Chinalytics Longmarch Blog

Bounce Baby Bounce (Source: Wikipedia

When I first started to learn the ropes of web analytics, I turned to Avinash Kaushik’s blog (Occam’s Razor) and book (Web Analytic’s: One hour a day) for a great deal of insight and actionable advice. One thing that stuck with me early, was Avinash’s emphasis on Bounce Rate as “The sexiest metric ever“. With all the caveats of generalizing metrics across different websites, bounce rate analysis is still a great place to start, when you plan to optimize your website.

Many companies and analyst have followed Avinash’s lead and are now prioritizing the reporting of Bounce Rate metrics. Talking to many clients in China I noticed a common question on everyone’s lips, tough: “My bounce rate sucks. What can I do?“.

Over time I developed a standard approach to address this question. Take a look at my 5 step guide:

Step 1: Does your bounce rate really suck? (Benchmarking)

Good or not? (Source: http://etc.usf.edu/)

In order to understand if you need to take immediate action to improve your bounce rate (as opposed to focusing on other KPIs), it is critical to benchmark your site’s performance.

Since user behavior and web design varies greatly among cultures, it is critical to find relevant local benchmarks for your site, ideally in your industry. In the US, services like compete.com provide valueable data. In China we have to do without any reliable 3rd party benchmark (what a shame). Even Google Analytic’s Benchmark function is not relevant, since it compares sites by industy, but does not provide country specific numbers.

A rule of thumb based on my experience in China (and please leave your ideas in the comments segment):

1. For micro sites for branding campaigns with mainly banner traffic: 85% to 90%
2. For landing pages of search marketing campaigns 25% to 40%
3. For landing pages of targeted direct marketing campaigns (20% – 30%)

If your numbers are higher, your bounce rate really sucks and you do need to take immediate action.

There are 4 common drivers for bounce rate.

Bounce Rate Causes

Lets take a look at each of them.

Step 2: Landing page segmentation

Bounce Rate is calculated by dividing the number of single page visits  to a page (bounces) by the number of overall entires (visits that started on this page) to that same page. Bounces can only occur on landing pages (the first page a visitor sees on a visit to your site). So when your overall site shows a high bounce rate, you should first look at which landing page contributes most to your overall site bounce rate.

The most effective way to do that, is to calcualate the weighted bounce rate of all your landing pages. Stephane Hamel wrote the defining post about the methodology in 2007 on his Immeria blog. In effect you calculate the impact the bounce rate of each landing page has on the overall site bounce rate, by weighing it according to each pages importance (measured by the number of page views).

Use this formula

Bounce Rate * (Page Views/Total Page Views).

to calculate the Weighted Bounce rate of each landing page.

Take Action: Focus further analysis and optimization efforts on the landing pages with the highest weighted bounce rate. Check if your problem landing page is implementing best practices, usability test it, make changes, then A/B test the new version vs. the old version.

Step 3: Traffic Source Segmentation

Another driver for a high bounce rate on your site is low traffic quality. If your advertising efforts drive visitors to your site that are not interested in what your site has to offer, the best landing page cannot convert them. So before to start getting all excited about remodeling the landing experience, take a look at the traffic sources for your problem landing page. Many web analytics tools (regrettably not Omniture) allow you to easily segment your bounce rate by traffic source and / or type of traffic.

Bounce Rate by traffic source in Google Analytics

When doing this segmentation, look out for high volume traffic sources that drive traffic with a very high bounce rate. Very high is relative and a good benchmark is usually the bounce rate of your direct and search traffic. Visitors from these sources are usually highly targeted. If their bounce rate is high, your landing page likely has a problem. If these traffic sources have a low bounce rate whereas others, especially banner ads, partnership links etc have a very high bounce rate, don’t change your site, change your (paid) traffic sources.

Step 4: Creative Segmentation

When seeing high bounce rates for banners or SEM campaigns, it makes sense to dig one level deeper. Often these campaigns run with multiple creative executions of the banner or multiple copy executions for the text ad. Sometimes that creates a situation where one banner’s creative or call to action or one text ad is not relevant to offer made in the landing page. That is turn leads to a high bounce rate.

To understand if that happened to your campaign, you first need to make sure that your banners and text ads are comprehensively tagged (Google Analytics: UTM _content; Omniture SAINT tags) to differentiate between different creative versions. In the next step, A/B test your various creative version in multiple spots, to measure which one leads to the higher bounce rate.

