Real-Time Insights Tools by Google

Search Trends and online activity are a window into the attitudes, perceptions and needs of your consumers. Google has brought together our fast, free insights tools to help you make sense of this data and start gaining valuable insight in real-time.

It’s the difference between guessing and knowing. To see how this tool can work for you, check out our demo Google Real-Time Insight Finder Demo Video.

The Limitations of Average Time on Page and Average Time on Site in Google Analytics

I’ve been told that I don’t have a very good poker face, and I’m ok with that.  I find it actually helps my clients, since they can tell almost immediately when I don’t agree with something or when a point needs more clarification.
For example, there are times I see marketers heavily focus on time-based metrics like Time on Page and Time on Site (as if those metrics were 100% accurate).  Unfortunately, this isn’t the case and the numbers reported can be far from accurate.


The confusion about how time metrics are calculated usually leads to some interesting conversations (as clients see various times listed in their reporting and want to start comparing pages and visitor segments.)  That’s hard to do, since there are serious limitations with the way time metrics are calculated in web analytics (for now anyway).  Even worse, people can subsequently make decisions based on the data.  This is why I tend to focus on key actions visitors can take versus other measurements of success.  I track these actions via conversion goals and events versus focusing heavily on time-based metrics.  I’m not saying that time-based metrics are useless, but they need to be taken with a grain of salt (at least until we find a more accurate way to track them).


For example, let’s say a marketing manager analyzed a new piece of content and was blown away with an Avg. Time on Page of 5:26.  This is where my (lack of a) poker face leads to a deeper conversation about how the actual Time on Page could be much different.  My comment is typically something like, “I think it’s great that visitors who decided to visit another page on your site spent 5:26 on the page.”  I often get a strange look at this point.  Then I start to explain that Time on Page is actually calculated by page jumps.  So, if someone reaches a certain page and exits from that page (or bounces), then Time on Page is 0:00.  No, that’s not a typo.  Time on Page is 0:00 whether they spent 10 seconds on the page or 10 minutes.  I’ll explain more about how time metrics are calculated below, but I wanted to give a quick example now so we’re on the same page.









Note, although I’m focusing on Time on Page and Time on Site in this post, there are ways to calculate time metrics like Elapsed Time for certain processes that occur on a page.  For example, check out my tutorial for using TimeTracker to calculate elapsed time (or Time to Complete).  It’s an interesting way to extend event tracking to record how long it takes to finish a process like completing an elaborate form.


Access To Data Can Be Powerful But Interpreting Data Incorrectly Can Be Dangerously Powerful
I never want to rain on anyone’s parade, but it can be dangerous to walk around your office telling everyone something that might not be entirely accurate (and can be way off).  When I start helping clients with analytics, I typically make a point to grab a conference room with a white board and start explaining how Time on Page and Time on Site are calculated.  After I explain how the metrics are calculated, there are times that I hear a level of skepticism, since there isn’t hard evidence that the metrics are really calculated this way.  And I’m totally cool with that. I’d be a hypocrite to say that being skeptical is wrong, since I test everything.
In online marketing, I don’t necessarily take anyone’s word without testing it myself.  Which brings me to this post…  Based on what I explained above, I decided to run an experiment to test Time on Page and Time on Site in Google Analytics.  My goal was to document the hard numbers so you can see how the metrics are reported.  I think it’s important to understand the limitations of the metrics, so you can provide context in your analysis.  If you provide context, then your clients can better understand the analysis you provide.


The G-Squared Lab


When setting up this experiment, my goal was to isolate directories and pages and then document the exact time that I spent while visiting those pages (and sets of pages).   If each visit was isolated, then I could drill into the reporting to see exactly how Google Analytics tracked time for each page and each visit.  I’ll detail the paths I took in the experiment below.


Quick Introduction: The Formulas for Time on Page and Time on Site:


Before we hop into the experiment, let’s start with a quick introduction to how Time on Page and Time on Site are calculated.  Both metrics are based on page jumps (or moving from one page to the next).  By calculating the difference between visiting one page and the next page during the visit (via the page timestamp), Google Analytics can determine how long you stayed on the preceding page.  Calculating Time on Page via page jumps is where a major limitation is exposed.


