1. Custom Reports Tab

The first, and most obvious, change to custom reports is the location. Custom reports how has its own tab across the top of the page.

When you click on the Custom Reports tab you’ll be taken to the overview page. When you create and save reports this is where they will be stored. To create a new report, just click on the New Custom Report button at the top of the page.

You’ll also notice at the bottom of the page there that you can migrate any reports you made in the old version of Analytics to the new one. Note that when you migrate your reports they will not be available in the old version anymore.

2. Custom Report Filter

The most helpful update in my opinion is the custom report filter. In the old version any report you created applied to all the data in your account. Now, you can tell Analytics which data you want to include.

When you click on the drop down arrow on the filter box, a list opens up of all the dimensions. You can choose to exclude or include and dimension that meets the criteria you set.

For example, let’s say I only want to include data in my report that comes from Google or Bing. I can set a filter to include just these two sources in my report.

3. Report Tabs

There has been a small, but much welcomed tweak in how the custom report tabs work. In the old version of analytics you could create multiple tabs within your report, but you had to include the same dimension across each report tab. With the new version of Analytics you can now report on any dimension, on any tab in the report so you can have all the data you need in one place.

4. Report Types

There are two report types you can choose from when creating a custom report, Explorer and Flat Table. The Explorer report type is similar to the report layout in the old version of Analytics. You select your metrics, and then drill down into the metrics based on the dimensions you choose.

The flat table report allows you to analyze two dimensions side-by-side. You can also select up to 25 different metrics to include in the report.

The difference is you don’t have to drill down into one dimension to see the other. For example, if you wanted to compare the performance of a keyword by source you would need to click on the keyword to view the source performance. With the flat table report you can view the performance data for each keyword in each source all on one page.

5. Sharing Reports

The final update is in how custom reports are shared. In the new version of Analytics note that when you share a custom report the link will reflect the state of the report when you first created it. This means if you create a report and share it with a coworker, then go back in and edit the report a week later the link you shared will not include the edits.

Even though I’m still adjusting to the new Google Analytics, I have found all of these updates to be very helpful in creating custom reports. If any of you have comments or questions about the new Google Analytics custom reports please share them below!

This post will walk you through how to analyze mobile data to determine if you should start developing a mobile version of your website.

Where Do I Find Mobile Data?

Depending on the version of analytics you’re using, old or the new beta, the mobile data is in two slightly different locations.

You can also use filters to view mobile data. The older analytics has a Mobile Traffic default filter already set. In the beta version there is a Visits from iPhones default filter, but no Mobile filter yet. I’d check regularly though because I discover something new has been added to the beta analytics almost every day.

Since I just want to analyze organic mobile traffic, I created my own mobile organic advanced filter. To create this filter, have the Mobile dimension exactly match Yes (there’s no drop down menu yet in beta analytics so make sure the Yes is capitalized or else the filter won’t work). Then click Add ‘And’ statement and set the dimension Medium exactly matching organic. Your filter settings should look like the screenshot below.

Once you have this filter set up you are ready to begin analyzing your organic mobile data.

How Much of My Organic Traffic is From Mobile Devices?

To begin to get a picture of organic mobile traffic, I set my date range for the past year and went to the visitors overview page.

Mobile traffic for this client was steadily growing in 2010 but spiked up at the end of the year and have remained at higher levels throughout 2011. From May 2010 to May 2011 mobile traffic grew 266%. This chart also shows that organic mobile visits made up 4.56% of total for the year, but I want to know what percent of total organic visits mobile was responsible for.

To determine that I set both the organic mobile filter and the non-paid search traffic filter. This makes it easy to compare the mobile organic visits to the total organic visits.

I looked at the data over three time periods, 12 months, 6 months, and 3 months, to get a better idea of the growth of mobile over the past year.

Over time, the percent of organic visits coming from mobile phones has risen. In the last 3 months it made up 9% of total visits and will likely only continue to grow.

How Do Mobile Visitors Interact With My Website?

Now that I’ve determined my organic mobile traffic is growing, I want to see if these visitors interact with my site differently than users search on a computer. If you go back to the Mobile tab and scroll down you’ll see a breakdown of performance by each mobile device.

To determine if mobile visitor interaction was different than the average visitor interaction, I compared the average performance of organic visits to each mobile device. Using excel, I calculated the % difference of Pages / Visits, Avg. Time On Site, and Bounce Rate for each mobile device compared to the total organic averages.

I want to focus mainly on the performance of the iPad, iPhone, and Android as they make up over 92% of current mobile organic visits and have enough data to show some trends. The iPad has a slightly lower average pages per visit, but a higher than average time on site and slightly lower bounce rate.

