You’re the analytics evangelist for Google. Doesn’t every marketer understand the importance of analytics? With hundreds of thousands or perhaps millions using Google Analytics, why would Google need an evangelist, and what do you see as the most important part of your job?

Numbers are hard to come by on this but in my humble experience a tiny fraction of people who should use data productively access it, and a tiny fraction of that actually end up using data effectively. We, as a universe, have a long way to go.

My role at Google is in two parts. In the inward facing part I am the “customer evangelist” as I help shape the vision, direction and features of 13 different Google tools that provide data to customers. In the outward facing role I help the top xx Google customers to leverage data more effectively.

The most important part of my role is that I am a small part of larger effort to create a data democracy in the online world.

All of the above is distinct from my role as a blogger (evangelizing the use of data in web decision making) and as the co-founder of MarketMotive (providing latest online marketing education and certification).

Avinash, following your first book, Web Analytics: An Hour a Day, what drove you to write your second book, Web Analytics 2.0: The Art of Online Accountability and Science of Customer Centricity? Did the field change so much since the first book?

The world indeed has changed a lot in two years, especially in three areas: user centric design, competitive intelligence and social media. There are a raft of new and delicious options that simply did not exist when I wrote the first book.

But the primary impetus behind writing the book was to address challenges that we all now face, challenges that present new opportunities (to engage and influence current and future customers) and how to measure success is a complex ever evolving ecosystem.

An example of that last point is in Chapter 9, if you see Figure 9.04. It makes you stand back and marvel at how we are measuring anything at all!

Even in a “standard” area like paid search analytics there has been so much evolution in the last couple years, analytics of which are covered in the new book.

Do you see both books as an evolution? I mean, would you recommend readers start with the first and then go to the newest one?

It is definitely an evolution.

People who have read the first book should feel that the second book is an immediate step up to a more evolved way of thinking about analytics, from the classic Trinity to the new Web Analytics 2.0. With each chapter there is a new way of thinking about what we already know and assumed. The second half of the book is where all the delicious stuff is that will help you change the game—it covers analytical techniques, social media analysis, competitive intelligence, new ucd approaches and of course things like multi-touch campaign attribution analysis.

If you want to start with the basic and take a gradual course then I recommend Web Analytics: An Hour A Day, but if you are willing to be a bit brave then Web Analytics 2.0 will get you to the goal faster.

I did want to point out that both the book are written for marketers, executives and analysts. You don’t need particular deep technical knowledge to become a analysis ninja.

Can you describe in a few words what is the main philosophy behind the book, the concept of Web Analytics 2.0?

Here is my definition of Web Analytics 2.0.

It is: the analysis of qualitative and quantitative data from your website and the competition, to drive a continual improvement of the online experience of your customers and prospects, which translates into your desired outcomes (online and offline).

Put simply, it is the art and science behind making intelligent decisions about all you do online—as a company, blogger, non-profit.

One sentence in particular caught my attention: “We’ve evolved from hits to page views to visits. Now we have outcomes.” Can you elaborate on that?

I have become convinced that more of the online world is not data driven because we have been reporting “silly metrics.” By that I mean hits and visits and page views and even visitors. What is the point of all those metrics?

They are all “aggregate” metrics that simply tell you “consumption”.

What they don’t do is answer this question: “What the hell happened?”

That question is important to answer because that is what matters to every senior decision maker, people who cut checks, people who will promote you and me.

Hence my near paranoid focus on first measuring outcomes—what happened as a result of all those people showing up? Did we make money? Are more people coming to my protest against cutting redwood trees tomorrow? Am I wasting my time marketing on twitter? What was the point of using video to sell micro-chips to B2B customers of Texas Instruments? So on and so forth.

God will welcome into sweet heaven people who focus on measuring outcomes. Because outcomes help businesses and people get better each day.

Your book has a lot of great advice for big companies trying to understand how their customers interact with their websites. But if I work for a Small-Medium Business, with a very limited budget, how can I go about implement this strategy? Any advice on whether search marketers should hire skilled web analysts or outsource the job to experts?

The book should be useful to businesses of any size. Throughout the book there are recommendations where to start and what to do first or what tools to use. For example on Page 13 it tells you that if you are a small biz then you must do x first, then y and then z and don’t worry about a and b. Or in Chapter 10 The Ladder to Analytics Nirvana gives a very specific road map for someone who is small to someone who is big. Same thing with Paid Search, Chapter 4 has the basic to medium stuff in terms of what to analyze for higher ROI and Chapter 11 is where all the juicy complex “I am going to be awesome” advanced stuff is.