That’s because if you visit a page and then exit the site, the Time on Page is 0:00.  As mentioned earlier, that’s whether you spent 10 seconds or 10 minutes on the page.  On a similar note, if you visit just one page on the site and leave (which is a bounce), the Time on Page is also 0:00.  Time on Site is calculated by adding the time that a visitor spent on the site by combining the Time on Page for each page that was part of the visit.  Average Time on Site simply divides total time by number of visits and does not exclude bounces.  You can see the formulas below.


Time on Page A = Page B Time – Page A Time (based on the timestamp)


Average Time on Page = Total Time on Page A/(Pageviews – Exits)


Total Time on Site (For a Specific Visit) = Page A + Page B  (or the sum of Time on Page for each page in the calculation)


Average Time on Site = Total Time on Site/Visits (this metric includes bounces)


Example:

As a simple example, let’s say that someone visited Page A for 2:00, then visited Page B for 3 minutes, and then exited the site.  The Time on Page for Page A would be 2:00.  However, the Time on Page for Page B would be 0:00.  Google Analytics can identify the Time on Page for Page A only because there was a jump to Page B (and it calculates the difference in time between the two pages).  Since the visitor exited the page from Page B, there is no way for Google Analytics to know how much time they spent on the page.  The Time on Site would also be 2:00, since the time on Page B cannot be determined.  Based on this simple example, can you tell why Time on Page and Time on Site need to be taken with a grain of salt?  What if that visitor spent 7:00 on Page B?  You would think by just looking at the reporting that visitors were more engaged with Page A, when in reality, they were spending more time on Page B!


The Experiment: 5 Scenarios Plus a Bonus


For the experiment, I set up several directories with four pages in each directory.  All the pages within each directory are linked together via simple text links.  For each visit, I identified how many pages I would visit and how long I would spend on each page (give or take a few seconds).  Everything was documented so I could cross reference my notes to see if the reporting matched up.  I will list each scenario below and show you the reporting in Google Analytics.  Then you can see for yourself how the metrics are calculated.


Scenario 1: Bounce After 20 Seconds


For the first visit, I wanted to simply bounce, but after a short visit.  I visited the first page, stayed for just 20 seconds, and then exited the site.  I wanted to show you how the Time on Page for a bounce is reported as 0:00.  After checking the reporting, it surely was.





Scenario 2: Bounce After 5 Minutes


I’ve heard some confusion about what determines a bounce in Google Analytics.  For example, some people think that if you spend enough time on a page, it’s not considered a bounce.  So, I spent five minutes during the next visit, but stayed on the landing page.  Once again, the time on page was 0:00 and it was considered a bounce.  I just wanted to clarify that point.







Scenario 3: Land on Page A, Spend 1:17, Click Through to Page B, Spend 1:30 and Then Exit


For the third visit, I wanted to visit the first page in the directory (Page A), wait 1:17 and then click through to Page B.  I would spend another 1:30 on Page B and then exit the site.  Based on what I explained earlier, the Time on Page for Page A should be 1:17 and the Time on Page for Page B should be 0:00.  In addition, the Time on Site for this visit should only be 1:17, even though I actually spent close to 3:00.  The reporting confirmed this.






Scenario 4: Land on Page A, Spend 1:23, Click Through to Page B, Spend 0:58, Click Through to Page C, Then Exit

For the fourth visit, I wanted to visit Page A for 1:23, Page B for 0:58, then click through to Page C.  I would spend 1:00 on Page C and then exit.  The reporting should show a Time on Page of 1:23 for Page A, 0:58 for Page B, and then 0:00 for Page C (even though I spent over a minute on the final page).  Also, Time on Site should be 2:21, even though I spent 3:21 on the site.  The reporting confirms this.






Scenario 5: Combining Visits to Show Average Time on Site

I mentioned above that Average Time on Site (ATOS) includes bounces, so I wanted to show you how this looks when you combine multiple visits.  So, I visited the same set of pages three times.  The durations for each visit were 0:00 (a bounce), 1:11, and 2:14.  Based on the calculation for Average Time on Site, the bounce should be included and the ATOS should be 1:08.  The reporting confirmed this.





Bonus: The Virtual Pageview


I mentioned earlier that if you exit from a page (including a bounce), then Time on Page is 0:00.  But that’s not always the case.  Welcome to web analytics.  :)  There’s something called a virtual pageview that enables you to trigger a pageview, but in reality, a page wasn’t really loaded.  It’s a versatile feature in Google Analytics and can help you track clicks off your site, conversions that don’t require a page to load, and other clicks you want to track that don’t necessarily load pages.  You can also use a virtual pageview to track conversion goals (since you can use that virtual pageview as the destination URL for the conversion goal).  Again, you can read my blog post about conversion goals and events to learn more about this functionality.


