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Internet Marketing and Web Development in Higher Education and other tidbits…

Mobile Analytics Revisited (Have You Been Keeping Up?)

04 Nov 2009

written by Michael Fienen

Mobile Analytics Revisited (Have You Been Keeping Up?)

This past March, I wrote a tutorial that described some techniques of measuring mobile traffic in Google Analytics.  Believe it or not, you can’t just set that kind of stuff and forget it.  In just the eight months since then, the topography of the mobile landscape has changed, and I wanted to share some changes that I’ve made to improve tracking of mobile devices as a result.  The key sign for me was when I logged in and noticed we were missing all the newer smartphones (unfortunately I don’t keep close tabs on this report due to resources) due to a minor issue with the resolution filter I originally described.  Collecting and interpreting analytics well requires a fine hand and attention to the metrics and what they mean; using a set it and forget it mentality is better than nothing, but will prevent you from getting at the best information you could collect.

The key problem is that the original screen resolution filter described in the first article operates on a now false premise: mobile devices are sub-VGA in resolution.  By now I’m sure you’ve heard of Droid, the new Android 2.0 based smartphone from Motorola.  Now, that is just one example, but it’s hitting a whopping resolution of 854 x 480.  The HTC Touch Pro2 runs 480 x 800, and even it’s older sibling was 480 x 640.  By comparison, the iPhone and iPod Touch’s resolution is 320×480.  Higher res. screens on mobile devices are the new trend, one that I’m not entirely convinced that they won’t possibly rival netbook resolutions soon.  Sure, we could just add some of these resolutions to our initial filter (which I did), but that doesn’t entirely solve the problem.  This is especially true since once you break the barrier of VGA resolutions, you run the risk of sniffing out normal computers.

My solution is now a two pass system in our Google Analytics mobile traffic profile.  I’ve kept the resolution filter in place as the starting include filter since that’s still the best and most reliable place to start, and modified it to account for the higher resolutions.  I took a simple approach and just added in the main increments that we’ve seen on the upper end of the spectrum: 480, 640, 800, and 854.  You could just as easily simplify the entire thing to grab everything 854 and down (you’ll see why this works in step two) as that seems to be the current top mark for phone resolution at the moment.  My resulting regex for the new include filter based on the old one is:

^([1-2]?[0-9]?[0-9]|3[0-6][0-9]|480|640|800|854)x([1-3]?[0-9]?[0-9]|4[0-8][0-9]|800)$

A simpler approach (which matches anything 899×899 or less) could just be:

^([0-8]?[0-9]{1,2})x([0-8]?[0-9]{1,2})$

That’s step one.  The issue is that you are guaranteed to catch normal Windows users with either one of those filters (Apple machines almost always have higher resolution than that, even older machines, but I’ve learned there are no absolutes in analytics).  That’s where the second pass comes in to play.  After the first filter, which tells the profile what to include, set up a custom exclude filter on the field “Visitor Operating System Version.”  For the pattern, have it exclude matches for:

(95|98|ME|XP|Vista|NT|10\.[4-6])

This will get rid of all relevant versions of normal desktop Windows (Windows 7 reports as NT currently, as far as I can tell) and OS X, and leave you with a nice pool of mobile users to paw through.  This should even allow you to scale up the resolution filter as smartphone screens improve and maintain your reporting accuracy, though you might want to add Linux to the exclude filter then too.

If anyone has come up with what they feel is a better solution, I’d love to hear about it in the comments so we can discuss ways to improve upon the method I’ve offered here.  So far, I’m very happy with the results this is producing for us, but we also have a pretty small mobile device user base, so it’s not easy for me to tell how well this might work in a larger environment where more variables might come in to play.

The content of this post is licensed: The post is released under a Creative Commons by-nc-sa 3.0 license


About the author

Michael Fienen

For six years, Michael served as the Director of Web Marketing at Pittsburg State University. Currently, he is the Senior Interactive Developer at Aquent and is also CTO for the interactive map provider nuCloud. When it comes to web communication, he focuses very heavily on interpersonal communication components of websites, as well as content considerations that must be taken into account when building usable sites.  He is an active supporter of the dotCMS community, accessibility advocate, consultant, internationally featured speaker on web issues, and general purpose geek who wears many hats.

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  • http://www.draftmotif.org Paul Prewitt

    Also you could look under the Visitors > Operating Systems to get a quick listing to see what the top mobile devices are. However for this option you’ll have to know what the phones are called.

