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An API Integration To Measure Significant Change

3:29 pm - August 28, 2009 in Google Analytics Blog
Sophisticated, useful and cool applications are being developed everyday through the open Google Analytics API. We're loving what we're seeing. Basically, developers are grabbing their data from Google Analytics and slicing and dicing it, mixing it and mashing it with other data and applications, creating dashboards and widgets, and innovating some of the coolest stuff a data driven person could hope for. For example, we're really impressed with an app called Trendly which makes it easier to find important movers and shakers among your data via an innovative new interface, cutting down on the time you need to monitor your profiles. The team who built Trendly is using it as their one stop Google Analytics dashboard. We asked the team to share how this application came about, and here's what they wrote:
How many of you can afford to pay someone to monitor your analytics full time? We can't. We're a small startup, and we just don't have the resources to make that happen.

We use Google Analytics to track visits to our website, www.dabbledb.com. We'd love to have someone watching the hundreds of keywords, referrers, and campaigns that drive traffic to our site, someone who would send us a quick email whenever something really interesting happened: "Hey guys, thought you'd like to know that your average visitors from 'online database' doubled last week, and it's staying there - guess that SEO is working!"
So, using the Google Analytics API, we created Trendly, a monitoring and visualization tool which you can look at anytime and easily see what's changed. In short, Trendly uses mathematical models to take noisy data like this:

and figure out when significant changes have happened, marking it like this:

According to Trendly, our average daily visitors from the search words "online database" went up from 18 to 32 in mid-January, and then up again to 50 in early February. Also, Trendly sends us periodic emails to let us know about changes like these, saving us a lot of time. It also prepares a news feed with attractive charts that put the changes into perspective relative to everything else that's going on. Take a look at this - it calls out significant changes and makes them easy to notice with a timeline on the right.


When we first built Trendly for our internal use, we cobbled it together by screen-scraping and downloading exports from Google Analytics. But part of what made this tool exciting to us is that it solves a pretty universal problem. Trendly is your analyst until you can afford to hire a full time analyst. Heck, it probably keeps a clearer log of important changes than an analyst would! And with Trendly, you can delay this much longer since it cuts down your worflow by hours per week.

The new GA Data API allowed us to share it! With no signup and a couple of clicks, anyone can authenticate with Google and authorize us to grab their data and generate the reports. Suddenly our internal tool became a new product offering which can help any Google Analytics user. Give it a try and see for yourself.

What the guys at DabbleDB built is amazing. 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.


Nick Mihailovski, The Google Analytics API Team

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The Value Of Landing Pages

2:09 pm - August 31, 2009 in Google Analytics Blog
Imagine that we're launching a brand new advertising campaign for our new e-commerce website that sells Empanadas, my favorite food. The structure of the website is simple. We have a homepage, a few category pages that lists empanadas by type (baked, fried, etc), and hundreds of individual pages for each type of empanada (ham and cheese, steak, chicken, veggie, etc.).

Website structure

(click to enlarge)

Given this site design and our goal to sell as many empanadas as possible, let's look at this question:

Which type of landing page (home, category, or product) leads people to purchase more empanadas?

To answer it, we'll use two Google Analytics features, Custom Reports and Advanced Segments, to find out exactly, in dollars, which is the best type of page. And to perform this analysis we need one of two things: 1. e-commerce or 2. goals with a goal value.

Searching for the answer in Landing Pages
First go to the Content > Landing Pages.

(click to enlarge)

This report is naturally a good place to start but it only gives us three metrics: Entrances, Bounces and Bounce Rate. I want to know dollar amount, not bounce rate. To get the value of each landing page we have to create a custom report.

Step 1) Create the Custom Report
Go to Custom Reporting and create the following report:

Dimension: Landing Page
Metrics: Entrances, Abandonment Rate, Goal Completed and Value per visitor

(click to enlarge)

Great. Now I know the average value for any visitor that starts on these pages. On average the value per landing pages is $0.07. This means for all people who arrive at my webpage, on average each person will buy $0.07 worth of empanadas. Not much huh? However, as you can see some pages have a consistently much better conversion rate than others. For example, my home page -- /home.html -- gives me a per visit value of $0.10. I'd like to compare that with my other two page types: product and categories. We could go through this list and pick out one by one which is better, or write a regular expression in the search filter box, but an easier and more flexible way to identify these page is via Advanced Segments.