Action: Run a creative A/B test before launching a campaign to ensure maximum performance.

Step 5: Loading time (Geo Segmentation)

Another very important factor for bounce rate performance is the loading time of your landing page. Especially rich landing experiences (often Flash based) require the download of large amount of data before they are ready for consumption. The longer visitors have to wait before the experience begins, the more likely they are to bounce. So far so easy.

The key challenge for web analysts is that loading time data is not available in any  web analytics tool. In order to get reliable data, you need to buy the services of companies like Gomez, who specialize in web performance measurement (see last weeks Web Analytics Wednesday). This data is especially important in China, where loading times can vary widely across provinces and cities due to a unique network layout (see ChinaNetCloud’s presentation on SlideShare).

A good indicator for loading time challenges is a large variation of bounce rates across provinces in China. In order to get Google Analytics to show you the bounce rate by province in China, go to the map overlay report and click on China. This will go directly to the “by city” breakdown. Then go to the URL bar of your browser and replace the term “city” with the term “region” (** here magic happens **).

Bounce Rate by Province (China)

Action: If you see a large variation (especially between northern and southern provinces) you have a good indicator that your need to improve your hosting infrastructure to address your bounce rate problems.

These are my five steps. What are yours? Did I miss anything important? Let me know in the comments.

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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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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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For those of you who couldn’t join yesterday’s Adworld 2009 event, please find the presentations of the Web Analytics session attached. Regular readers of this blog will have seen my 10 Rules for Winning through Analytics presentation already.

Among the other three  decks I want to highlight the “Measurement and optimizations at Qunar” presentation Charlene Ng gave. Qunar is one of the leading travel portals and the leading travel search engine in China. They have presented atthe June WAW but the current deck is more detailed and provides a much better understanding about how they do analytics.

Thanks again to DCCI for hosting this event and of course for all the Speakers & Sidney Song (OMD) – as the moderator

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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)

As a follow up to the March WAW video, here are a few more links to interesting Google Analytics resources.

Google Conversion Unversity Videos on Youtube

Google uploaded videos of 24 for presenations covering GA funtionality, Web Analytics skills and best practices to Youtube. These are very well done and insightful. You can find Stephanie Hsu’s presentation of AdWords integration there as well.

Google Conversion usiversity trainings and Google Analytics Individual Qualification (IQ) test

A collection of short trainings, covering topics from ‘first steps in GA’ to ‘In-depth Analysis’, very useful for beginners up to more advanced users alike. They  explain all GA features and some analytics techniques.  The trainings also form the basis of the GA Individual Qualification test, that allows individual analysts to become certified in GA. That is a very powerful personal marketing tool right there. Would I prefer hiring a Google IQ certified applicant over one without any certification? You bet!

Regrettably, both videos and trainings are mainly available in English.  The Chinese version of the conversion university, while available, lacks the breath of content and the link to the IQ test. So the work the the GA China team is clearly cut out.

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For those of you who didn’t have the  March Web Analytics Wednesday Beijing with Google’s Stephanie Hsu take a look at the video posted above.  Its a full recording of the presentation and Q&A session. The presentation last for 32 minutes and then we have 23 minutes of Q&A. Make sure you don’t miss the insightful questions from our audience. For Song Xing’s video of the event take a look here. We understand that image and sound quality are still sub-optimal and are working on it.

For those of you who have never attended a WAW, this is also a great sneak peak, to see if joining us would add value to you. What is missing from the video of course is the great food and the networking. For that you really have to turn up in person.

During the presentation, in front of a record crowd of 60 attendees,  Stephanie focussed on Custom Reports and Advanced Segments. Both are very powerful tools and Stephanie does a great job demonstrating that power. She is showing actual screenshots from GA, which sometimes makes it diffucult to read what is on the slides. This is compoundend by the fact that I had to use an evluation version of a video conversion software to get the video uploaded.

The video was graciously provided by one of our attendees, but neither of us are experts at filming and editing video. Any recommendations and support in the filming and editing process you can provide is highliy appreachited.

What did you think of this month’s WAW? Like it? Hate it? Want better Chinese translation? Let me know in the comments.

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After talking about Omniture and January and Webtrends in February we are completing the Web Analytics trinity in March with a presentation about Google Analyics. In a premier for the Beijing WAW we have a guest from the Google Mountain View HQ to give us Web Analytics insights, straight from the source.

