If the third-party domain prohibits either of these conditions, then Google Ana-lytics does not track visitors from one domain to the other.

Figure 25. When a visitor moves from one domain to another, his tracking cookies mustmove with him

Google Analytics 39

ImplementationFirst, make sure the pages on both web sites have the GATC installed. If the track-ing code cannot be added to both web sites, then tracking will not work. In additionto adding the tracking code, the pages must be modified as follows:

_uacct="UA-xxxx-x";_udn="none";_ulink=1;urchinTracker();

The above modifications are necessary to change how the tracking code interactswith, and configures, the tracking cookies. The _udn variable determines the do-main for the tracking cookies. Normally Google Analytics uses the subdomain andprimary domain in the location bar of the browser for the cookie domain. By setting_udn to "none", the tracking code uses the entire hostname for the cookie domain.The _ulink variable is a switch that activates a security feature within the trackingcode. This feature creates a "key" that ensures that the tracking variables are setwith the same values on both domains. You can see the key in the URL. It is storedin a query-string parameter named _utmk.Once the tracking code has been modified and installed, __utmLinker() or__utmLinkPost() must be added to your web site. As mentioned above, these func-tions extract the tracking cookie values and add them to the destination URL asquery-string parameters.If the web site transfers the visitor between domains using standard anchor tags,then __utmLinker() must be added as follows:

Buy Now');

If a form is used to transfer the visitor between domains, then __utmLinkPost()must be added to the necessary forms. Modify all appropriate forms as follows:

__utmLinkPost() will change the form action, by adding query-string parametersto the value in the action attribute, when the visitor submits the form.It is important to note that you may need to tag the links and forms on both websites. Why? Every page on Web Site A and Web Site B should be considered asearch engine results page, and thus a starting point for a visitor's visit. However,

Google Analytics 40

if there is no chance that the visitor's visit will start on Web Site B, and then moveto Web Site A, then there is no need to change the links or forms on Web Site B.

Tip

Many people have been experimenting with DOM scripts to dynamicallycall __utmLinker() or __utmLinkPost() rather than manually add the func-tions to the HTML. While these scripts will work some of the time, successhas been inconsistent due to variations in the DOM from one browser toanother. Care should be taken when experimenting with this customization.

Once the tracking code has been modified, and __utmLinker() or __utmLinkPost() have been added, visitors will be tracked between domains. To aid in reporting,it is a good idea to add a filter that attaches the web site hostname to the requestURI. This makes it easier to identify common content on each domain. The settingsfor the filter are shown in Figure 26.

Tracking Across Multiple SubdomainsLike tracking across multiple domains, the primary issue with tracking acrossmultiple subdomains has to do with the cookie domain.

Figure 26. Advanced filter to add the hostname to the Request URI

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By default, the GATC includes the web site subdomain in the cookie domain. Thismeans that a cookie set by the GATC while the visitor is visiting one subdomaincannot be utilized by the GATC on a different subdomain. So, a visitor who visitsmultiple subdomains on a web site will receive a different set of tracking cookiesfor each subdomain. I've illustrated this issue in Table 5.

Table 5. How subdomains affect cookie domainsDomain Cookie Domain Can be accessed bysupport.foo.com[http://support.foo.com]

.support.foo.com[http://.support.foo.com]

support.foo.com [http://support.foo.com] only

secure.foo.com [http://secure.foo.com]

.secure.foo.com[http://.secure.foo.com]

.secure.foo.com [http://.secure.foo.com] only

To resolve this issue, the cookie domain must be consistent from one subdomainto another. The subdomain must be removed from the cookie domain. Once thesubdomain is removed, the cookie can be accessed by the GATC that appears onany subdomain, as illustrated in Table 6.

Table 6. Changing cookie domain enables tracking across different subdomainsDomain Cookie Domain Can be accessed bysupport.foo.com[http://support.foo.com]

.foo.com[http://.foo.com]

support.foo.com [http://support.foo.com] or se-cure.foo.com [http://secure.foo.com]

secure.foo.com[http://secure.foo.com]

.foo.com[http://.foo.com]

support.foo.com [http://support.foo.com] or se-cure.foo.com [http://secure.foo.com]

The tracking cookie domain can be changed using the _udn variable. In the defaultconfiguration, _udn is set to a value of "auto", causing the subdomain to be includedin the cookie domain._udn can be set to a specific value, which will in turn be used for the cookie domain.So, setting _udn to the web site's primary domain lets the tracking code access thecookies on various subdomains.