Step 2) Create the Advanced Segment
Take a minute to think about the layout of your website. Is there a unique identifier that let's you segment your landing page types? If there isn't then ask your Webmaster what you can do to get around this problem. In our example, remember that our website is very simple. Every empanada page contains the word empanada.html, every category page contains category.html, and the home page is home.html. To begin with, let's create a category segment.

Create the "Category" Advanced Segment
1. Go to Advanced Segments>Create New.
2. Dimension: Landing Page
3. Contains "category.html"
4. Name it "Visits that land on Category."
5. Save and Apply to report

Ouch! Visitors that land on my category pages spend an average of $0.04. Much worse than the average of $0.07. Now let's compare with what happens when a user lands on a page of an individual empanada product page. It's the same process as above except we use Landing Page Contains "empanada.html."

Create the "Empanada" Advanced Segment
1. Go to Advanced Segments>Create New.
2. Dimension: Landing Page
3. Contains "empanada.html"
4. Name it "Visits that land on empanada."
5. Save and Apply to report

Here is what we get:

(click to enlarge)

Wow! Visits that see a product page before anything else spend $0.30 on average. That's over 7 times more than the value of the category landing pages. Which pages should we use? Our empanada pages of course! We no longer have to guess which page is best. Even if we have hundreds of different types of empanadas we can calculate to the penny the potential value of focusing our advertisements on products.

Yeah, that's nice but how do I do the same for my website?

The above is a great example of full circle analytics. Set up goals, then create the reports and segments you best need to analyze the success of the goals. We chose to look at Landing Pages, but after you have goals, reports and segments in place, you can do most analyses.

Here are the key takeaways:

1. Most importantly your URLs must have a unique identifier (like our ?type=empanadas) so you can segment by page type AND either e-commerce implementation or a goal value.

2. Instead of thinking home, category, and product think home, broad, or specific. Usually, the more specific and focused the landing pages the better.

3. If you don't use an e-commerce website don't worry, you can do the same analysis. For e-commerce websites its much easier for us to calculate exact dollar return -- but! we can also use goal value to calculate user value. So, if you don't sell a product, your goal might be to have the users fill out a contact form. If for every 100 users that fill the form you can gain 5 leads that over a month spend an average of $100 each then the value of your form is 5x$100=$500/100=$5 per form completed. This goal value can also be used to calculate landing page value.

Now that you know exactly how to use Google Analytics to identify the value of your landing pages it's time to apply the lessons to your website. How much money do your landing pages bring you?


Posted by Gabriel Francis, Google Analytics Team

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Episode 2: Bottlenecks To Implementation For SMBs

3:46 pm - September 1, 2009 in Google Analytics Blog


The first episode in the three part "Data Driven Discussion" series about bottlenecks to implementation focused on large, enterprise-class companies. In this episode, we ask our experts Nick and Avinash the question, "What obstacles does a small-to-medium sized business face in implementing analytics?"

SMBs are often more nimble than large businesses but resource-constrained with everyone working overtime. A lot is at stake. In this environment, analytics can have a huge impact, answering questions and giving guidance through data to back up major decisions.

Posted by Jeff Gillis, Google Analytics Team

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Episode 3 Of Bottlenecks To Implementation: Should You Use An Agency?

10:09 am - September 4, 2009 in Google Analytics Blog


Here is the final episode of our three part series on bottlenecks that companies face implementing web analytics. In this episode, we ask the question, "Should you use an agency, or can you do everything (implementation and analysis) in house?"

Bottom line: you need big brains.

And if you decide to go with one of our authorized consultants, you can find one near you. They are analytics do-everything agencies which often double as SEMs, SEOs, and Website Optimizers so you get the full circle of support for almost everything you do online - including strategic recommendations on improving your web presence and marketing.