Even though the iPad is classified as a mobile device, the experience of searching on an iPad is most similar to a desktop or laptop computer and should be treated as such. Where I begin to see some difference in visitor interaction is on the iPhone and the Android. Both have a much lower average pages per visit and a much higher bounce rate, showing a decrease in visitor interaction with our site. Although average time on site is slightly less than average on the iPhone, it’s 32% higher on the Android. This may seem like a good sign, but remember your site may load differently on a smart phone so check your site speed to see if mobile visitors are spending more time on your site looking at product or waiting for it to load.

How Well Does My Mobile Traffic Convert?

So now that you’ve looked a mobile traffic and site interaction, it’s time to determine how well your mobile traffic is converting. The client in this example is and Ecommerce client, so I want to look at revenue brought in by mobile devices. To view this data go to Conversions >> Ecommerce >> Overview (just Ecommerce >> Overview in non-beta).

The mobile conversion rate is 57% less than the average organic conversion rate, a sign that you might need to create a better mobile user experience. Mobile transactions and revenue make up about 3.5% of the total, but mobile visits over this 6 month were 7.8% of the total showing again that you aren’t getting as much out of these visitors as you could. What was surprising to me though was that the average transaction value was higher than the total organic average. This shows that mobile shoppers are just as willing to spend on your products as non-mobile shoppers.

Do I Need a Mobile Website?

To answer this question, let’s look at the data:

• Over the past year, mobile visits grew 266%.
• Mobile users view fewer than average pages per visit.
• Mobile users have a higher than average bounce rate.
• Mobile users have a 57% less than average conversion rate.

For this client it is definitely time to start developing a mobile version of their site. Analyze your mobile traffic and see what the data is telling you.

Alas, the fourth and final installment of the Analytics test series: the regular expression portion. While the Google Analytics test might seem daunting, with a little studying, it really isn’t bad at all. Yesterday, Jessica covered event tracking.  Previously Erin covered types of Google Analytics cookies and how they are each used to track activity.  In this post, we’ll cover the  different functions of each regular expression character, along with examples.

Regex can be a lot to take in but when it’s broken out in simplified form, it’s not half as bad as its reputation would lead you to believe.  (I hope you’ll agree!) The regex portion of the series will serve to help you through the questions on the test centered around regular expression.  Regex was a fairly small portion of the Analytics test, with maybe 3-4 questions, but is a pretty handy knowledge set to maintain, even if you aren’t worried about taking the test.

Regex can be used for several things within Google Analytics, such as:

1. Setting up goals funnels
2. Tracking equivalent pages
3. Filtering data within reports
4. Profile filters

Finally, without further adieu:

Regex Character Guide

Regex Wildcards

. is a wildcard for any single character.

• Act . matches Act 1, Act 2, Act 3, etc. but does not match Act 10
• Act .. matches Act 10, Act 11, Act, 12, etc.

Note:  If you want to use a period as an actual period, not as a wild card you’ll need to use a backslash before the period, as a qualifier.  You would want to do this if excluding an I.P. address in Analytics.   The same rules apply for question marks, which can also be used in regular expressions, as we’ll discuss later.

• U\.S\. matches U.S.
• 163\.212\.171\.123

? matches 0 or 1 of a previous item (Use / as discussed above, if using a question mark in the literal sense).

• 51? matches  5 or 51
• AB? matches A or AB

+ matches 1 or more of a previous character.

• 51+ matches 51, 511, 5111, 51111, etc.
• AB+ matches AB, ABB, ABBB, ABBBB, etc.

* matches 0 or more of previous item.

• 51* matches 5, 51, 511, 5111, 51111, etc.
• AB* matches A, AB, ABB, ABBB, ABBBB, etc.

{} quantifies the number of the previous item.

• 51{2} matches only 511 (the 2 means that there are two of the previous item, which was a 1)
• 51{1,3} matches 51, 511, 5111 but does not match 51 or 51111

Match Set

[] matches one item in a character set.

• [uU]\.[sS]\. matches u.s. and U.S.
• [1-9] matches any number between 1 and 9

^ negates the set.

• ^[uU] will not match u or U
• ^[1-9] will not match any number between 1 and 9

() allows you to group contents as an item, using | to separate grouped items.

