Chinalytics Longmarch Blog

One of the most elelgant features in Google Analytics is Advanced Segments. It allows analysts to quickly and easily segment visitors based on their behavior on your site and answer questions like:

• What share  of overal conversions comes  from visitors that were searching for brand search term vs. non branded terms?
• Which pages do visitors read that do not convert but don’t bounce? (Maybe you missed a goal, or the opportunity to add a call to action to critcal page)
• What is the conversion rate for visitors from groups of untagged referrers (e.g. social networking sites, blogs) vs. tagged traffic sources and search
• What is the best indicator for the difference between “super engaged visits” (>10 PV/Visits) and “normal engaged visits” (>1 to 9 PV/Visits). Is it the traffic source? Is it the landing page? Is it the conversion for a specific goal?

Just create 1 or 2 segments and compare the bahavior difference in almost any report in GA, instantly. Anyone who every tried to answer similar questions in Omniture or WebTrends will feel nothing but gratitude to Google to make segmentation that easy. You can find much more detail in Avinash’s great Google Analytics Releases Advanced Segmentation: Now Be A Ninja! post.

What Avinash didn’t tell us in his post is how to supercharge our advanced segment using user defined variables. I learned about this power of this combination, when trying to segment out the behavior of registered visitors. GA has no build in function that can identify registered visitors, but Google Analytics Help had the solution. User Defined Variabeles!

Adding  a small piece of JavaScript to your login script

<script type=”text/javascript”>pageTracker._setVar(‘registered_user’);</script>

tags this visitor as a member of the “registe

red user” segment by setting a variable in the GA cookie that is handed over with each tracking image request. That sounds complicated but in the end it just means that GA now allows you to create a custom segment based on this User Defined Variable and voala, you can segm

ent each report for registered visitors. Sweet!

But why stop there? Advanced Segments allow an unlimited number of variations. For example

• Compare “Converting Registered Visitors” (who convert to a Goal) vs. “Non Converting Registered Visitors”, to improve conversion
• “Returning Registered Visitors” (more than 1 visit in the time period) to “Single Visit Registered Visitors”, to improve loyalty.
• etc..

To take matters further, Convurgency provided a list of ideas for User Defined Variables in their Google Analytics – User Defined visitor tracking post in Juy 2007. They include ideas like:

1. Visitor Type Segmentation (Business Users, Technical Users, etc) based on form inputs
2. Simple A/B testing by setting a user defined variable for each landing page
3. Referrer Segmentation

This was before the new GA code and before advanced segments became available. Respect! Now implementing all these ideas became even easier.

What are you waiting for? Go segment!

Any ideas for cool segments? Please leave them in the comments.

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• What does the Google Analytics API mean for you? (cmswatch.com)

Florian's GA certificate

If you think you can do that, too, just go to starttest.google.com, pay 50 bucks USD and give it a go. I warn you, tough. This is harder than you think! Well, at least it was harder than I thought. To prepare yourself, you can go the Google Conversion University and join the free online classes.

Be warned tough, that while the classes seem very straight forward at times, the test is not. They have a couple of tough nut to crack in there. Especially areas that I am not handling on a daily based proved tricky. Here are some areas I should have taken a closer look at before taking the test.

1. Adwords – Adsense Integration
2. Regular Expressions (Regex)
3. Tracking of sub domains and cross domain tracking
4. E-Commerce tracking

Be smarter than me and study those in advance. I hope my experience helps those of your still planning to take the test. I definitely encourage you to do so. It was certainly worth it for me. It forced me to up my GA game and become a better analyst. Now I can proudly put the “Google Analytics Certified” batch on this blog.

Thank you. You may stop applauding

P.S.: Also a big thanks to Google’s Avinash Kaushik, for his encouragement and inspiration.

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• Update: Analytics Webinar with Avinash Kaushik (adwords.blogspot.com)
• I have just passed my Google Analytics Individual Qualification (IQ) test (seodoz.com)

One of interesting new features of Google Analytics, we didn’t have time to explore during the March WAW event, is Motion Charts.  Motion Charts are a fascinating visualization idea, displaying up to 5 dimensions of data in a very intuitive format. They were pioneered by Dr Hans Rosling in a famous TEDtalk discussing global health and are now available in Google Analytics to visualize web analytics data.