The Virtual Pageview Reporting:


In this scenario, I visited one page and bounced.  However, I triggered a virtual pageview after staying for 1:28 (before I left the page).  So, does triggering a virtual pageview impact Time on Page?  It absolutely does (see the screenshot below) and it’s important to understand this when analyzing your reporting.  The bounce rate was 0% and Time on Page was 1:28.  If the virtual pageview was not triggered, the Time on Page would have been 0:00 and the bounce rate would have been 100%. Keep this in mind when analyzing your reporting.





Moving Forward With Time on Page and Time on Site


There you have it.  We’ve taken a detailed look at Time on Page and Time on Site in Google Analytics, while exposing some of the limitations with time-based metrics.  I hope the results from my experiment help you better understand how the metrics are actually calculated.  To be clear, I’m not saying to forget about Time on Page and Time on Site, but you just need to take the metrics with a grain of salt.  As you’ve seen in this post, the actual numbers may be off (and way off for certain situations).  This discrepancy makes it challenging to determine if the time reported was good or bad, which can definitely inhibit making solid decisions on the data.


Some key takeaways regarding Time on Page and Time on Site:

1. Don’t obsess over Time on Page or Time on Site.  Unfortunately, the metrics are flawed and can skew your analysis.  Keep the limitations in mind while analyzing site performance.
2. Time on Page does not include exits (or bounces), and can inaccurately report actual time on the page.  It can be much lower or much higher than reported…
3. Time on Site does include bounces, but still cannot determine the actual length of time spent on exit pages.  Therefore, this number can also be way off.
4. Virtual pageviews enable Google Analytics to calculate Time on Page, even for exits (from the time the virtual pageview is triggered).
5. Try and focus on key actions that visitors can take on the site (in the form of conversion goals and events.)  Then you can use time metrics to help support your findings (if it makes sense for the site in question).

Track Back : Search Engine Journal

Google Analytics Tracking for Adobe Flash

Google Analytics Tracking for Adobe Flash

The Google Analytics Tracking for Adobe Flash component makes it easy for you to implement Google Analytics in your Flash-driven content. This component, developed by Adobe Systems, Inc., contains all of the functionality of the Google Analytics Javascript code. The Flash Tracking component is a compiled tracking object native to ActionScript 3, making Analytics implementation intuitive in Flash, and Flex development environments.

Why Use Flash Tracking?

Without the Google Analytics Tracking for Adobe Flash component, tracking Adobe Flash content with Google Analytics involves a number of technical hurdles. First, you must develop a custom interface to ga.js so that your Flash application can execute the appropriate Analytics method, such as trackPageview() or trackEvent(). In addition, you must also anticipate whether your Flash content will have access to the browser Document Object Model (DOM), since tracking fails for those objects where access to the DOM is denied (typically when your content resides on 3rd party sites). This involves understanding how to use the ExternalInterface call in ActionScript 3 to access the browser DOM and to degrade when access is denied.

The Google Analytics for Adobe Flash component simplifies tracking your Flash content and handles DOM access gracefully. It is useful for a number of common tracking purposes in Flash, such as: :

• An embedded Flash widget on an HTML page


• A standalone Flex application or Flash-only site hosted on an HTML page


• A distributed Flex/Flash game or program where the developer has no control over where the widget will be placed

Keep in mind that tracking applications in Flash has some structural variations from tracking website pages. Familiarity with Analytics Tracking is essential to understanding how this plug-in works. You can also view the Design Documentation for this project for detailed information on how the Analytics Tracking model has been ported over for this component.

Note: Currently, Flash tracking is available for any Flash content embedded in a web page. Tracking of data sent from Adobe Air, Shockwave, or via the Flash IDE (e.g. using Test Movie) is not supported at this time.

Supported Development Environments

You can develop Analytics Tracking for Flash in either Adobe Flash or Adobe Flex environments. Each environment requires a different component, which you can download from http://code.google.com/p/gaforflash/. These components are based on ActionScript 3 and can be set up in one of two ways for each environment:

In Adobe Flash

• Add and configure a simple component in the component inspector and drag it to the stage.


• Import the Flash Tracking libraries directly into your library and start coding.

In Adobe Flex

• Include an MXML component that you configure from am MXML file.