My hope was to always provide a shallow end of the pool so everyone can get in, then those who want to do more can slowly, with confidence, move to the deeper part.

In terms of hiring… it would depend on your budgets and in-house sophistication. I have come to believe that if you don’t know what you are doing it is best to hire a consultant and put them on a “profitability plan” (i.e. you do the work I don’t know and we’ll both share the profit—not just hourly rates). Over time as your budgets increase, you become a medium sized biz, it is prudent to bring it all in.

Another interesting subject you deal with in the book is the web analytics career. What do I need to succeed as a web analyst? And if my main focus is on SEM, how can your book help me succeed?

You need to loooooove the web and all the glory and all the possibilities. If you don’t have passion for this medium there is no way you can put up with the work that is required.

Other main skills I look for: Initiative. Curiosity. An aptitude for data. Statistics 101. Self taught.

If your focus is on search then first Chapter 13 will help you plan your career effectively and help you create your own path for success. But most of all I am sure at some level we all understand that Search is not everything, certainly not paid search. The book will help you understand how the broad portfolio of online marketing works and of course how to be king by being data driven.

In the end it should make you a more rounded individual, and thus, I hope, a more marketable person in the job market.

You’re well known for the 10/90 rule. Why do you think it’s more important to put the emphasis on people rather than technology? How much is enough? How do you set reasonable goals and know whether you’ve achieved them, or to put your head down and try, try again?

Here is the picture that illustrates my concept of Multiplicity:

What it shows is the breadth and depth of the tools that are required to answer the four important questions: What, How Much, Why and What Else.

Even a few years ago for you to get access to tools would have required you to spend a lot of money, that’s not the case any more. Clickstream? Surveys? Competitive intelligence? A/B or multivariate testing? You got it, every single one has a 95% world class tool available for free.

So having tools (access to data) is no longer the key differentiator between companies, large or small. Having the brains to actually make sense of it all, look to the right tool to get the right answer, be able to actually analyze the data and not just data puke is not cheap. That’s where humans come in, that’s where the strategic differentiator comes in.

I had created the 10/90 rule almost five years ago when I was at Intuit. Never in my wildest dreams did I think it would actually be practical, but it is now. Every company in the world should not shoot for 10/90 (10 tools and 90 in people), experiment and find your balance. I think many people start with 10/90 and in a few years might morph to 35/65. No worries as long as you can so ROI impact.

But I have to admit, if you are not egregiously overloaded in the big brains (internal hires or consultants) you don’t stand a chance.

There’s a lot of buzz around attribution modeling, knowing the “value” of certain clicks to the overall conversion process. Is it important to know whether the last click was the major influence on a conversion, or will analytics packages increasingly try to understand the various steps in the overall buying funnel? If so, how?

This is the only question on which I’ll bow out from answering, it is complex and I think the reason we are in the soup we are is we look for shortcut quick answers. There are none. Of course I absolutely apply critical thinking to this in the book and provide answers.

Analytics systems are notorious for delivering vastly different interpretations of seemingly simple data—how users interact with a site. Why aren’t there more standards, and why are the reports from different vendors so different?

This is like asking a four year old boy how come he is so lame that he does not already exhibit the mannerisms and sophistication of a fully grown man.

Our industry is a baby, it is in a growth spurt, we must be patient and let things evolve. And they will.

Complete side note: It is utterly futile to wait for perfect data to make decisions and / or spend time comparing numbers between Omniture and WebTrends. What is the point of it? So we are more comfortable that one piece of data is 5% better than the other? Pause and think for 60 seconds how tv ratings are measured. It will horrify you how the data is collected and subsequently used for multi million dollar decisions. On its worst day the worst third party cookie based tool gives better and more accountable data for Marketing spend online. I personally don’t recommend wasting time trying to get the last 5% accuracy, simply not worth it. Implement tools correctly and completely. Don’t worry about the wife you just divorced. Worry about the one you just married and make a happy life with her.

Thanks so much for the opportunity to do this interview.

Thank you for spending the time to map your most interesting views of this subject, Avinash.

Editor’s note: Want to know more about Avinash’s new book, Web Analytics 2.0? He’s written a short overview of the book in this blog post. You can also buy the book through this affiliate link with Amazon and Avinash will donate 100% of the proceeds to two charities, the The Smile Train and Ekal Vidyalaya.

As a search agency we see many different styles of PPC campaign design, from in-house teams, other agencies, and occasionally running across those from the search engines themselves. Broadly speaking, campaigns can fit into one of two categories:

1. Those heavy on keywords or phrases made up of low number of fairly generic terms (for the purpose of this article, referred to as “shorter keywords”)
2. Those using a high number of terms ranging from generic to improbable (or “longer keywords”)

Our approach has always been to go for broke with the number of terms we use in our campaigns. So, we’re better aligned with option two and I know we are not alone.