Motion chart or my blog traffic - click to animate

If you are interested to learn more about Motion Charts in GA, click on the image above to see my blog traffic visualized using motion charts and take a look at this training at the Google Conversion University.

The use of Motion Charts in not limited to Google Analytics however. They are also part of the visualization tools in Google Docs (see an example by the guys from Efficient Frontier) and can be accessed through an API. A good example for using the motion chart API is Eric Peterson’s Twitalyzer tool, that measures users influence on Twitter, and visualizes changes in influence over time. These interfaces open a wealth of interesting usage areas for data analysts, that can be integrated in dashboards and client presentations.

Now here in an idea: Use motion charts to visualize online buzz!

In China, product related discussions happen mainly BBS’s and sometimes on blogs. So these are the platforms we spent most of our time tracking and analyzing for our clients. to visualize user discussions on these two platforms, I propose the following motion chart setup:

The idea, of course, is to use this visualization to identify trending topics that need to acted upon. Those topics would clearly show in the top right corner (many posts, attracting many responses responses) of the chart with large bubble   sizes (many page views) and red color negative aggregate sentiment).

I you have any ideas on visualize social media tracking data? Let us know in the comments!

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• Video of March WAW with Google’s Stephanie Hsu (longmarch.chinalytics.com)

I am a bit late to the party, but in case you didn’t know yet, Avinash’s classic – “Web Analytics – One hour a day” is now available in Chinese (精通Web Analytics–来自专家的最佳Web分析策略). Please check it our here.
You will be pleased to know that Avinash’s name has been translated as ka(3) xi(1) ke(4), which regrettably has has no special meaning. I checked on DangDang and Joyo, the two leading online retailers of books in the Middle Kingdom, and regrettably, both do not carry the book yet. Still some promotion to do, but way to go Avinash. Kudo’s and congratulations!

On a more serious side, this is a great step for the Chinese web analytics community, since it enables a vastly larger group of people to learn about Avinash’s passion (an mine). It will certainly make my life easier training web analysts here at MRM. Its an a exciting new tool for us proponents of Web Analytics in China and I will make liberal use of it, and so I hope will you.

My colleague Song Xing (Sidney) is gearing up to translate not only my
posts, but also posts of global web analytics thought leaders on his blog. If you prefer to read about analytics in Chinese, go there! Also tell your less English inclined friends about this great resources. Its a big step forward in our industry.
To take a look at what other thought leader write about analytics in
English, take a look at my blogroll in the sidebar or subscribe to my shared items feed. Or as a comprehensive references, buy

• Avinash’s book “Web Analytics: One hour a day”, which provides much more than the title suggest
• and Eric T. Peterson’s “The big Book of KPI’s” which remains an amazing reference.

While both “guru’s” are engaged in a vigorous debate about how “hard” or “easy”
web analytics is, I recommend you to read them both. They offer amazing
insights and have been my guide on this interesting ride since the
beginning.

Catching up with my analytics reading, I saw this interesting post from Dennis Mortensen explaining his Online Business Media Quadrant Model.  His basic assumption is that in a world where user generated content becomes more important, measuring only what happens in the controlled environment of your site (with tools like Google Analytics and Omniture) gives us only half the picture (or one quarter, if you don’t measure UGC on your site). He recommends a more holistic approach by looking at

• Controlled On site Content
• Controlled Off site Content (User Generated)
• Uncontrolled On site Content (Syndicated content – PR)
• Uncontrolled Off site Content (User Generated)

What a nice summary of some of the work we have been doing recently A couple of thoughts on this (not yet well structured)