• (U\.S\.|US| u\.s\.|us) matches U.S., US, u.s., or us

Regex Anchor String

Match a string of characters using ^ to start the series and $ to mark the end of a string

• ^US matches ‘US Holiday’ but does not match ‘Monday is a US Holiday’ because it does not start with ‘US’.
• Holiday$ matches ‘US Holiday’ but does not match ‘US Holiday Dates’ because it does not end in ‘Holiday’.
• ^US Holiday$ only matches US Holiday

Regex Shorthand:

\d matches any number just like [0-9]

\s matches any white space

\w matches any number, letter, or underscore like [A-Za-z0-9_]

Now let’s try something a little more comprehensive.

What does \d{1-5}\s\w* match?

1. 1234 Johnson
2. Johnson
3. 132344 Johnson
4. 123

If you guessed ‘a’ or ‘d’ then you are correct.  Here’s why:

\d means that there is a string of numbers, which knocks out choice b, and {1-5} means that it can only have from one to five characters long, which knocks out choice c.  These are the kinds of questions that you will need to prepare for. \s represents the space and \w represents the word ‘Johnson’, however, as we know * means there could be an infinite number of  \w, which matches any character, or it can mean that the last character may not be present, so choice ‘d’ works also.

Although it isn’t an extensive part of the test, be prepared to answer a couple questions on regex when you’re taking the Analytics test.  Much like this last question, you will need to be able to identify what a sequence of characters and numbers will match.  As I said earlier, it is helpful to have a guide so that when you are setting up profiles, filters, goals, etc. you can be sure to do it correctly, without accidentally skewing your data.

For more tips on regular expression, check out the Analytics help section.

I’d love to hear any tips, tricks, or questions!

Why should we care about event tracking?

Because. Event tracking can give you lots of power.  Experienced online marketers understand that the more data you have about what’s going on in your accounts/on your site the more decision-making power you obtain. So if you have site elements that can’t be tracked with standard Analytics goal tracking, you should consider event tracking as a means to gather data about how people are interacting with them and the value you’re receiving from them. Examples of these elements include:

• Any Flash-driven element, like a Flash website, or a Flash Movie player
• Embedded AJAX page elements
• Page gadgets
• File downloads
• Load times for data

Let’s take a moment to discuss the two basic ways that you can modify your Analytics code to measure goal metrics aside from the URL destination, time on site, and pages/visit goals that are trackable with the “standard” Analytics code. The first is the virtual pageview (_trackPageview())method. This allows you to track clicks on elements of your site that don’t actually lead to new pages loading, but isn’t as flexible as event tracking and has some downfalls like making other reporting in your Analytics account more complicated (as virtual pageviews are combined with actual pageview count stats). Generally, unless you have specific goals that require virtual pageview tracking, using the event tracking (_trackEvent()) method can get you more detailed, more-segmentable information.

(Aside: Making your flash and silverlight content Analytics-event-tracking-friendly can be somewhat more complicated than the creation of the tracking code; this isn’t our concern for the test (there were no questions about this issue in our experience) but I’m just telling you because it’s important to know.)

The event tracking method is fairly flexible in terms of accommodating the structure in which you would like to track your events. The code looks as such: _trackEvent(category, action, optional_label, optional_value) where category and action are always required, and the others are available to give you more segmentation ability. Category, Action, and Label are customizable as you’d like, and value is a numeric value you can determine for that action. Before you start event tracking, it’d behoove you to write out the eventual “ideal” structure for your tracked events to ensure that you don’t name a category “Videos” if you would have been better off naming it “Kids’ Videos” since you have 50 video types on your site.  Same for action, for example, if you consider a video being “50% Watched” differently than one “80% Watched”.

Unlike virtual pageviews, event tracking implementation won’t skew your pageview results, however, it’s helpful to understand the differences in event tracking reporting between total events (I like a video and watch it 9 times and I have 9 total events) and unique events (each event is only counted as one per visit no matter how many times it happens).

Event tracking on the Google Analytics IQ Test

If code implementation confuses you, that is pretty okay. As a marketer, you need to understand the principles of what you can DO with event tracking, but the Analytics IQ Test does not cover its implementation in extreme detail.

On the test itself, most of the event tracking questions (our team seems to each have had 3-4) focus on knowing what types of site elements you would use it for, the general requirements you must have in the code as described above, and understanding the ways in which virtual pageviews and events differ and which you should use.

I didn’t get any questions about which version of the code you need to be using (ga.js, not urchin.js) but it’s worth knowing. If you’ve experienced any additional/different event tracking question types on the Google Analytics IQ Test, let us know!