As spelled out in Convert More New Users Using Advanced Segments, I’m a huge fan of the new advanced segments feature in Google Analytics. This article offers another example of how advanced segments can help us chop up our data into more digestible morsels. My aim here is to show the workings of this type of segmenting and combine it with a custom report to analyze keyword data more efficiently. I finish with a note on applying similar filters and the many possibilities that follow. The goal? To demonstrate that there is now an easy way to measure the reward for the effort of building those keyword lists.

A common misconception

I know some of you are thinking “why not just use “long tail” and “head” instead of saying “shorter” and “longer.” I can hear a lot of experienced search marketers and analysts replying “because they aren’t the same thing!” But then there are some slightly quieter murmurs coming from an unnamed corner of the SEM population. This sounds more like “erm, well, yeah, they are. Aren’t they?”

To clear things up, the “long tail” refers to the hundreds or thousands of keywords sending users to your site which, individually have few visits but collectively form a substantial chunk of your total traffic. The length or number of words in the keyword in this definition is irrelevant.

To make the second group feel better about this situation, I would like to point out that the long-tail often is full of keywords containing 3 or more words. To compound this, we also don’t really have a collective term for these long, ungainly but highly profitable terms. If we did it might prevent this misconception.

Defining “longer keywords” & “shorter keywords”

In the example below, the obvious starting point for defining a “longer keyword” is four words or more because from this point onward, the sum total of all the bars in the chart is less than the total of the previous (three words) bar alone. By that mark, keywords with three words or less become my “shorter keywords.” As a general rule, your “longer keywords” will be between 3+ and 5+ terms. Either go with that or your own analysis.

Create the segments

I doff my cap to the Google Analytics Twitter team for publishing this screenshot of a regular expression (regex) filter and to my colleague James Carswell who took the Google regex and made it even shinier.

Now that you have your definitions, add the below regular expressions as in the images. Think about whether there are any other terms or traffic types you might want to exclude at this point.

The longer keywords segment

I have used this segment to specify that we want to see keywords which contain at least three spaces and therefore have four words or more (\s|\+).*(\s|\+).*(\s|\+) (we altered the original Google regex to take into account the fact that occasionally words are separated by “+” signs instead of spaces in Google Analytics).

The shorter keywords segment

Use this segment or copy from the below image to create a segment which only shows keywords containing three words or less.

Build a custom report

This example is taken from an ecommerce website so I have included revenue data. You can equally use goal conversion data or bounce rate and average time on site. I have included source and medium here so that the data is available should you want to take it to the next level.

After applying both the custom report and advanced segment to a keyword report you can start to play.

A small tip here: the maximum number of keywords you can view in one report in Google Analytics is 500. However, when viewing a keyword report you can change the limit by appending “&limit=n” to the url in your browser. Where “n” is the number of keywords you would like to see data for—I believe the maximum value is 20,000. You won’t see any change in the interface but when you download the report as a .csv file you should have all the data you wish.

Go out and play with your segments

There are so many ways we can apply variations of this segment to suit different job roles and business needs. Also, the results really will be different for everybody. Here are some different ways of looking at interpreting the data for this segment.

Value of short vs. long keywords. Try comparing the data for the two above segments. What do you see? Is it worth bidding on those big generic terms anymore?

SEO value. Try charting the rise of visits of your “longer keywords.” Is the situation improving? As above compare the segment with the shorter keyword segment. Are they both going in the same direction?

Campaign cross-pollination. You may be able to segment by paid search, and in turn use the data to inform and help guide your SEO work. Similarly, are there some nice long terms you haven’t got in your paid campaign which are working for SEO?

AdWords optimization. What is the relative CPC of the longer vs. shorter terms? Do you need to come up with different bid strategies for each?

Google ad position. Combine these two segments with a custom report showing overall data for the different ad slots. Do longer terms perform better?

Sussing out the true value of the head vs. the long tail. If you really must define things by long tail and head status then try adding a number of visits quantifier to each segment. A great use for this is to see the shorter terms which are effectively in the long tail in terms of volume. They can often give you great ideas for a whole new array of terms.

Compare the engines. Look at the relative performance of Google, Yahoo! and Bing, segmented by longer and shorter keywords. Which one is doing a better job of bringing in the traffic on longer keywords? How does that affect your strategy on these terms?

Above all, experiment! You might not know something is useful until you try it.