• Import the Flash Tracking libraries into your script tags/AS3 files.

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How Does the Component Work?

In order to use the Flash tracking component in your environment, you either use the visual tools inside Flash, or you set up the tracking object directly in your code. Regardless of whether you are setting up the component visually or via code, you provide the following elements:

• The web property ID--This is also known as the UA number of your tracking code and looks like UA-xxxxx-yy, where the x's and y's are replaced with the numbers that correspond to your account and profile information for the object you are tracking. See Web Property for more information.


• The tracking mode--Choose either bridge mode or AS3 mode. This mode determines how your tracking communicates with the Analytics servers and is described in detail below.


• The debugging mode--No matter which environment or tracking mode you use, you can turn debugging on to validate and test your tracking.

Tracking Modes

Depending upon on how you distribute your Flash content, the Analytics for Flash component communicates to the Analytics servers either by bridging the communication between the Flash content on an existing Analytics tracking installation, or by communicating directly to the Analytics servers. These two modes are called bridge mode and AS3 mode, respectively. Both modes use the same Analytics tracking functionality, and it's easy to switch your Flash application from one mode to the other. In addition to choosing a communication mode for Analytics tracking, you can also use a debug mode to troubleshoot or validate your tracking.

In either mode, allowscriptaccess must equal always in order for campaign tracking to work. This parameter turns on read access to the page's URL and referrer information required by the Flash tracking code. Without allowscriptaccess, the Analytics tracking code degrades gracefully. It still provides most user activity data, but will not confirm to the Google Analytics campaign attribution model.

Bridge Mode

Use this mode if you control both the HTML page and the Flash content. This mode is best if you have already implemented Google Analytics (ga.js) tracking on your website and you want to add tracking to embedded Flash content. The bridge mode simplifies Flash-to-JavaScript communication by providing a unified ActionScript 3 interface to the ga.js code. It provides the connection from the ActionScript 3 calls to the Analytics JavaScript in order to make the tracking work.

The connection to the Google Analytics Tracking Code can be configured through the web property ID parameter in one of two ways:

• Most common method. The Google Analytics Tracking Code object already exists on your page with its own name, such as pageTracker. In this case, you provide the full DOM reference to the tracking object. For example, if your object is called pageTracker, you would reference that object in your code as window.pageTracker. For example, the following code snippet illustrates how this would be configured using the Adobe Flex environment with ActionScript 3:
  
  
  
  
  

  tracker = new GATracker( this, "window.pageTracker", "Bridge", false );

  
  


• Alternate method. If you have not created a page tracking object on your page, you can simply pass in your web property ID, and a JavaScript tracking code object will be created for you. With this method, reference to the base ga.js javascript source file is still required on your HTML page. The following code snippet illustrates how this would be configured using the Adobe Flex environment with ActionScript 3:
  
  
  
  
  

  tracker = new GATracker( this, "UA-12345-22", "Bridge", false );

  
  

In order for bridge mode to function correctly, ExternalInterface.available must be set to true in your ActionScript 3 code. This also means that allowScriptAccess should be set to always in the HTML page that embeds the Flash content. The following example illustrates HTML code configured for bridge mode:

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AS3 Mode

Use this mode if you control the Adobe Flash ActionScript 3 code, but you do not control the hosting environment of your Adobe Flash application. For example, if you are developing Flash content for distribution across many sites, then you would use AS3 mode. AS3 mode is completely independent from the ga.js tracking code and contains all the Analytics tracking functionality. There is no need for a separate ga.js tracking installation with this mode. In addition, AS3 mode uses the Flash storage mechanism to track session information for the visitor.

For certain DOM parameters such as language, the AS3 component tries to retrieve the values from the browser. If the values are not present, the component either uses the Flash equivalent value or defaults to no.

Troubleshooting and Validation

The Google Analytics Tracking for Adobe Flash component provides a debug mode to simplify validation and troubleshooting. When enabled, all tracking data is intercepted and directed to a screen in a text box instead of the Analytics servers. In this mode, you can see real-time the data that otherwise would be collected by the server. This feature also helps keep test data outside of your production data. You can enable the troubleshooting feature can by setting the visualDebug option to true in the component inspector.