Posted by Jeff Gillis, Google Analytics Team

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Using Google Analytics To Identify High-Performing Keywords

4:48 pm - September 11, 2009 in Google Analytics Blog
The topic of using Google Analytics to optimize your PPC keyword buys never gets old. We have posted about it here a bunch. It's putting your PPC money where your analytics mouth is, uh, for lack of a better metaphor and is one of the core reasons to use web analytics. Recently, a Google blog called Solutions for Southeast Asia wrote a post about this topic, covering the techniques to use Google Analytics and AdWords to find and add the most effective unused keywords. It's a great post - definitive and very thorough, going from soup to nuts, expanding on these steps:

Step 1: Ensure Goals and E-commerce Tracking are set up
Step 2: Access the Keywords Report
Step 3: Export non-paid keywords to a spreadsheet
Step 4: Expand the list of keywords using other Google products
Step 5: Download a list of keywords that you are already advertising on
Step 6: Identify keywords you are not advertising on
Step 7: Expand on these keywords and start advertising

For explicit directions on each of these steps, take a read of the article. You and your website will benefit. Of special note - step 4, which we've pasted in below. As you can see, Vinoaj, the author, gives you an extensive list of Google products that can help you refine or expand your keyword list. Some of them you've probably never even heard about, but will simply take your targeting to the new levels, especially when budgets are tight but you want to grow your business as Q4 approaches.

To consider more keyword options, consider using some of Google's other free products to discover more opportunities: Google Insights for Search, Google Trends, Site Search reportsin Google Analytics, Webmaster Tools, and even the new Wonder Wheel. If you are an AdWords user take a look at the Keyword Tool, Search-based Keyword Tool, Search Query Performance reports, and more. Once you have identified additional keywords you would like to advertise on, add it to your list of keywords from Step 3.

Happy analyzing!

Posted by Jeff Gillis, Google Analytics Team

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New Video: What is the Analytics API?

3:09 pm - September 17, 2009 in Google Analytics Blog


I spent a day in Irvine, California interviewing some of the software engineers who built the Google Analytics API, starting with Jacob Matthews, the tech lead behind the API. If you haven't read any of our API documentation yet but you have been wondering what the Google Analytics API is all about, we put together a couple of videos where we hear about the API from the people who built it. Here is the first one where we keep it high level and ask Jacob, "What is the Google Analytics API?"

Enjoy!

Posted by Nick Mihailovski, The Google Analytics API Team

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Back To Basics: Save Clicks, Save Time

5:24 pm - September 21, 2009 in Google Analytics Blog
Did you know that you can save clicks and jump directly to a deep-level report from your dashboard? Let's say that you want to see which cities in California you get traffic from. Ordinarily, you'd need to click Visitors, then Map Overlay in the report navigation. Then, you'd need to click United States, then California. But, you can save 3 of these 4 clicks by simply adding this report to your dashboard.

Try it now. Go to one of your favorite reports that requires several clicks to access. Once you've arrived at the report you want --and at the level you want it -- click Add to Dashboard. (The Add to Dashboard button is at the top of your report on the left, next to the Export and Email buttons.)


You'll now see the report on your dashboard. The next time you log in to your Analytics account, you'll be able to see the top cities from California on your dashboard and jump right to the report with a single click.


Posted by Alden DeSoto, Google Analytics Team

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New Video: Steps to Using the Analytics API

1:58 pm - September 23, 2009 in Google Analytics Blog


Last week, in our Google Analytics API video series, Jacob Matthews discussed What is the Google Analytics API? In this new video, Jacobs goes deeper and describes the three steps developers need to take to retrieve data from Google Analytics: Authentication, Account Query, and Profile/Report Query.

Feeling inspired? Play with our interactive javascript examples to see the API in action.

Posted by Nick Mihailovski, Google Analytics API Team

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Fall In Love With Motion Charts

4:33 pm - September 24, 2009 in Google Analytics Blog

Have you used Motion Charts yet? If not, it's a little like playing an instrument. It takes a little practice, but once you get the hang of it, it's the best thing you ever did - fun and informative and you'll want to do it daily. Create them and watch them reveal patterns you weren't aware of in your keyword activity or some other area that is important to your site.

We've written a few posts on Motion Charts and made a video, and now we wanted to refer you to a great article called How To Use Google Analytics Motion Charts To Maximize Results, on Searchengineland.com, written by one of our Authorized Consultants, Daniel Waisberg from easynet search marketing in Israel.