Google has a bidding methodology called Budget Optimizer that attempts to maximize the traffic you receive for the keywords in a campaign. This is useful for early buying cycle keywords. However, every keyword should be reaching some goal regardless of where it falls into the buying cycle. It was difficult to track the effectiveness of these campaigns until recently when Google made some changes to Google Analytics.

Now you can more effectively bid on early buying cycle keywords, or keywords that you want exposure for, but do not have direct returns by combining the new Google Analytics goals with a budget optimizer campaign.

Google Analytics changes explained

Google Analytics expanded the number of goals you can track from four to 20. However, it looks a bit different as you now have “goal sets.” Where each goal set has 4 goals, and you can have 5 sets per profile.

Second, Google added two additional goals:

• Page views per visit
• Time on site

Please remember with Google Analytics, time on site actually means time on site minus the last page visited, as Google analytics only tracks data when there is a click. For instance, if your site visitor exhibited this behavior:

• Page 1: Time 5:00
• Page 2: Time 2:00
• Page 3: Time 1:00
• Exit via closing the browser window

In this example, Google does not know the browser window was closed and that they should stop tracking time on site. Therefore, Google Analytics would report 3 page views and 7 minutes on site for this user.

Using goals to measure branded or early buying funnel keywords

I’m a fan of bucketing keywords by where they fall in the buying cycle. If someone is in the learning phase of the buying cycle they need an informational page. If they are in the buy section of the funnel you need to monetize the click by encouraging them to buy.

This creates situations where keywords (and often display or content ads) reach a user who is at a different point in the funnel than the ads you’re displaying and you might not be able to monetize the user—at least for now.

For instance, the keyword “plasma vs LCD TV” is a learning cycle keyword. Buying a high-end electronic device is expensive, and takes thought and time before even beginning initial searches, much less actual purchases. Therefore, this keyword will rarely lead to a purchase as its a research oriented search. However, that does not mean it’s a bad keyword. When you have keywords that describe your products or services, but cannot be directly monetized, you have three options:

• Set a different conversion goal for the keyword, such as a newsletter subscription; and then set a value for what a newsletter subscription is worth
• Use an attribution management bid system
• Use a budget optimizer campaign

Use budget optimizer campaigns to increase your exposure

The budget optimizer works by your giving control of your bidding to Google in an attempt to maximize the traffic you receive. The optimizer does not care which keywords get clicks, it just cares that your budget is spent and that it gets you the most clicks possible.

The benefits for a publisher who measure RPV (revenue per visitor) are obvious. As long as your RPV is higher than your CPC, you want as much traffic as possible on those keywords. However, for those who sell a product or service, there is a unique way of using the budget optimizer to maximize your company’s exposure.

If you set ROI or profit based bids by keyword then do not use budget optimizer.

If you consider words to have unique values, then do not put those words into a budget optimizer campaign.

However, if you have keywords that, are early in the buying cycle and do not directly lead to revenue, describe your industry and products well but are hard to monetize and you want exposure on because they are words in the news, branded terms or for other reasons then putting these keywords into a budget optimizer campaign can be a useful way to maximize your exposure on non-direct ROI keywords. With the budget optimizer campaign, set a “branding or exposure” budget. The goal of this campaign is to move searchers into your buying funnel so you can monetize them at a later date.

However, you still need to make sure that these keywords are helping you reach goals.

Setting goals for budget optimizer campaigns

If you set a goal for these keywords such as a newsletter subscription or contact, then you should be able to determine what those conversions are worth and then set keyword bids. Therefore, words that lead to those types of conversions should not necessarily be in a budget optimizer campaign.

A few metrics for measuring budget optimizer campaign keywords are:

• Page views per visit
• Time on site
• Video views
• Bounce rate
• Send to a friend
• Bookmark
• Print this page
• Tweet This (or other social actions)
• Download product specs

The above goals do not directly lead to revenue; they lead to exposure and show levels of consumer engagement.

It just so happens that the new analytics goals will let you measure these items much more easily. Follow these easy steps to be able to measure the effectiveness of these early buying cycle keywords:

1. Set goals for the budget optimizer keywords, such as 3 page views per visitor.
2. In Google Analytics, set goals for time on site, page views per visitor, or use one of the expanded goal sets to track the social sharing items.
3. Add early buying funnel keywords to a budget optimizer campaign.
4. Let the budget optimizer campaign send you traffic. It may take up to two weeks for budget optimizer to really get going; have a little patience here.
5. Examine your analytics to optimize your budget optimizer campaign
   • If a keyword is not meeting your goals – delete it
   • If a keyword is meeting other goals (such as newsletter subscriptions) then move it to your normal max CPC or conversion optimizer campaign
   • If a keyword is meeting your budget optimizer goals, but no other goals, then leave it where it is
6. The last step is to measure if your overall revenue is higher while the budget optimizer campaign is running. If yes, then as long as the increased revenue is higher than the money you are spending in the budget optimizer campaign, then you are in good shape. If not, then lower the budget optimizer”s budget.