While the Google Analytics test might seem daunting, I am here to say it isn’t nearly as difficult as one might think, as long as you do your homework. Yesterday, Steve covered the different types of Google Analytics cookies and how they are each used to track activity.  Now that you understand how Google Analytics tracks information with a user, let’s go through how e-commerce data is passed into Google Analytics.

If your site sells any sort of products or services online, you should have e-commerce tracking in place. If not, it is time to start! E-commerce reports will allow you to track your site’s transactions, revenue, products purchased, e-commerce conversion rate, the number of times people visited your site before purchasing, etc. If you have your account synced with your Google AdWords account, you can also review revenue data by keyword to help with PPC optimizations. The e-commerce tab appears on almost every data page within Google Analytics, so you can break down revenue in just about any way imaginable.
There are three steps to e-commerce tracking:

Step 1: Enable e-commerce reporting in your website profile.

To do this, go to the edit profile information page and then choose edit in the Main Website Profile Information section. From there, change the “E-commerce Website” option to read “Yes”

Step 2: Add the Google Analytics tracking code to the receipt page.

Make sure the same Analytics code that is on the rest of the site is added onto the receipt page. You will need to make a few modifications to the code, which is covered in Step 3.

Step 3: Add tracking into the Google Analytics code, in order to correctly track each transaction.

For this, you will first use the _addTrans() method which signifies a transaction has occurred. From there you will also have to add arguments, that provide details such as order ID, total order amount, and the amount of tax charged.  Your code will need to dynamically retrieve the values from your merchant software in order to populate the correct information. Below are the specific arguments to fill in, the ones with an * are required.

Order ID*, Affiliation, Total*, Tax, Shipping, City, State, Country

Let’s look at an example from the Google Analytics learning center:

The other piece shown above, is the _addItem method, which helps pass along the details on each item a visitor purchases. If they purchase more than one item, this will be called multiple times. Again, there are arguments that need to be added in order to pass along the details. The arguments with an * are required. Note that the Order ID should be the same as in _addTrans.

Order ID*, SKU/Code*, Product Name, Category, Price*, Quantity

Finally, at the end you will need to call _trackTrans() in order to send all of the information to Google Analytics. This is highlighted in yellow above.

For most e-commerce sites, the actual checkout process takes place on a separate domain or subdomain. In this case, you need to also add code based on the steps for tracking activity across domains and subdomains. I won’t go into that portion here, but you can watch the step-by-step video if this applies to you.

Now that you understand how to do the e-commerce tracking, how does this apply to the Google Analytics IQ exam?

While this wasn’t an expansive area of the exam, there were certainly questions that asked you to review the code provided and determine if it was correct or not based on what the question is looking to track. It is important to understand all of the different elements or arguments that are tracked and which arguments go with each of the call methods.

It is also important to remember, that the standard Google Analytics tracking code automatically knows when an https protocol is being used, so there is nothing for you to modify as your site moves between http and https.

To learn more about e-commerce tracking, visit to Google Analytics IQ Lessons to get more detailed step-by-step instructions.

Cookie Monster has said on numerous occasions that C is for cookie and that’s good enough for him. But as Internet marketers, we know that cookies are so much more than that! As a matter of fact, they are one of the most important concepts covered in the Google Analytics IQ Test and you have to understand them if you want to pass the test. Fortunately, cookies are also one of the easiest concepts in the test to learn about and understand.

In order to pass the Google Analytics IQ Test, you need to score at least an 80%. There are a total of 70 questions on the exam, so you need to correctly answer at least 56 questions. Based on what I’ve personally observed, you can expect that about 15% of any given exam will pertain to cookies, so it’s worth spending some extra time studying them. I’ve organized the rest of this post into two sections: what you need to know about cookies and what you can expect on the exam.

What You Need To Know About Cookies

Google Analytics generates data about users who visit your site through pieces of text exchanged between browsers and servers called cookies. They are established when someone enters your site and analytics data is generated when they click off of your site. Google Analytics cookies are first-party cookies, which means that they are established by your website. This allows the data generated by cookies to only be viewable by the website owner.

There are four cookies that Google Analytics establishes, _utma, _utmb, _utmc, and _utmz. (If you establish custom variables to define additional segments of data, you will use the custom variable cookie, _utmv as well) Let’s look at these cookies individually. Keep in mind that the most important things to remember for the test are the names of the cookies, how long they last, and what triggers their expiration.

_utma: This cookie identifies unique visitors by assigning them a unique ID upon their first visit to your site. It will last 2 years so long as the user does not delete it. At the 2-year mark, it will expire.

_utmb: This cookie generates data about a visitor’s session. It establishes or updates a session every time a page of a site is visited. If it is not updated, it expires by default after 30 minutes.