Source: Google Analytics Tracking for Adobe Flash

Web Analytics: Google vs. Yahoo

Google Analytics & Yahoo Web Analytics Comparison

The last 12 months have seen the Google Analytics (GA) product evolve into a serious contender for a do-it-all product for most businesses, a period that has also seen Yahoo! get serious with analytics through the purchase of the not-so-well-know European product called Indextools - the decision by Yahoo! to make the Indextools product free with a rebrand into Yahoo Web Analytics (YWA) has turned the free market into a rich, dynamic, competitive space that’s seeing incredible product innovation.

Insightr Consulting are certified in Google Analytics through the Individual Certification programme as well as being a pioneer member of the Yahoo Web Analytics Consultant Network (YWACN). Our clients use a mixture of Google Analytics, Omniture Site Catalyst and Yahoo Web Analytics, with the majority of new businesses adopting either GA or YWA. In Singapore where we are based (and indeed across Asia) budgets for analytics are not as formalised as they are in markets like the US and Europe, so free tools are very popular here.

This article is the outcome of over 5 weeks work carrying out a feature by feature comparison of the Google Analytics and Yahoo Web Analytics products - a project that required a large overhaul after Google announced new features on the 20th October (more examples of the product innovation we’re seeing). Yahoo Web Analytics is still a largely unknown product to business and analysts alike as the product is only available through the YWACN or via Yahoo! advertising sales teams - this comparison should give you a pretty good feel for what you’re missing.

As our friends at Yahoo would say, this is perhaps an unfair comparison - as Indextools always liked to think of the old product as 80% of Omniture at 10% of the price. They weren’t wrong. In fact as an Omniture Site Catalyst user for over 7 years (how many of you can recall SC7?) the Yahoo offering is in many ways more powerful than Site Catalyst; especially when it comes to ‘advanced’ features such as segmentation which for Site Catalyst are only available with an additional “Discover on Demand” license.

Some of you will disagree with our analysis, and particularly with our scoring system - we’re hoping that the comparison is a thought starter and something that will lead to your consideration of the YWA product as a tool worth considering. We feel the scores are fair based on our usage of products and the way our clients will use them.

So, here’s the presentation - it’s best if you view it in full screen otherwise the screenshots will be too small. We apologise for the low quality of some of the screenshots - this was required to keep the file size down. It’s also a long document, over 50 pages - but that’s because both of these tools are powerful and are feature packed! We hope you enjoy it and get value from our work:

If you like the look of Yahoo Web Analytics, and in light of the fact that you’ll need to go through a Yahoo! consultant to get access we’re offering a Google Analytics to Yahoo Web Analytics migration package for a fixed US$2,500 project cost. Here’s what we’re offering:

Insightr: The ultimate comparison between Google Analytics and Yahoo Web Analytics; http://j.mp/2wenKg

Google Ad Planner: right audience for your campaigns

In our ongoing effort to give you in-depth data for your media plan, we've released a number of Google Ad Planner enhancements that provide a more granular view of where your audience can be found.

Subdomain data
We've added subdomain data to Google Ad Planner to give you a more detailed view of sites. This information can help you refine your media plan by providing more information about specific pages.

With subdomain data, you can search for subdomains; view the top subdomains based on total domain traffic for a site; view traffic, demographics and other data for the subdomain itself; and add subdomains to your media plan. Learn how to search for subdomains.

Ad placements
Ad placements are specific sections on a website where advertising can be purchased, such as the middle right section of a page.

Google Ad Planner now offers ad placement data so that you can make better informed decisions about where to target your ads. You can review placement data for sites in the Google Content Network, and beta test publishers using Google Ad Manager. Additional placement data is coming soon. Learn how to search for ad placements in Google Ad Planner.

Reach and relevance at a glance
With our new interactive graph, you can easily see which sites in your plan provide the best reach and relevance. In its default setting, the graph will compare sites in your search results by audience reach and composition index. Sites with the most reach will appear in the top-left quadrant. Sites with the most relevance will appear in the bottom-right quadrant. Sites near the top-right quadrant will have the best combination of both reach and relevance. You can customize the graph to visualize and compare sites in a variety of ways.

(Click to see full size image)

In the example above, a graph of sites reaching seniors age 65 or older shows that newsmax.com has the best relevance, facebook.com has the best reach, and nytimes.com has a mix of both.

More detailed publisher data
Publishers and site owners can now use Google Ad Planner to share additional Google Analytics data points such as page views, unique visitors, total visits, average visits per visitor, and average time on site. As a result, you can feel even more confident in the accuracy of Ad Planner data, and make better informed decisions about the sites you include in your media plan.