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ImplementationYou can configure Google Analytics to track visitors across multiple subdomainswith the following process: 1. Modify the tracking code to include _udn variable. 1. Apply a filter to clarify Google Analytics reports. 2. Segment traffic into multiple profiles for improved reporting (this step is

optional but recommended).Begin by modifying the GATC to include the _udn variable. Set this variable to theprimary domain for the web site:

_uacct = "UA-XXXXX-X";_udn = "primarydomain.com";urchinTracker();

Once the tracking code has been modified and installed, you have to add a filterto the appropriate profile (Figure 27). The filter will differentiate pages that appearon multiple subdomains. For example, the page index.html may appear on multiplesubdomains but will appear as index.html in the reports. Adding the hostname tothe Request URI will differentiate multiple versions of the same page.

Figure 27. An Advanced filter that concatenates the hostname and the Request URI

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The final step in configuring multiple subdomains is optional but recommended.It is a good idea to create a separate profile for each subdomain. This provides agreater level of reporting and more insight into visitor actions on each subdomain.

Warning

This filter, or any filter that modifies the Request URI field, will break thesite overlay report. The reason is that the Site Overlay report uses the RequestURI to identify which links in the Site Overlay report correspond to specificdata (like clicks and visits).

To create the additional profiles, use an Include filter (Figure 28) based on theHostname field. When complete, there should be one main profile that containssummary data for all subdomains and individual profiles for each subdomain.

Tracking Across Multiple Domains with Multiple SubdomainsTracking visitors across multiple primary domains, which contains multiple sub-domains, can be done. The key to a successful implementation is making sure theGoogle Analytics tracking cookies are set with the correct domain and that thecookies are passed between the primary domains. There are three steps to configurethis type of tracking:1. Modify tracking code on each subdomain and primary domain.

Figure 28. An Include filter used to create a profile for a specific subdomain

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2. Modify links and forms on both sites to use __utmLinker() or __utmLinkPost().

3. Add a filter to clarify the data within reports.Many of the GATC modifications for this configuration are similar to the settingsused in the multiple domains and multiple subdomain tracking. _udn is used toremove the subdomain from the cookie domain, thus making tracking across eachsubdomain possible. _ulink is used to trigger certain actions in the tracking codenecessary for cross-domain tracking. Table 7 lists how the tracking code shouldbe modified for each domain.

Table 7. The GATC configuration for a web site that uses multiple domains and multiplesubdomains

Site 1 hostnames Tracking codesecure.site1.com [http://secure.site1.com]

_uacct = "UA-XXXXX-X"; _uhash = "off"; _udn = "site1.com"; _ulink = 1; urchinTracker();

products.site1.com[http://products.site1.com]

Site 2 hostnames Tracking codesecure.site2.com [http://secure.site2.com]

_uacct = "UA-XXXXX-X"; _uhash = "off"; _udn = "site2.com"; _ulink = 1; urchinTracker();

support.site2.com[http://support.site2.com]

The primary difference is the _uhash variable. _uhash creates a unique hash (ornumerical representation) of the domain name. This number is then placed in thetracking cookies.Originally _uhash was created to speed the processing of profile data and to ensurethe integrity of the tracking cookies. With improvements to the tracking code andthe server architecture of Google Analytics, _uhash has become antiquated. How-ever, it must be set appropriately for tracking to work.