_utmc: This cookie works hand in hand with _utmb. It expires only when a user quits a browser.

_utmz: This cookie determines what brought a user to your site. This cookie tracks things like PPC ads, organic search, and referring links. It also tracks page navigation so that you can see how visitors move through your site. This cookie expires after 6 months unless it is updated. Updates occur when a page view initiates.

Each cookie contains unique code. For example, the _utma cookie looks like this:

_utma= 17334963. 432099211. 1115376676. 1115376676. 1115376676. 1

The first number set is the domain hash and is followed by the random unique ID, the time of the initial visit, the beginning of the previous session, the beginning of the current session, and the session counter. It’s important to note the time of the initial visit can be the same as the beginning of the previous session and/or the beginning of the current session if the visitor is there for the first time or they deleted the _utma cookie in the past. These times are set in UNIX, which is seconds since January 1, 1970.

The _utmz cookie is organized slightly differently and allows you to generate a wonderful code to track your PPC activity. It begins with the domain has, but is followed by a timestamp instead of the code contained in the _utma cookie. It also contains a session number, a campaign number, and source values for things like PPC tracking (which you use in a PPC destination URL). It will look something like this:

_utmz= 17334963. 1115376676. 2. 3.utmcsr=google|utmccn=(campaign)|utmcmd=ppc|utmctr=keyword

If you have additional questions about cookies, feel free to comment on this post or visit Google’s page on cookies in Analytics.

What to Expect on the Exam

Once you understand the ins and outs of cookies, you’re technically ready for questions dealing with them on the IQ test. Sometimes it’s helpful to understand what kinds of questions may be asked about particular topics. Every IQ test question I’ve ever seen about cookies has been a multiple-choice question dealing with one of four things; the names of the cookies, when they expire, what they track, and how they are organized.

Names of Cookies:

You can expect a question or two about the names of the cookies. A common one I’ve seen is, “What are the four kinds of cookies used by Google Analytics?” The answers are, of course, _utma, _utmb, _utmc, and _utmz, but occasionally you’ll see answers like _utmd and _utme.

Expiration:

In my experience, common questions about the expiration of cookies have dealt with multiple cookies at once. For instance, a question you might see asks which will not expire if there is a new page view while they are active? The answer to this is _utmb and _utmz.

What Cookies Track:

The most common cookie questions address what each cookie tracks. For example one question might ask “The _utma cookie tracks what aspect of a visitor’s activity?” Answers could include what brings a user to a particular page on your site and when a user quits their browser, but the correct answer is that it assigns visitors a unique ID.

How Cookies are Organized:

I’ve only ever seen questions about the different components of the _utma cookie, but I recommend reviewing the slides on the Conversion University site to understand each component of each cookie. I’d also suggest having this page open when you take the exam.

In the next few days, some of my colleagues will discuss other key aspects of preparing for the Analytics exam. Stay tuned to PPC Hero or SEO Boy for these helpful tips and as with any of the concepts we discuss on our blogs, leave us comments if you have any questions. Good luck!

Make sure to check out the rest of the series:

Part 2: E-commerce Tracking

Part 3: Event Tracking & Virtual Page Views

Part 4: Regular Expression (Regex) Character Guide

Most of us probably won’t be found running away from a giant stone ball rolling after us anytime soon, but that doesn’t mean that we can’t be archaeologists. You see, thanks to Google’s In-Page Analytics tool, anyone can become an SEO archaeologist! This unique and powerful tool can give you a look into the past and help you understand how your site’s visitors have interacted with your pages.  It can help answer troubling questions about where people are clicking on your website or what the most popular features of your webpages are. Before you can start tackling these questions however, you have to know what you are doing.

To begin using In-Page Analytics, start by logging into your Google Analytics account and click on the site profile you’d like to work with.  It’s important that you already have Google Analytics tracking installed on all of your pages, or you won’t find this tool helpful.  Furthermore you need have had Google Analytics running on your site long enough to accumulate an adequate amount of data. 45 days is generally enough time.

The next step is to click on Content in the left sidebar. This will bring you to your Content Overview page and make an In-Page Analytics option appear in the left sidebar. Click In-Page Analytics in the sidebar to continue.

You will automatically be taken to an analysis of your site’s default home page. The left sidebar contains a summary of all the pertinent information regarding the specific page. All of these statistics can be found in other parts of Google Analytics, but they are much easier to reference thanks to the side bar.