Every keyword, ad copy and landing page should increase revenue. If it does not, it needs to go away. However, you cannot always measure direct revenue for every keyword or traffic source.

Therefore, you should set appropriate goals for a keyword regardless of where it falls in the buying funnel to make sure your entire budget is working toward turning a searcher into a customer.

Engage consumers early in the buying cycle, so when they are ready to buy your company has already been part of the conversation. However, do not just spend money to receive clicks. Make sure every single click is helping to increase your overall profits.

Google has just announced a number of new features for Google Analytics, including more powerful reporting capabilities, greater customization options and a new “intelligence engine” that Google says can help search marketers drive smarter data insights. Here’s a rundown of the new features, and why they’re important.

Analytics intelligence

Google Analytics new “intelligence engine” comes with default alerts that will show you interesting trends in the data: it surfaces insights. Exactly how the algorithm that does this works is not clear, but it certainly takes into consideration many variables, in a very Googly manner. This can be a great way to start your day looking for insights—a kind of a conversation initiator for web analysts.

As we can see in the screenshot, Google did not let us down when it comes to design and UI. The interface is very intuitive and lets the analyst move through new alerts quickly.

Some really interesting things you can do:

Define your alert sensitivity. This controls the number of automatic alerts that will be triggered to appear in the Intelligence report page. As Google explains, “a higher significance rating is assigned as the difference between the actual performance increases with the expected performance.”

Create advanced segments based on alerts. On the right column of each alert you will see a link to create a segment. This is highly useful, since through alerts you might discover high converting segments that should be tracked separately, so Google makes this segment only one click away.

Change grouping method. This is very handy if you prefer to look at the data grouped by metrics or by dimensions. It’s a very useful way to change the data arrangement.

Creating custom alerts

Creating alerts is a very effective way to track visitor segments that are specially interesting to you. Let’s say you run a banner campaign to bring people into the website. How can you decide when to stop the campaign? Now you can use alerts to have an email delivered to you once your campaign bounce rate increases by 10% compared to the same day in the previous week. This is valuable because the website visitors (where the banner is shown) might get used to it, meaning that you reached those users that were high probability prospects. The option to compare to the same day in the previous week is a very good addition since it accounts for weekly variability.

For help on how to create custom report you can visit this help article

Other new features

In addition to the intelligence engine and the ability to create custom alerts, Google has also enhanced Google Analytics with the following new features, according to this Google Analytics blog post:

Expanded goals & site engagement goals. Two new goal types allow you to measure user engagement and branding success on your site. The new goal types allow you to set thresholds for Time on Site and Pages per Visit. Furthermore, you can now define up to 20 goals per profile.

Expanded mobile reporting. Google Analytics now tracks mobile applications built for iPhone and Android devices. Mobile app developers can understand how users engage with their mobile apps, such as what actions are taken within an app and what features are used. In addition, for customers with a mobile website, Google Analytics can now track traffic to mobile sites from all web-enabled devices whether or not the device runs JavaScript.

Advanced analysis features (incl. advanced table filtering). Google Analytics provides an arsenal of power tools you can use to perform in-depth, on the fly analysis without having to export your data to spreadsheet tools. Using Secondary Dimensions, you can view multiple levels and combinations of data side by side instead of having to drill down into each level. You can then use the Pivoting feature to cross-tabulate two different metrics with two different dimensions.

Multiple custom variables. Custom Variables gives power-users the flexibility to customize Google Analytics tracking to collect the unique site data most important to their business. With this feature, users can classify any number of interactions on the site into trackable segments.

Despite all of these great new features, we will still be dependent on human beings to analyze and take action on the data. Even Google Analytics evangelist Avinash Kaushik has written about his 10/90 rule, which says that just 10% of web analytics relates to tools, while 90% of both cost and success has to do with humans who analyze data and make recommendations for change based on their insights.

Last year Google Analytics released a few new features, among them the Motion Charts. The feature is based on software developed by Gapminder, acquired by Google in 2007.

According to the Google Analytics blog, “Motion Charts provide a multi-dimensional, over-time analysis of the data in your report.”

This feature provides a powerful way to visualize data in five dimensions: x-axis, y-axis, size of bubble, color of bubble, and time. In fact, if you choose metrics that are combinations of more than one metric, such as conversion rates (number of conversions divided by number of visits) bounce rate (number of bounces divided by number of visits), and Pages/Visit (number of pages divided by number of visits), you can even increase the number of dimensions to eight.