Google Ad Planner

If you're a media planner at an ad agency, you know that planning an online display buy can be challenging, particularly in scaling your campaign's reach while keeping it relevant for your target audience. Plus, how do you keep track of the millions of sites out there that might be just right for your campaign?

To make your life easier, we're introducing Google Ad Planner, a research and media planning tool that connects advertisers and publishers. When using Google Ad Planner, simply enter demographics and sites associated with your target audience, and the tool will return information about sites (both on and off the Google content network) that your audience is likely to visit. You can drill down further to get more detail like demographics and related searches for a particular site, or you can get aggregate statistics for the sites you've added to your media plan.

While Google Trends for Websites, announced last week, is designed for all users, Google Ad Planner is designed with media planners in mind. Using Google Ad Planner, you can quickly create media plans and export to a .csv file, which can be opened in most spreadsheet applications. Or, you can export to DoubleClick's MediaVisor, which helps you manage all your other media planning, buying and campaign management activities.

We hope you'll find this tool useful and discover many relevant sites--small and large--that would otherwise be hard to find. As Ad Planner is a new product, it's currently available by invitation only. If you're interested in trying it out, you can apply here.

Advanced Filtering in Google Analytics

Advanced Filtering in Google Analytics simplifies narrowing down data in the reports table by allowing threshold filters to be created. Instead of creating standard profile filters or weeding through rows and rows of data, Advanced Filters can be created on the fly for any report.

Here is another in-depth look at one of our recently announced new features: Advanced Filters (or Advanced Table Filters), written by the excellent team at LunaMetrics, a Google Analytics Authorized Consultant.

For the daily user, Advanced Filters may be the most useful new feature of the bundle of new features, in terms of streamlining your actual process once you access a report and are actively doing analysis. They are found at the bottom of the table in any report. As a habitual poweruser, I've been clamoring for it for years, and it has made my process so much simpler. It's the equivalent of replacing a screwdriver with a powerdrill.

You no longer need to export your data to slice and dice it to see your desired subsets. Now, you can set a filter while looking at a certain report to get the information you want, without having to exit and create a filter or advanced segment. Within seconds, you can whittle down a massive data table to look at a subset that is important to you.

One example already given in this tutorial video is to show just the keywords that have a low bounce rate (less than 30%) and that referred at least 25 visits. Right away, you've found high value and high traffic keywords. We're using this feature almost every time we look at a data table in a report. It makes you feel much more command over your data.

Here are three more interesting uses of the new Advanced Table Filtering:

Looking for specific non-branded keywords

Sometimes, it helps to see keywords that contain a certain word or phrase, but exclude the brand name. Taking a company called DeLallo Italian Foods, for example. If I wanted to see all the keywords that contain the word Italian food but exclude the brand name DeLallo, I could easily use the advanced filters for this. Previously, I would have done this using regular expressions in the filter:

Filter Keyword: containing ^(?=.*italian food)(?!.*(delallo)).*

No more! Now, we don't need to do this! Now, it is so easy with the advanced filters. Just filter for Keyword containing Italian food and excluding DeLallo.

And presto! Your report is updated. And, at any time, you can edit this filter to further refine it, or delete it altogether.

Landing Pages, Sorted by Bounce Rate

Has this ever happened to you - you're looking at your Top Landing Pages report, and you sort by bounce rate, only to have a bunch of pages with 1 entrance clogging the top of the report? With advanced filters, you can filter out those pages with a low number of entrances to get a better look at which landing pages with significant traffic have a high bounce rate. All you have to do is filter by Entrances greater than 50 (or whatever threshhold floats your bounce-rate-boat).

Top Content, Sorted by $ Index

Another similar use for sites with e-commerce or a goal value enabled is when you're looking at the Top Content report, sorted by $ Index. What you're trying to find are the pages that have the highest value - those that are viewed during a visit that results in a conversion. Again, it's common to get a lot of pages at the top that have a low number of pageviews.

First, it helps to filter out those pages that have a low number of pageviews. But once you do that, you'll likely see the pages with the highest $ Index are pages of your shopping cart or checkout process. We can filter out these pages with the advanced filters too - just add a new condition below your first filter that excludes pages that contain the word cart (or checkout, etc.) in the URL.