The main part of the In-Page Analytics tool is the live look into your website with overlays on click data. Google Analytics displays your site as if you were looking at it by itself and places bubbles on each part of the site where people have clicked to go a particular page.  As you can see, the Home button on SEO Boy is one of the most popular parts in terms of clicks on the page.

You can also filter the data in the bubbles on different factors like geographic region, operating system, and the keywords that brought them to the site.  Generally your site needs a high level of traffic to really get the most out of your filters and make sound decisions on about improving your site.

Google Analytics summarizes these percentages under the Outbound Destinations section of the left sidebar.  I really like this feature because it eliminates some of the scrolling and manual scanning for click data.  I’ve found that In-Page Analytics bubbles, though helpful, can sometimes be difficult to spot on a page.

Additionally, In-Page Analytics features a ribbon that informs you of the percentage of clicks you get below the “fold” of your website.  Most people will find that the majority of their clicks occur above the fold.  However, if you see an extreme disproportion in the number of clicks above and below the fold, it may indicate that you need to make some changes to your page.

As far taking this data and making a decision with it, you will need to define an objective for your page. Perhaps you operate a blog and you want to encourage visitors to keep reading other articles you have posted. You can use In-Page Analytics to see if they are finding the links to read additional posts easily or if they are navigating to an unrelated page instead.  This will help you decide whether you should relocate those links to another part  of the page or better explain what the irrelevant links do. You might operate a site with a specific conversion like downloading a PDF. In-Page Analytics can help you determine whether visitors are clicking the download button or whether they are clicking on something else.  Fortunately, you can create a filter to within In-Page Analytics to display goal conversions instead of clicks. Upon establishing your objective, you will then analyze the data that directly pertains to it.If you find that you are falling short of where you would like to be, i.e. visitors aren’t navigating your pages the way you intended, it may indicate that you need to make a change.

Just like archaeology, In-Page Analytics is all about past behavior. Although it doesn’t necessarily indicate exactly how people will behave on your website going forward, it will help you learn about historical performance. Use this to answer your questions about how you have done in the past. You may find a treasure that will help you prepare for the future.

Unfortunately that traffic might not the kind you want as a site owner. This happened to me recently when I had a picture in a blog post on my personal site generating a lot of search traffic. The picture was a Delta airplane that I had tagged with all of the right alt text and Meta descriptions. The image had been indexed by Google and had started to show up on the first page of image search results. The users that were searching for images of Delta airplanes would click on the picture and Google Analytics would count it as a visit to my site. This generated both a spike in traffic and a high bounce rate because those users were only interested in finding an image, not engaging with my site’s content. I was able to determine this by first recognizing that the page with the image was getting a lot of traffic. Then I examined the source of the traffic; the bulk of which was organic search traffic. Finally I looked at the keywords that most people were using to arrive at that page and found that they were using terms such as “delta airplane pictures” or “pictures of Delta airlines.”

As a site owner, it can be very frustrating to get these kinds of traffic spikes that convolve your Analytics data. Fortunately there are several options; adding a robots.txt file to prevent image indexing, adding a filter in Google Analytics, and editing the meta data on a particular page to prevent images from being indexed.  Generally speaking, I don’t recommend using a robots.txt file or Meta data edits as solutions to this problem. There may be some people who come to your site through an image search and find something on your site that piques their interest.  Even if these types of visitors are few and far between, they are quality visitors nonetheless and provide an opportunity for engagement. Consequently, I’ll show you how to filter out these kinds of image searches in Google Analytics.

Whenever you apply a filter, you always want to make sure that you have an unfiltered profile to collect raw data. Without this, you are unable to get an accurate snapshot of your website’s Analytics data. Remember, Google Analytics cannot reprocess raw data into its original form once it has been filtered.

To begin applying a filter for image searches, click Add Website Profile, just below the list of existing profiles in your analytics account. On the next screen make sure that “Add a Profile for an existing domain” is selected and fill out the additional information boxes for the profile.

Make sure you name the profile properly to indicate that you are using it to filter out image searches. Click finish when you are done naming the profile.

Next, click “edit” in the same row as the new profile on your account home page.

On the next screen click “Add Filter” on the Filters Applied to Profile Bar.

Next name the filter and select custom filter.  Next make sure that Exclude is selected (default option) and select “referral” from the filter field drop down menu. In the filter pattern box, you will need to insert a regular expression. To filter out image searches from Google use ^http:\/\/images\.google\..*$ and for Bing use ^http:\/\/www\.bing\.com\/images\/..*$ Once you have created a new filter, click save changes.