Here’s an example of a Motion Chart:

When it comes to search marketing, Motion Charts can bring significant value in the following ways:

Improve SEO keyword targeting. As we will see in the examples below, one of the advantages of Motion Charts over other forms of visualization is that it enables the analysis of large numbers of keywords across several parameters. This helps finding emerging keywords both in terms of traffic and performance. In addition, by analyzing bounce rate trends for keywords, we get a feel for landing pages that are underperfoming.

Expand PPC keyword targeting. By analyzing SEO targeting and results (as above), we can understand which organic keywords can present an opportunity to tap into new markets. Sometimes visitors get to the website using keywords that were not previously thought of as a match for the website. Motion Charts can uncover these “hidden” gems, allowing us to incorporate them into paid search campaigns.

Analyze PPC keyword performance. Paid search optimizers often fall into the trap of analyzing the average performance of campaigns or keywords. However, as Avinash Kaushik says about disappointing measurement techniques, “averages have an astonishing capacity to give you “average” data, they have a great capacity to lie, and they hinder decision making”, therefore the need to analyze not only averages, but segmented traffic and trends over time.

How to choose data displayed on a Motion Chart

As we saw above, Motion Charts can be very useful when analyzing search traffic whether we are looking into organic or paid traffic. In the screenshot below we see the keywords report (which can be reached from the Traffic Sources section). We will use it to exemplify how to populate the Motion Chart in the best possible way, according to the website owner needs.

The screenshot uses Google Analytics data from this Global Warming website.

It is important to understand that the Motion Chart data is defined by the report from where it was triggered. Here are the 5 parameters that should be defined carefully:

Keyword medium. Just above the chart, in the upper-left side, you will find three links: all, paid and non-paid. This can be used to analyze all keywords, just paid keywords, or just non-paid (organic) keywords.

Time range. As with all Google Analytics reports, time range can be adjusted from one single day to a few years worth of data. In order to neutralize time series effects I recommended analyzing at least a few weeks worth of data. Searcher behavior varies between the days of the week, the weeks of the month and the months of the year. Therefore, when analyzing daily behavior I recommended analyzing at least eight days (1 week + 1 day). When analyzing weekly behavior, at least 5 weeks (1 month + 1 week) and when analyzing monthly behavior analyze at least 13 months (1 year + 1 month).

Data grouping. This refers to the grouping of data into daily, weekly or yearly buckets. If you choose a six month range and group data in months, you will see six data points on the Motion Chart time-axis. If you choose the same date range but group it into days, you will see around 180 data points in the time axis.

Show rows. With Motion Charts it is possible to analyze up to 500 keywords in one shot. By using the drop down in the bottom-right corner of the table, you can include 10, 25, 50, 100, 250 or 500 keywords in the Motion Chart (although more than 25 keywords is a bit cluttered).

Filter keyword. This can be used to include or exclude keywords on the report. Keyword filters a very useful in when analyzing branded vs. non-branded keywords separately, or to analyze groups of words that hint at specific segments of visitors. It is possible to use regular expressions to build complex patterns if simple filters aren’t adequate for your needs.

After choosing your parameters, click on the visualize button above the chart (see top-left gray buttons on the figure above). This builds the Motion Chart for you.

How to choose Metrics to be analyzed on the Motion Chart

As with all web analytics analyses, the most important step when using the Motion Chart is to understand the website objectives and what metrics should be used to measure and improve these objectives. Often, your objective will be determined by whether yours is an ecommerce site or not.

When ecommerce is enabled in Google web analytics, interpreting results gets very intuitive. You know, for example, the value of a specific keyword (be it organic or paid) because you know how much revenue it brought. In the example below, the following metrics were chosen to be analyzed:

Visits (y-axis) shows the amount of visits for each keyword.

Bounce rate (x-axis) shows the % of bounces for each keyword. This is very helpful since it hints at whether a keyword matches the landing page or not—in other words, are you delivering the promise you give on your PPC ads or search result snippet? As Bryan Eisenberg writes: “keywords don’t fail to convert… we fail to convert visitors for that keyword.” Look for keywords far from the y-axis, especially red and/or big ones. Tip: optimize the landing page for these keywords.

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—thee keywords should get priority optimization treatment. Tip: increase exposure of these keywords on the website (and other SEO efforts) and 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.

For websites that do not sell online (or for those that do not use Google Analytics ecommerce feature), we would analyze results using slightly different metrics.

Visits (y-axis) is the same as above.

Bounce Rate (x-axis) also the same as above.