Stepahnie Hsu, Analytics Specialist at Google, will reprise her Google Conversion University talk for us, and dig into the integration of Google Adwords and Google Analytics. I have also asked her to share some insights into the new GA features like advanced segmentation and motion charts. Stephanie will be supported by Zhou Yang, Google local GA support specialist, for translation and localization.

In another first, and courtesy of OMD’s Sidney Song (宋星) we will also be able to record the event on video and share it with those of you who cannot attend.

Please join Stephanie, Zhou Yang, me and 30 – 40 other web analytics enthusiasts to learn more about web analytics, meet other web enthusiasts and have an all around great time. Bring any friends who might be interested to join our community along as well to:

Location: Luga’s Villa (right behind 3.3 in Sanlitun)

Time and date: Wednesday March 3rd  4th , 8PM

We will have a buffet dinner and soft drinks available for our guests. Be prepared to spend RMB 50 for the evening. As usual the knowledge you get in exchange is invaluable

Please RSVP by commenting to this post or drop me an email florianpihs[at]gmail[dot]com.

P.s.: Thanks Stan for helping me to correct the date

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I just got an email from Stephane Hamel announcing V 1.02 of WASP, his Web Analytics Solution Profiler utility. Congrats and kudos to Stephane. Good job. Rock on.

For those who have not heard about WASP here is the skinny:

WASP is a Firefox plugin for Web Analytics professionals that allows you to easily do

• Quality Assurance:  Check If your own pages are correctly tagged, that is
  • If there is a web analytics tag on your page (works for ad tracking tools, A/B testing tools and e-commerce tracking as well). This works page by page, but also automatically with a crawler if you are using the licensed version.
  • If that tag is executed correctly and is sending the right data to the Web Analytics server (e.g. the right profile ID in GA, or the right Omniture SAINT tag). This works especially well for Omniture and Google Analytics, since WASP provides an “enhanced tags view” that explains each data point sent to the server.
• Market Research:  If and page you are visiting has a web analytics solution installed (e.g. your customers, your competitors, or your own business in other countries)

I have been an enthusiastic WASP user for more than a year and encourage you to test it out. My only reservation is that it doens’t work well for Flash “experiences”. To Q&A those, we have been using HTTP Watch and looked at the data sets send to the server manually. I am looking forward to version 2.0,  so I can ditch HTTP Watch completely.

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So many great news today! First Omniture’s announcement about the Baidu, then the news that Google Analytics has a new Benchmarking function. Lets talk about the GA Benchmarking first, so I can focus on Omniture / Baidu in a longer post during the weekend.

Why Benchmarking?
Reporting is easy, providing actionable recommendation is less so. In an earlier past I recommended a 3 step approach based on answering 3 simple questions:

1. Is this number good or bad?
2. Why is the numbers good or bad?
3. What can we do to improve this metric and is it worth the effort?

In order to answer questions 1 and 2 it is critical to know how well other players (especially competitors) are doing, since comparing just to your own numbers will leave you in bubble without context for question 1 and without best practices for question 2. Find more details in Avinash’s post on competitive intelligence.

Why is this announcement so important for China?
Regular readers of this blog might remember my rants about the lack of benchmarks in China. While you can benchmark you reach (unique visitors) with some accuracy using IResearch’s iUserTracker, key industry benchmarks like CPC, CTR or Bounce rate are unavailable in China. There is not even an industry organization like the IAB that
conceivably could report such numbers. As long as you don’t have a large sample of clients or long experience in the market, you are out of luck (just barely avoided a 4 letter word here)

How useful will it be?
Well, that depends. I just singed up for the service (you need to agree to share you own data anonymously first) and will share more comments in the future, but I see a number of limitations.

• The benchmark needs to be relevant to my industry, GA allows you to choose among a number of verticals to address this issue. So the value will depend on how close this match is.
• The benchmark needs to be relevant to my geography. Most benchmarks are widely different in China than they are in the US (or in Japan, or in Germany for that matter). So far it seems this problem is not addressed. That is a key weakness for us poor web analytics souls in China an without this feature, GA benchmark will remain a nice graph is the system.

Yours truly will of course immediately get in touch with his local Google resources and see what he can did up. Stay tuned for more. [Update: My friends at Google confirmed that the benchmark is global. No luck for the wicked]

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