You will need to create separate filters for Bing and Google. It can take up to 24 hours for the applied filters to giving you data in the new profile.  Thanks to these new filters we can now get clean traffic data without our statistics being skewed by low quality image searches.

Google Analytics is the best tool to gather performance data for your SEO accounts. If you aren’t familiar with Analytics it can be overwhelming at first. The first time I logged into an analytics report I think I just sat and stared for a few hours. There is a plethora of data but if you know how to manipulate it you can get almost any information any way you want it utilizing their custom reports feature.

I’m going to go through some basics of custom reports and then walk through the step-by-step process of building a custom report. After reading this post you will know how to create your own custom reports to analyze and improve search engine performance.

CUSTOM REPORT BASICS

Before we jump into the step-by-step process of building custom reports, I want to go over the two basic components of Google Analytics reports:

Metrics – A metric is a qualitative measure of how visitors interact with your site. They represent the columns in your report. You can select from five different groups of metrics: Site Usage, Content, Goals, E-Commerce, and Advertising.

Dimensions – A dimension is a characteristic of a visitor or a page on your site that you can use to organize your metrics.  They represent the rows in your report. You can include up to five dimensions in each report, one as the main dimension you want to measure and four sub dimensions to further drill down.

In the sample traffic source report below you are looking at the metrics Visits, Pages/Visit, Avg. Time on Site, % New Visits, and Bounce Rate for the dimension Source/Medium. Each different Source/Medium value is an individual row in the report.

It is best to have an outline of what you want included in your report before you start building. However, there are restrictions that come into play when combining metrics and dimensions. Google’s help page provides an outline of valid dimension-metric combinations. Once you have an outline of the data you want to show in your report it is time to start building your customized report.

BUILDING YOUR REPORT

I want to create a report analyzing the keyword performance for the different pages of my website. I have already created an outline of the dimensions and metrics I want to include in my report. The main dimension I am focusing on is Page Title and I want to drill down to the Keyword sub dimension. I’m going to measure performance by looking at Visits, Avg. Time on Site, Avg. Time on Page, Pages/Visit, Bounce Rate, and % Exit.

Step 1: Create a Custom Report

To create a new custom report click on “Custom Reporting” in the left-hand column. When the box expands, click on “Manage Custom Reports”. Once on the Manage Custom Reports page simply click “Create new custom report” in the top right corner of the page.

Step 2: Name the Report

The first thing you’ll want to do when you get to the Create Custom Report Page is to name your report. Give your report a simple yet easily identifiable title. Different people may be creating reports in the same account and you want to be able to easily identify your report from the list of saved reports. Click “edit” next to the title field, type in your custom title, and hit “apply”. I’m naming my report “Keyword Performance by Page”.

Step 3: Select Your Dimensions

To select your dimensions all you do is drag and drop each dimension into its appropriate slot. Google has made this easy for us as it has color coded where the dimensions and metrics should be placed.  I decided to add one more dimension to my report, Source, so I can view the referring source of each keyword.

Step 4: Select Your Metrics

After you select your dimensions, Google Analytics automatically grays out the invalid metric pairs. This way you don’t have to continually refer back to the valid pairs chart on Google Help. Drag and drop your metrics just as you did for dimensions.

Step 5: Preview Your Report

You can preview your report at any time during the creation process. Just click “Preview Report” and a new window will pop up showing how your report will look. Spend time clicking through it and see if there are any changes you want to make to your report data or structure.

Step 6: Create Your Report

Once you are satisfied with your report click on “Create Report” at the bottom of the page.  I now have a report showing my selected metrics for all pages on my website. If a click on one of my pages it takes me to a list of the keywords users entered that brought them that page. By clicking once more it breaks it down even further to the referring sources for that particular keyword.

Something important to note, if you have an account that utilizes both SEO as well as PPC you’ll want to filter out the PPC traffic. To view just the results from SEO traffic you can use Google Analytics default segments. Click on the drop down box next to Advanced Segments in the upper right-hand corner of the page. Deselect “All Visits” and select “Non-paid Search Traffic”.

Click “Apply” and now your report in only showing visits resulting from SEO traffic.

Step 7: Manage Your Report

Once you create your report it will be saved to the Manage Custom Report list. From here you can go back and edit your report at any time. If you find that it doesn’t quite provide the information you are looking for you can add, delete, or rearrange any of the metrics or dimensions. You can also share a link to your report if you are managing an account for a client or you can delete your report all together.

Conclusion

I believe the best way to learn is it just get in there and do it. Play around with different dimension and metric combinations. Get creative and see if you can uncover a new way to look at the data. If you have any questions on creating reports or any reports you find to be most helpful in SEO analysis please post below!