Goal conversion (color of bubble). For websites that do not sell online, we can measure site success using several parameters: registrations, whitepaper downloads, comments from users, etc. It is highly important to have these goals configured on Google Analytics. We use goal conversion in a similar way we used ecommerce conversion rate above: to see keywords that convert well but do not get enough traffic.

Per visit goal value (size of bubble). Another important setting on Google Analytics is the $ index variable. It is used to understand the contribution of each source/page (in this specific case, keywords) of the website to conversions. In the Motion Charts this metric can be used to show how much revenue each keyword brought to the website.

The Motion Charts you create can be shared by sending a link to your colleagues directly from Google Analytics; this link will lead people to the exact same chart you created (note that the viewer must have access to the Google Analytics account). However, if you want to present the results to management and are not willing to trust internet for meetings, or if you want to share the results in a website widget, I recommend three alternative ways to share Motion Charts:

CamStudio is an open source screen recording tool that lets you record your screen very easily. This has the advantage that you can use Google Analytics as usual and just record the resulting chart. This might be handy for presentations, and you can also share it in a website using a YouTube widget. However, it has the disadvantage that you cannot interact with the chart and change its settings.

Google Docs Gadget is a great way to share, because it keeps all the functionality of the Motion Chart and you can change the settings as you analyze the numbers. Viewers do not need access to the Google Analytics account in order to view the chart. However, you will need to export the data and organize it in Google Docs, which might take a considerable amount of time, especially for large quantities of data (explanations on how to format the data). Unfortunately, as of today, the gadget works with Firefox but not with Internet Explorer or Chrome.

Successive screenshots: although this is far from optimal, since you lose the motion effect and you cannot interact with the chart, it might be needed to include in Powerpoint presentations or if you want to hide your company’s data (pictures are easier to edit than videos).

I believe that Motion Charts are more than just cool graphs—they really provide some deep visualization that is difficult to achieve through other tools. Motion Charts can be used together with other Web Analytics techniques to optimize search traffic results.

Bonus – if you ever thought that geeks are not talented when it comes to singing and playing the guitar, here is the proof that analysts know something about music: Motion Charts anthem.

Below is a Motion Chart created using Google Analytics taken from Google’s official Youtube channel; it was recorded and shared through YouTube:

If you have additional ideas on how to use this feature, please share them in the comments.

A few days days ago, I noticed some strange Google Analytics data: Googlebot appeared as a browser in the reports. Although this might sound like a not-so-important fact when it comes to SEO, it is a major change in the Web Analytics field. As Avinash Kaushik and I wrote in the SEMJ journal article Web Analytics 2.0: Empowering Customer Centricity, an important advantage of all JavaScript based solutions (Google Analytics, Omniture, Yahoo Web Analytics…) is:

The JavaScript is not read by crawlers, which generates high amounts of traffic and are not representative of customers’ behavior. Crawlers can be excluded from the analysis; however, it is a time consuming task, and many of them are not recognizable.

To check whether this bot is really from Google, and not some kind of user agent switcher, I drilled down on the data and here is what I found.

Googlebot appears in Google Analytics reports

First of all, as we can see below, the Googlebot is recognized as a browser (version 2.1):

Second, when we drill down to the network location report we find the following:

How does it affect the data?

If we look at the behavior of this bot, we see a very low time on site, very low pages/visit, and very high percentage of new visits. This might be due to the fact that the bot does not fetch cookies, which is essential to accurate analytics tracking. Below are some numbers:

Statistically speaking, this means that the Googlebot is an outlier, which is a data point that lies outside of the overall pattern of a distribution. It means that it can distort the numbers. In the example above, just a few visits with very low time on site and percentage of new visits can significantly decrease the overall average time on site andpercentage of new visitors, which is clearly bad for someone looking at the overall behavior of visitors.

How to exclude Googlebots from your Google Analytics data

Here is a filter that can be applied to Google Analytics profiles to exclude this Googlebot from messing with your data.

What lies ahead?

Google has been officialy scanning JavaScript since 2008. So maybe this has been a low priority or low usage technique untill now, used only in very specific cases. But recently we have seen an increase in this practice, so the big question is whether this is a trend that will increase as time passes or is it just a few specific tests run by Google? Editor’s note: Google declined to comment when asked for more information.

For now, we can only hope that this kind of data is not being collected by analytics packages from the back door. If it has been this might have been skewing the data quite a bit given Googlebot’s low time on site and percentage of new visits stats.

Disclosure: The data used on the screenshots above was extracted from the Web Analytics Association website. If you would like to take a look at this data, it is currently available to all members as part of the Web Analytics Championship.