1. In China, but I assume in other countries as well, marketers still
   have a strong urge to control as much of the conversation as possible.
   This old reflex limits the reach of their message. Web 2.0 is a reality
   in China and marketers need to move away from a shouting mentality towards
   communication mentality, that emphases listening to the “voice of the
   customer”.
2. Separating online marketing and PR becomes increasingly difficult, especially when looking at user generated content (or IWOM to use another term). Marketers need to find ways to tear down the organizational barriers that prevent better integration especially on the analytics side. Otherwise their understanding will be limited to one or two quarters of Dennis’ quadrant.
3. Measuring user generated content (quantity, quality, key messages) is a new challenge for most advertisers in China. With the amount of conversation going on in the millions of blogs & BBS’s in China, manual tracking and counting is not scalable. While Sam Flemming’s CIC owns the conversation an IWOM tracking in China, looking at other alternatives might pay off, especially regarding integration with current analytics efforts.
4. Measuring off-site content provides a new challenge to advertisers as well. While some of the large advertisers in China just get comfortable with Web Analytics on their own site, some of our client already move away from viewing their site and their banner campaigns as the main medium for communication. They increasingly communicate with 3rd party vendors (e.g. video, sharing sites, business communities) to engage their audiences more effectively. While that makes a lot of sense from a marketing perspective, analytics team need to find solution for tracking the effectiveness of these cooperation. A low trust environment like China adds its own challenges (tip: never trust your vendors numbers). The best solution we have found thus far is forcing vendors to integrate our tracking codes on the pages / experiences they create for our clients. While this opens a whole different can of worms (explaining the differences between “our” numbers and “their” numbers anyone?) it seems like the best available solution.
5. Making your own content “mobile” will significantly increase your reach. Many brands in China have experimented with viral videos, hosted both on their mini-sites and video sharing sites and our numbers show a huge boost reach. Who says video’s are the only “mobile” / “viral” content brands can create? Think about making your downloads available on third party sites, your mini-games and your widgets and you will be rewarded with an explosion in reach. At least as long as you can measure it. Users seem to be more willing to engage in advertisers content when they are in the “mood” to watch, play or download.

Due to the Chinese National Day holiday I am little late on this one, but Avinash Kaushik just earned another feather in his guru cap with his post on engagement as a web analytics metric. His main points are:

1. Engagement is not a metric that anyone understands and even when used it rarely drives the action / improvement on the website.
2. Because it is not really a metric, it is an excuse. An excuse for an unwillingness to sit down and identify why a site exists. An excuse for a unwillingness to identify real metrics thatbmeasure if your web presence is productive.
3. It is nearly impossible to define engagement in a standard way that can be applied across the board. Definitions that exist are either too broad (to cover every nuance) or too narrow (hence very unique)
4. At the heart of it engagement tries to measure something deeply qualitative. Yet most efforts to measure it in our world tend to be hard core quantitative

Its great to see someone spelling it out so clearly, so please go ahead and read the whole post.

My own experience trying to implement Avinash’s suggestions for our own clients, suggests a deeper meaning between the lines of this post. An answer to the question “Why do Web Analytics practitioners still use Engagement as a metric, when most of us should share Avinash’s skepticism?”
Its because in most companies Web Analytics is in a silo, where it is not involved in strategic planning and decision making. Following Avinash’s methodology we end up asking strategic questions over an over again, but in many companies web analysts are not expected and / or not allowed to ask questions like:

• What is the objective of your site?
• What is the objective of this campaign?
• Can that objective be measured by analyzing clickstream data?
• What action do you want the user to take on the landing page?

These questions are owned by other silos, so we end up defining metrics that are under our control. We look at the data from our web analytics tools and hope to gain additional insights from “Engagement” however we define it. This may not solve the problem, but it is within our circle of influence.

So Avinash, what I hear your really saying is this: “Web Analysts of the world unite, and increase your circle of influence. Make Web Analytics a strategic function”. Oh, and one more thing: Don’t call your approach Web Analytics 2.0. Call it by its real name. Don’t hide behind a pretty moniker. Call it Strategic Web Analytics

If you have not read Avniash’s Book (and you should), you have to read and watch this post. Its a great overview of his ideas, and provides a great framework on how to think about web analytics. What, you still haven’t clicked?