The other day, one of my more hands-on clients forwarded me this article about monitoring rankings using advanced filters. After reading it I knew that I would be able to use this for all of my clients, and I started telling my colleagues about how helpful it would be for everyone to use. However, I forgot one important detail. Not all of us know about filters, let alone advanced filters. I also realized that we haven’t really implemented very many “User Defined” segments, so all in all, we’d have to start with those steps first before we truly appreciated the “golden nugget” that had been uncovered.

What are Google Analytics filters & how are they used?

There are two types of filters, predefined and advanced. Google describes them as:

• Predefined Filters: Predefined filters are a quick and easy way to accomplish some of the most common filtering tasks.
• Custom Filters: Custom filters allow more advanced manipulation of data.

So what does this mean for you, and how are they set up?

Note: When working within an account it is a good idea to always have one profile without filters. It’s important to keep a “clean” profile, and create additional profiles that contain segmented data. This will help ensure the integrity of your data in case a profile is deleted or a filter is set up incorrectly. Just imagine if you only tested on your main profile – you’d have no way of knowing what data was accurate, and if you deleted the profile, you’d have no way of getting historical data back.

Creating Predefined Filters

There are a few choices when creating a predefined filter, so you first have to decide what you want to exclude or include in your profile data. After you know what you want to track, log into your account, and choose the profile you will be working with (or create a new one). Once you are in the profile you can add/edit filters from there.

1. Exclude all traffic from a domain. Use this filter to block all traffic from a certain domain. Google help explains that Google Analytics uses reverse IP lookup to find the domain of your users, so this can come in handy for larger companies that have their IP addresses mapped to the domain instead of their ISPs.
2. Exclude all traffic from an IP address. We use this one all the time to block traffic from our IP address. This helps ensure that the testing I do for clients, and the visits I make to their site doesn’t skew the data. I know the level of traffic I generate isn’t all that much, but by excluding our IP the data is just that much cleaner, and who doesn’t like that?
3. Include only traffic to a subdirectory. This helps you track only the traffic that goes to a subdirectory of your site. This is particularly handy when you have a blog that you want to monitor traffic on. You would simply enter ^/blog/ if you want to track www.mysite.com/blog.

Creating Custom Filters

There are a variety of custom filters that you can create, each one giving you a deeper dive into the data that is captured in your Analytics account. When creating a custom filter, you first have to choose one of the following:

• Exclude
• Include
• Lowercase
• Uppercase
• Search and Replace
• Advanced

From there you will make a selection in the filter field that will help further segment your filter. There are a variety of choices ranging from Request URI, data from your campaigns (like keywords), eCommerce data, and visitor information. When paired with the choices above, you can see how quickly you will be able to create an immense number of filters to help you really hone in on certain parts of data.

User Defined Segments

If you checked out the article I referenced above, you’ll know that the other feature mentioned and used in the advanced filters they created were user defined segments. You may have seen these when viewing reports in Analytics, either on the top under advanced segments, or on the left hand side under Visitors. Either way, it’s important to understand that they are just one more powerful tool that we can use to get a better idea about who is visiting a website, and how one segment’s behavior may be different than another’s.

Still wondering why you should care? User-defined segments allow you to label your visitors based on the actions they take while on your site, such as sign up for your newsletter, fill out a contact form, or make a purchase. And considering the fact that all of these actions would lead us to believe they are probably more engaged than the average Joe, it would be really great if only we could see how they interact differently when it comes to other important metrics like time on site or pageviews, or even bounce rate.

Setting up User Defined Labels

Now don’t leave when I say this, but user-defined labels require a bit of extra code. But it’s not that bad, I promise! Firs you have to decide the name for your segment, such as “customer”, and you add a little snippet of code that helps identify that label when they reach the confirmation page. Check out this help section for specific details on adding the user-defined code. (Be sure to scroll down to the “technical notes” to make sure you understand all aspects before diving in.)

So now that you understand filters and user-defined labels, you can start to do some of the experimenting that Patrick discusses in the article above, and start gathering some pretty amazing data that will help you better optimize your accounts.

A friendly tip:When dealing with keyword reports or filters, you are bound to notice that there are visitors coming in through (not set), and it might be a fairly large number. This is because not all visitors come in from a keyword; some come in directly either by typing in your URL, or through a bookmark. You can verify that your filter is correct by calculating the percent of visitors that came in through (not set), and compare that to the percent of Direct visitors in the Traffic Sources report. They won’t be identical, but they should be fairly close.