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In addition to changing the tracking code, each site must be modified to use__utmLinker() or __utmLinkPost(). Remember, these functions pass the GoogleAnalytics tracking cookies between the domains via the query string. If the cookiesare not passed between domains, then the visitor's session will not be trackedbetween web sites.Finally, to help clarify the data in your reports, use an Advanced custom filter toattach the hostname to the Request URI.To aid in analysis, it is wise to create separate profiles for each primary domainand/or each subdomain. Do this by using a simple Include filter based on theHostname field. Sample filters can be found in Figure 25 and Figure 26.Theoretically, there is no limit to the number of primary domains or subdomainsthat you can track a visitor across. However it may be impractical to track acrossmore than two or three primary domains.Frames and IframesYou can use Google Analytics on sites that have frames. However, take care duringinstallation and configuration. The most common problem with sites that useframes is that the original referral information can become distorted. This can leadto problems when tracking online marketing.When implementing Google Analytics on a site that uses frames, make sure thatboth the frameset and the pages within the frame are tagged with the Google An-alytics tracking code. If both pages are not tagged, then Google Analytics does nottrack referral information correctly.A side effect of tagging both the frameset page and the pages within the framesetis that there will be an artificially high number of pageviews for some pages, spe-cifically the frameset. If the frameset page is not critical (i.e., it is simply a navigationmenu or page header), then consider removing it from the profile using an Excludefilter.

WarningIf your web site uses frames, and the number of pageviews is a critical metricfor your business, be sure you filter your profile data appropriately to insureaccurate metrics.

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iframesThe effect of iframes on Google Analytics tracking is similar to that of standardframes. If the outer page is not tagged with the Google Analytics tracking code,then the original referral information will be lost.Another common issue with iframes is the use of third-party shopping carts. If youare using a third-party shopping cart and embedding the shopping cart pages inan iframe, it will be impossible to track the visitor session from originating website to the shopping cart web site. The reason is __utmLinker() and __utmLinkPost() functions cannot be used in the SRC attribute of an iframe.

WarningSome have tried to pass the Google Analytics tracking cookies directly to thesource of an iframe. This technique will not work. When __utmLinker() or__utmLinkPost() execute, they create a hash, or key, based on the values ofthe cookies. The hash is sent to the third-party domain and used to checkthe accuracy of the values.

Marketing Campaign TrackingAnother important part of setting up Google Analytics correctly is the identifica-tion of all URLs used in online marketing. Unlike other configuration steps,marketing campaign tracking is not done in the Google Analytics administrativeinterface or on your web site. Marketing campaign tracking involves changing thelinks used in your marketing activities. I'll discuss this more in a moment.The reason why marketing campaign tracking is so important is that, by default,Google Analytics places your visitors in three basic referral segments:organic

Visitors who click on a search engine results pagereferral

Visitors who click on a link on some other web sitedirect

Visitors who go directly to your web site by typing the URL into their browsersWhile these segments are useful, they do not identify paid marketing activities.You want to measure paid marketing activities so you can better understand ifthey're successful! This can only be done with via marketing campaign tracking.

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How It WorksMarketing campaign tracking is based on the process of link tagging, which isadding extra information to the destination URLs used in your online marketingactivities. The extra information is actually a number of query-string parametersthat describe the marketing activity. I've illustrated how link tagging identifies yourmarketing activity in Figure 29.It all begins with the ad that the visitor sees (step #1). In this example the ad is apaid search ad on Google AdWords. When the user clicks on the ad, she is sent toa destination URL. Within the destination URL there are additional query-stringparameters that Google Analytics uses to identify the ad (step #2).When the visitor arrives on the web site landing page, the urchinTracker() functionbegins to execute. It examines the URL in the location bar and identifies the query-string parameters that identify the URL as a campaign URL. urchinTracker()extracts the query-string parameters (step #3). Then it splits the query-string pa-rameters into their name-value pairs, reformats them (step #4), and finally storesthem in the __utmz cookie (step #5). Because the values are now stored in a cookie,

Figure 29. How link tagging works

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any actions that the visitor performs can be linked to the ad that drove them to thesite.Let's dig a bit deeper and learn about the specific query-string parameters used inlink tagging. Table 8 shows how the tagged link in Figure 29 was created.Table 8. A destination URL before and after link taggingLink Before Tagging Link After Tagginghttp://www.google.com/analytics/

http://www.google.com/analytics/?utm_source=google&utm_medium=CPC&utm_campaign=en&utm_term=google%20analytics