    Our list is:
    iPhone
    BlackBerry
    iPod
    Samsung
    PalmOS
    Android
    Danger Hiptop

    You’ll even be able to get the odd ball fun ones like the Playstation 3 on this report.

    After that I’d recommend using an Advanced Segment to create something for like your top 5 mobile devices. This will give you the option to see how their stats compare to the overall stats.

    There are positive to both methods (profile/segment) but whatever you do please do something.

    • http://www.fienen.com/ Michael Fienen

      Yeah, I discussed that some in the original post. The challenge there is then you REALLY have to keep up with things to make sure your filter is getting all the phones, and you have to keep up with all the new ones coming out, which was just way more work than I’d want to do. My example helps out a little there, by making assumptions about mobile devices. But, that carries it’s own risks. You just have to balance your needs with what produces the most relevant reports for your needs.

  • Curt Grymala

    I had a thought while I was checking things out on my iPhone this evening.

    Are there any non-mobile/handheld devices that use portrait-oriented resolutions? I can’t think of any. Therefore, I would think a good way to capture a good number of mobile/handheld devices (you’d still need other filters to catch the landscape-oriented devices) would be to set up a filter to figure out when the height is greater than the width.

    • http://www.fienen.com/ Michael Fienen

      I’d be interested in how you could check for that with regex, nothing comes immediately to mind how you could apply that logic to a filter in Google Analytics. I would caveat you on that though, some people (not many) turn widescreen monitors 90 degrees for portrait mode (I’ve seen authors do this when writing). Likewise, tablet PC’s tend to be used in portrait mode like a sheet of paper. Even some mobile devices report inconsistently whether resolutions are portrait or landscape. But like I said, I’d be interested to see if someone can come up with some filter logic that would work.

  • http://www.blogbisnisinternet.com Bisnis via internet

    Mobile marketing is future marketing online. Thanks for measurement technique.

    David

  • https://twitter.com/mobilepornvids Mobile Guy

    Mobile is the future. I have seen it.

  • http://www.iup.edu/ Michael Powers

    Another way to pull out the mobile data is through an advanced segment. I’ve set one of these up with a series of conditions that filter down to only mobile operating systems (so this is a bit like Paul’s filter).

    Disadvantage: you’ll probably be seeing sampled data. For me, this isn’t a huge problem–I want to see trends, I want to see which pages mobile users go to and which they avoid. But a filter might be more accurate. (Accurate in the analytics sense, which means not really accurate, of course.)

    Advantages:

    1. Filters can only show data starting from the moment you create them. So if you create a filter today, you’ll start seeing data tomorrow, but it will be several weeks before you can start judging any trends. The advanced segment operates on your existing data, so you’ll see data immediately, and you be able to look at past data, too. (I’m not sure if there’s any limit on how far back you can go–I’m able to see mobile traffic all the way back to the launch of our new site in January 2008.) In our case, the data shows that mobile traffic to our site has increased by a factor of 20.

    2. You can apply the advanced segment to any existing report or profile. So if you have filters to separate out internal and external traffic, you can then apply the advanced segment to that filter for further analysis.

    3. You can compare advanced segments in the same report, which makes it easy to compare, say, all your traffic with the mobile traffic to see what percentage of your total traffic comes from mobile devices.

    Any disadvantages I’m missing here? I should set this up as a filter, as well, and compare the data, but in general this approach meets my needs pretty well.

  • http://www.tsshvac.com HEPA Filter Portland

    That sounds really useful. I don’t think that phone resolution will ever match notebook resolution though. Every time they decide to make phones better, someone else is going to make noteboooks better as well.

  • http://www.motoroladroid-a855.com/ droid lover

    The analytics is not an absolute. There are other tools available to track the parameters. I use my droid to do my searching while on the go. So far i am happy with the new Android platform. The features and apps are evolving.

  • http://isubisnis.blogspot.com/ Peluang Usaha

    thanks for information about Mobile Analytics Revisited

  • http://wessmencariuang.blogspot.com/2010/01/bagaimana-cara-mencari-uang-di-internet_26.html Antoine Osdoba

    seru juga belajar bisnis, thanks buat infonya.. sukses bro..

  • Rinoe

    Great posting about mobile analytics Michael, I learned a lot from your posting!

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