Daniel discusses both how to use Motion Charts and also what metrics to designate as which elements of the chart to best use the feature for optimizing your online marketing. In his example, Daniel chooses to have conversion values as the size and color of the bubbles so you can easily spot them for optimization opportunities. For instance, for an e-commerce site and a motion chart showing keywords with the y-axis as visits and the x-axis as bounce rate, Daniel says:
Ecommerce conversion rate (color of bubble) shows the conversion rate for a keyword. This is important since you might have high converting keywords that are not receiving enough traffic. Look for red-small bubbles located close to the x-axis—these keywords should get priority optimization treatment. Tip: focus on these and related keywords on your PPC campaigns.

Revenue (size of bubble) shows the amount of money this keyword is driving to your website. Look for big-blue bubbles—this is a signal that a keyword brings lots of money but could bring even more if it converted better. Tip: optimize the pages related to these keywords to improve conversion.
Daniel also goes into detail about how to share motion charts with others. If you're ready to try Motion Charts today, his article is your next step. Then, for more inspiration, here are a few more examples of using them.

Posted by Jeff Gillis, Google Analytics Team

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Advanced: Structure Your Account With Roll Up Reporting And More

1:58 pm - September 29, 2009 in Google Analytics Blog
Guest post by the team at E-Nor, a Google Analytics Authorized Consultant

For the analytics ninjas out there, you know that data accuracy is probably one of the most challenging aspects of analytics across all solutions and platforms, and you learn to apply best practices and establish processes to improve data collection and reporting.

But for the rest of us, how do we help marketers, business owners, and webmasters have confidence in their data? Analytics is all about clarity. It should help you see actionable statistics clearly and quickly. However, when you have a website structure with multiple domains and subdomains - which is often the case - sometimes things can get jumbled.

For instance, you are a CMO or a Director of Marketing at the enterprise and you are responsible for the performance and ROI of a large number of web proprieties. You look at your analytics reports and you can't find your ecommerce data from site A, site B is referring traffic to itself (definitely not a good thing!), and conversion data from your marketing campaign microsite is no where to be found.

This image sums up the feeling.

No need to panic. This post aims to offer an approach to help you plan your Google Analytics accounts setup in a structured fashion to help with clarity. I hope that by following the approach and the technical steps, you will be able to collect and manage all your data, make more sense of it, and most importantly, ensure what you are reporting on, trending, dashboarding and analyzing is based on accurate data.

There are two distinct sections of this post:

  • The Strategy (non-technical)
  • The How (technical)

The Strategy

There are many ways to structure your Google Analytics profiles when you have multiple domains and subdomains. But in this post I will limit myself to the one that I like the most and I believe is the least confusing.

Before I go into details of the solution, and for simplification, let us assume we are dealing with a project that has the following requirements:
  • A business with 3 domains (www.a.com, www.b.com, and www.c.com)

  • 1 domain (a.com) links to a 3rd party shopping cart (www.mystore.com)

  • 2 domains (a.com and b.com) have multiple sub-domains

Here is a graphical representation of the structure:

Measurement Requirements
  • Track each domain and sub-domain separately (e.g. www.a.com, news.a.com, and blog.b.com)

  • Track the rollup/overall traffic for all domains and sub-domains

  • Track full e-commerce transactions

Solution
  • Create a Google Analytics account for each domain (www.a.com, www.b.com, and www.c.com)

  • Customize the tracking code to link the multiple sub-domains with their main domains

  • Link the third party shopping cart with the main domain and install Google Analytics tracking code in all shopping pages

  • Create a rollup Google Analytics account and add its code to all domains and sub-domains
Graphical example of a well-planned Analytics Account Structure:

Now on to the technical stuff. If you don't enjoy javascript and regular expressions, you may stop here and ask your webmaster or technical analyst to read further :-)


The How:

I will try to illustrate the technical implementation in 10 simple steps:

1- Create a unique Google Analytics account for each domain www.a.com, www.b.com, and www.c.com and then use the account number UA-AAAAAAAA-1 in the code in step 3 and use the accounts UA-BBBBBBBB-1 for www.b.com and UA-CCCCCCCC-1 for www.c.com in the code in step 8.