Postscript: Google Analytics posted a response in the comments:

“The official Google bot does not execute Google Analytics JavaScript. We’re not sure what it is exactly, it could be anyone’s bot, some intern’s experiment, or other such traffic.”

I agree with this comment in that the official Googlebot reads JavaScript but does not execute it. Besides, it does not store and send cookies, which means that Paves/Visit would be exactly 1 and time on site exactly 0. Lastly, If the officiall Googlebot did execute JavaScript, we would have seen massive ammounts of visits.

It is also important to note that although we used Google Analytics as an example, we mean all JavaScript based solutions, including Omniture, Yahoo Web Analytics, WebTrends and others.

Please note that this issue requires additional investigation both in regards to Google Analytics and to how Google Search uses the Googlebot.

The Google Analytics Blog announced that advertisers can now import their Google Analytics Goals and Transactions into AdWords for conversion-tracking purposes and CPA bidding. Beyond CPA bidding the benefit is that it “lets you track campaign ROI and optimize your account for conversions directly inside the AdWords interface.” The move simplifies the overall process of conversion tracking.

Here’s information from the Analytics blog on how to set up Goals and here’s how to import those Goals into AdWords from the AdWords blog.

Finally, here’s a companion article on how to think about and work toward better online conversions.

As promised, Google Analytics is now logging traffic from Microsoft’s new Bing search engine into the “search engine” traffic source category, rather than in the “referring sites” category. As a result, Google Analytics users can now see for themselves whether all of Microsoft’s Bing marketing is producing more traffic for Bing, which in turn is generating traffic for their web sites.

Looking at our Search Engine Land stats, Bing’s a big winner. For the past three days, it has sent more traffic to us than Yahoo. Here’s Bing:

June 12 is only a partial day, so it’s really June 8 through 11 to look at. It shows Bing sending us between 350 to 400 people per day. In contrast, Yahoo beat Bing on June 8 then fell behind:

From June 9-11, Yahoo’s been sending us less traffic than Bing, about 300 to 350 per day.

I’ve never seen Microsoft in the second place spot for search engine traffic referrers. Going back two weeks ago, it was comfortably ahead of Bing, sending us nearly three times the traffic.

This is only one site, of course. Looking at my personal blog Daggle, yesterday’s traffic breakdown was like this:

There, you can see that Bing is well behind Yahoo, sending Daggle only 5 visits to Yahoo’s 35. So by no means is what’s happening on Search Engine Land indicative across the web.

Google, of course, far outdistances them all — sending just over 1,000 visits. The same is true with Search Engine Land:

While Bing and Yahoo were sending less than 500 people each to Search Engine Land, Google was sending well over 5,000 per day.

This gap between search market share reported by companies such as comScore, Compete, Hitwise and NetRatings is well known. While they typically give Google a 70 percent share of the market, this comes largely from measuring searches that happen at each of the search engines and not what happens after a search is done.

Referrer stats show the percentage of share each search engine has based on the traffic they send to web sites. Those stats often have site owners finding that Google might have a 90 percent share (see Google By Far The Leader, If You Look At Site Owner Traffic Stats for more about this). No one’s really offered a good explanation for the gap — it’s not much studied, and it has been more pronounced with Yahoo. My own guess is that Yahoo recycles more searches at its site back into Yahoo’s own properties or to external sites that participate in its paid inclusion program (anecdotally, when you ask who gets traffic from Yahoo, those doing paid inclusion put their hands up).

More early stats have been coming in about how Bing is doing, and I plan to churn through those next week. Unfortunately, it’s not going to be until early to mid-July that we have full month June figures to get a real solid measure on how Bing’s done. But for a particular site, Google Analytics gives you a way to assess the situation now.

The Google Analytics Blog announced that they are currently adding support for Bing in the search referral reports. I actually noticed Bing showing up in the Google Analytics search referral reports this morning, on a limited basis.

Google added that if you want Bing to show up now, you can add a custom configuration to the Google Analytics tracking code for Bing. That looks like this:

pageTracker._addOrganic(”bing”, “q”);

A new study published by graduate students at University of California, Berkeley showed that Google is the leading footprint on the top 100 sites in their study.

As you can see from the chart below, Google Analytics was found on 81% of the top 100 sites, while DoubleClick (owned by Google) was in second place, by being found on 70% of those sites. Microsoft came in third with Atlas showing up on 60% of those sites. Here is the chart:

When combining Google Analytics, DoubleClick, AdSense, Google Widgets, Google FriendConnect and others, Google has a 88% footprint on those sites:

The New York Times blog has additional commentary on the limited study.

Postscript: Google: Master Of Closing The Loop? by Danny from April 2007, Danny talked about this issue. Back then Google said they would not mix and match the data between products.