Parsing the tagged link above identifies the query-string parameters used for iden-tifying the ad. Table 9 identifies each parameter and value.Table 9. The name-value pairs extracted from a destination URLParameter Valueutm_source Googleutm_medium CPCutm_campaign Enutm_term google%20analytics

Now I'll describe what each parameter actually represents:utm_campaign

The name of the marketing campaign. Think of this as a bucket. It holds all ofthe marketing activities in some bigger effort. For example, buying some key-words on Google, running some banner ads, and sending out an email blastmay all be part of the marketing plan for some type of sale. These three activities,which are all part of the same campaign, can be grouped together for easy re-porting.

utm_medium

The medium is the mechanism, or how the message is delivered to the recipient.Some popular mediums are email, banner, and cost-per-click (CPC).

utm_source

Think of the source as the "who." With whom are you partnering to distributethe message? If you're tagging CPC links, the source may be Google, Yahoo!,

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or MSN. If you're using banner ads, the source could be the name of the website where the banner ad is displayed.

utm_term

The search term or keyword that the visitor entered into the search engine. Thisvalue is automatically set for organic links but must be set for CPC links.

utm_content

The version of the ad. This is used for A/B testing. You can identify two versionsof the same ad using this variable. This parameter is not included in Fig-ure 29.

It's important to note that not all parameters are required. The core parametersare utm_campaign, utm_source, and utm_medium. These three should always be usedwhen tagging a marketing link. utm_term should be used for tracking paid searchadvertising, and utm_content can be used for A/B testing advertising.You determine the value for each parameter. In reality it does not matter whatvalue you use. Whatever data you do use will appear in Google Analytics. However,it is important to follow some basic guidelines: Keep the value short. Use alphanumeric characters and avoid white spaces. Make sure the value is understandable to you, or to whoever use these reports.The value of each parameter will be imported directly into the Google Analyticsreports. This is very powerful. Google Analytics is importing information that isspecific to your business, like the name of a marketing campaign, and segmentingyour data based on the values. It displays the data, exactly as it appears in thequery-string parameter, in a series of reports. These reports segment web site trafficand conversions, thus providing insight into which marketing activities are work-ing. Figure 30, the Campaign Report, shows how Google Analytics segmentsvisitation data based on a marketing campaign.

Warning

All cost-per-click links that are not tagged will be categorized as "organic."This can artificially inflate organic traffic volume, leading to incorrect anal-ysis. If you're using Google AdWords it is highly recommended that AutoTagging be enabled. If other paid search systems are used, like Yahoo! SearchMarketing or Microsoft AdCenter then the destination URLs must be man-

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ually tagged. This is absolutely vital to configuring Google Analytics cor-rectly.

How to tag linksThe process of link tagging is simple. Start by identifying the marketing informa-tion to be placed in the query-string parameters. Specifically, you need to identifythe campaigns, mediums, sources, and potentially keyword and content values.Remember, the keyword parameter is used only for tracking search-based ads, andthe content parameter is used to identify different variations of an ad. I recommendusing some type of spreadsheet to organize the information.Once all the parameter values have been identified, modify the destination URLsto include the parameters and values. Place a question mark at the end of thedestination URL followed by the query-string parameter. Separate each name-val-ue pair using an ampersand (&).If the destination URL already has query-string parameters, simply add the GoogleAnalytics parameters at the end of the URL. Separate the Google Analytics pa-rameters from the existing parameters using an ampersand (&).

Warning

If your web site uses redirects on the landing pages, then there may be troublewith link tagging. The Google Analytics campaign tracking parameters mustbe present in the URL of the landing page. If the URL does not physically

Figure 30. The Campaign Report automatically segments site data based on theutm_campaign value

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contain the tracking parameters, then the visit will not be attributed to thecorrect ad.

Link tagging works for any destination URL. So, if you are sending out emails orusing banner ads, you should be tagging the destination URLs. In general, anytimeyou pay for advertising on the web, you should try to tag the URL used in the ad.