2- Create a Google Analytics account for the a rollup account that will oversee all domains and sub-domains (use the GA account number UA-XXXXXXXX-1 in the code used in step 3 and 8)

3- Add the following Google Analytics tracking code to the main site (www.a.com) and its sub-domains (blog.a.com, news.a.com, images.a.com, and media.a.com)

About the following code: We have a regular pageTracker object to track activity on each particular subdomain and a rollupTracker to track activity across all subdomains and the third party checkout site. (Click here to learn more about the customizations we made to the standard Google Analytics tracking code)

<script type="text/javascript">
var gaJsHost = (("https:" == document.location.protocol) ? "https://ssl." : "http://www.");
document.write(unescape("%3Cscript src='" + gaJsHost + "google-analytics.com/ga.js' type='text/javascript'%3E%3C/script%3E"));
</script>
<script type="text/javascript">
try {
var pageTracker = _gat._getTracker("UA-AAAAAAAA-1");
pageTracker._setAllowHash(false);
pageTracker._setDomainName(".a.com");
pageTracker._setAllowLinker(true);
pageTracker._trackPageview();
var rollupTracker = _gat._getTracker("UA-XXXXXXXX-1");
rollupTracker._setAllowHash(false);
rollupTracker._setDomainName(".a.com");
rollupTracker._setAllowLinker(true);
rollupTracker._trackPageview();
}
catch(err) {}

</script>

4- Enable E-Commerce Reporting

Analytics Settings > Profile Settings > Edit Profile Information

5- Add the following code* to all shopping cart pages on the store site (www.mystore.com)

*Make sure to add this code to the top of the pages.

<script type="text/javascript">
var gaJsHost = (("https:" == document.location.protocol) ? "https://ssl." : "http://www.");
document.write(unescape("%3Cscript src='" + gaJsHost + "google-analytics.com/ga.js' type='text/javascript'%3E%3C/script%3E"));
</script>
<script type="text/javascript">
try {
var pageTracker = _gat._getTracker("UA-AAAAAAAA-1");
pageTracker._setDomainName("none");
pageTracker._setAllowLinker(true);
pageTracker._trackPageview();
var rollupTracker = _gat._getTracker("UA-XXXXXXXX-1");
rollupTracker._setDomainName("none");
rollupTracker._setAllowLinker(true);
rollupTracker._trackPageview();
}
catch(err) {}

</script>

6- Add the e-commerce tracking code to the confirmation page after the GATC.

Read more about "How to track e-commerce transactions?"

7- Change the links to the store site (www.mystore.com) on the main site (www.a.com) to use _link as following:

If the current link looks like:

<a href="https://www.mystore.com">Buy Now</a>

Change it to:

<a href="https://www.mystore.com" onclick="pageTracker._link(this.href); return false;">Buy Now</a>

8- Repeat step number 3 for domains www.b.com and www.c.com after updating the Google Analytics account number UA-AAAAAAAA-1 and the setDomainName value.

  • To view the entire code for www.b.com and its sub-domains (click here)

  • To view the entire code for www.c.com (click here)

9- Create a profile for each sub-domain (only if needed)

In order to track a sub-domain (ex. blog.b.com) in its own profile, follow the following three steps:

a- Create a filter that include only traffic from Hostname=blog.b.com


b- Create a profile and name it "Blog"

c- Apply the sub-domain filter to the new profile

10- As you might have noticed from the codes that we added so far to all pages, we added an extra Google Analytics account to track all pageviews across domains and sub-domains to one Google Analytics account. We call this account “rollup account”.

var rollupTracker = _gat._getTracker("UA-XXXXXXXX-1");
rollupTracker._trackPageview();

Since in the rollup account, we will track pages from different sites and many of these pages might share the same naming convention, I suggest that you create an advanced filter that adds the hostname to the page name to differentiate between pages with same URI.

Once you apply the filter, the upcoming data will appear as following:


Note, in the example above if we didn’t apply the “Add Hostnames” filter, all home.aspx pages will appear as one page with 2685 pageviews.

If you have been with us so far, you are now ready to conduct your analysis based on clean and much more accurate data :)
  • To review each domain by itself and for deep-dive analysis, use the domain profiles

  • To get an overview and to see how the business is doing across all sites, use the “Rollup Account”

Related Posts


Posted by Allaedin Ezzeidin, Analytics Specialist @ E-Nor, a Google Analytics Authorized Consultant

More from Jeff Gillis in Google News

 

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