These three examples give you a taste of Advanced Table Filtering for your analytics, but they just scratch the surface. Once you explore your own analytics, I’m sure you’ll find many more uses of this flexible and powerful new feature. You'll really notice it's use when you find you're happily lingering for 5 extra minutes, using this new interface feature to easily gain insights and ask questions that would've taken you an hour before and possibly a data export. Pure wizardry. :)

Courtesy: Google Analytics

Free Google Analytics API Dashboard Application

Free Google Analytics API Dashboard Application

If you manage many Google Analytics profiles, it can be difficult to stay on top of all your top line metrics across accounts -until now. Trakkboard is a free, easy to use desktop application that allows analysts to create dashboards that pull data across different Google Analytics logins and different Google Analytics profiles to display top level metrics all within the same view.

This application was built using the Google Analytics API by our friends in Germany, Trakken GmbH and is available in English, German and Spanish. Once downloaded, you can add multiple Google Accounts, select Google Analytics Accounts and profiles, then choose from any of the pre-canned report widgets. The report widget will then appear on the dashboard. This process can be repeated with other Google Analytics Accounts, Profiles, and Widgets - and your customized dashboard is ready to use.



What's really nice is each report widget can be configured to automatically fetch new data from the API at a regular interval, for example, every hour. This dramatically reduces the time it takes to see top level metrics across all your accounts.

Some of the other available features include:

• 15 different report widgets available
• Top/flop keywords widget (movers & shakers)
• Drag-drop and resize report widgets
  
• Update all widgets at the same time
• Update individual widgets at set intervals
  
• Use tabs for more dashboards
• Resize report widgets
• Notes widgets for comments
• Add up to two Google Account Email addresses
• FAQ Center available in English, Spanish, German

We continue to be impressed by the new solutions developers are bringing to market by leveraging the Google Analytics Platform. If you have developed a useful new tool or integration on top of Google Analytics, drop us an email at analytics-api@google.com. If it's innovative and useful we'll highlight it to our readers on this blog.

New Update in Google Webmaster Tools

New Update in Google Webmaster Tools: Submit URL Parameters to Ignore

A good initiative by Google, allowing the webmasters to let the Googlebot know which URL parameters to ignore while crawling or indexing the site web pages.

1. Log in to your Google Webmaster tools account


2. Click on the ‘Site Configuration’ link on the left and then on ‘Settings’


3. At the bottom you will find ‘Parameter Handling’ to adjust the parameter settings.

Dynamic parameters (for example, session IDs, source, or language) in your URLs can result in many different URLs all pointing to essentially the same content. For example: http://www.example.com/product?pid=123 might point to the same content as http://www.example.com/product. You can specify whether you want Google to ignore up to 15 specific parameters in your URL.

Also, Google lists the parameters they have identified in the URLs of your site and suggests if the parameters are vague for them. You can confirm your choice to ignore or not to ignore. You can also add parameters that Googlebot was unable to identify and list them.

The reasons why I find this useful are:

1. This will really help Google in identifying the duplicate content pages and help them in proper indexing with fewer duplicate URLs.


2. This can result in more efficient crawling of Googlebot.


3. Googlebot can crawl more pages and index them, as the crawler efficiency is increased.


4. This can reduce the PageRank dilution as external website may be linking to various versions of your URLs and this will help Google understand that all these pages are same. Thus, providing the proper PageRank credit to the page.


5. Simple way to indicate the parameters to ignore or consider, instead of using the canonical option.

Google Local Lures Small Businesses

Google wants more small businesses to claim their listing profiles on Google Local (which is basically listings that pop up in Google Maps and local search results). To entice them, starting tomorrow it will give local businesses in the real world with physical addresses a free dashboard akin to what Websites get for free with Google Analytics (see screenshot above). Except that it will show stats such as how many times their business comes up as a search result, how often people click through, as well as how many times people generate driving directions to their business son Google Maps and where those people come from.

In return, all they need to do is claim and verify their listings at the Google Local Businesss Center. It takes about as much time as setting up a new email account, maybe a little more. Google gets clean data (and, thus, better results), businesses get free analytics and an opportunity to train Google’s search engine. Right now only a few hundred thousand businesses in the U.S. have been claimed out of approximately 20 million.

The other benefit to Google is that the more that small businesses can measure the impact of search, the more likely they will be to buy search ads. The dashboard shows the top search queries that result in a business’ listing showing up. The next obvious step is to start buying those keywords or optimize a business’ site to make sure they are on the page. There is no integration yet with Google AdWords (like there is on Google Analytics), but you can see that one coming from a mile away.