TipSome destinations URLs, especially those used in email marketing, can bevery long before the addition of the Google Analytics campaign tags. Onetrick is to create a custom URL on your web site and direct all traffic fromthe email to the custom URL. Then, when a visitor lands on the custom URL,you dynamically append the campaign tracking variables to the URL. Thiscan be done using application-level code or with a simple HTML METArefresh.More information about dynamically tagging campaign URLs can be foundin this Conversion University article:http://www.google.com/analytics/cu/tt_offline_campaigns.html

Understanding Conversion ReferralsVisitor campaign information is stored in the __utmz cookie on the visitor's ma-chine. This cookie not only stores campaign information, but also all referralinformation including organic referrals, marked campaign links, untagged referrallinks, and direct visits.Each time a visitor visits the web site, the urchinTracker() function updates the__utmz cookie with the appropriate campaign information. When the cookie isupdated, Google Analytics discards the previous campaign information. As a resultGoogle Analytics tracks only the current campaign information, not previouscampaign information.With that said, there is a hierarchy of data importance that Google Analytics ref-erences before it updates the __utmz cookie and overwrites the referral information.Remember Google Analytics buckets traffic in four basic ways:Campaigns

Links that are tagged with campaign informationReferrals

Visitors who click on an untagged link residing on a web pageGoogle Analytics 52

DirectVisitors who type the URL directly into the browser

OrganicVisitors who click on an organic search result

Here is how Google Analytics updates the campaign-tracking cookie based on thereferrer: Direct traffic is always overwritten by referrals, and organic and tagged links. Referral, and organic or tagged links always override existing campaign infor-

mation.For example, a user may visit a site via a tagged link in a newsletter. When thevisitor leaves the site, the campaign-tracking cookie persists for six months andindicates that the visitor arrived via the newsletter.The same visitor decides to come back to the site one day later and types the website URL into the browser. This is a direct visit. The campaign cookie will stillindicate that the visitor arrived via the newsletter because the second visit was adirect visit, and direct traffic does not overwrite existing campaign information.One day later, the visitor clicks on a tagged CPC link. The __utmz cookie is updatedto indicate the visitor clicked on a paid search link, and the visit is attributed tothe CPC link.

Note

The timeout value for the campaign cookie can be changed. By default it isset to six months. This value can be altered by changing the _ucto variablein the GATC.

Google Analytics can be configured to retain the original campaign data stored inthe __utmz cookie. To enable this feature, add an additional query-string parameterto a destination URL. The query-string parameter, utm_nooverride=1, will alertGoogle Analytics that the existing campaign information should be retained.While helpful, this technique does not prevent the GATC from updating the cam-paign cookie if a visitor arrives by organic search or untagged referral link. Thistechnique is helpful only in preventing tagged campaign links from overwritingprevious referral information.

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Tracking AdWordsThe Google Analytics and Google AdWords systems are connected. To take ad-vantage of the interconnectivity, the AdWords account must be linked to theAnalytics account. If the accounts are connected, there are two primary featuresthat become enabled: Auto Tagging and Apply Cost Data.Auto Tagging automates the link-tagging process that is usually used to track aCPC campaign. When Auto Tagging is enabled, as shown in Figure 31, GoogleAdWords automatically adds a query -string parameter to the destination URLthat identifies Google AdWords as the referring site. While this parameter is dif-ferent than the standard link-tagging parameters, it does the same thing. Thequery-string parameter is named gclid and contains a random value.The second benefit of linking an AdWords account to an Analytics account is theApply Cost Data feature (Figure 32). If you enable this option, Google Analyticsimports your AdWords cost data and uses it in ROI (Return On Investment) andother calculations. It is recommended that the Apply Cost Data feature be activatedas it provides an extremely powerful view of how AdWords ad campaigns areperforming.

Figure 31. The Auto Tagging feature is activated from the Account preferences sectionof the My Account tab

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WarningWhen Apply Cost Data is activated, cost data from the entire AdWordsaccount is applied to each profile in the Analytics account. Google Analyticsdoes not match the campaigns in AdWords to the profiles in Analytics. Thismeans if you are managing AdWords campaigns for multiple web sites andyou link the AdWords account to Analytics, cost data for the entire Ad-Words account is applied to each Analytics profile, even though the profilescontains data for a single web site. This is especially problematic if you tryto link a Client Center Ad