In cas you ever wondered where your Google Analytics data goes if you opt in to share it with the world, here is your answer: "To improve the quality of our site traffic estimates, we have upgraded our traffic estimation model. Our model uses a hybrid methodology that combines sample user data, from various Google products and services, with direct measured site-centric data. The model's direct measured signal is pulled from Google Analytics customer accounts that have chosen to opt-in to sharing their data with Ad Planner."

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Looks like Google has given its webmaster tool a makeover and added a few new features with lots more data available now on search terms, back links and internal links.

Not necessarily new but still very useful are the crawl stats on how long it took Google to download a page from your site which makes performance benchmarking and basic speed testing available for everyone now.

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Have a look at GPS Visualizer's free geo-coding service below, it not only returns the latitude and longitude but also cleans the address data.

http://www.gpsvisualizer.com/geocoder/

Data entered (misspelled address): 150 william st Wolomolo australia.

Data returned (correct spelling appended at the end): -33.874455,151.218939,150 william st Wolomolo australia,"150 William St, Woolloomooloo NSW 2010, Australia".

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When running Windows IIS as your web server, you might find that the issue of upper and lower case URLs can very annoying and troublesome.

Windows is a case insensitive OS, meaning test.html and Test.html are actually physically the same file in Windows. However, in Google Analytics they will by default be recorded as two different pages if you don't do something about it. The following screenshot shows an example of the folder '/candidates/' and how it is tracked as two different folders simply because of the case sensitive issue.

It's very easy to fix the problem. All you need to do is to setup a filter in your Google Analytics profile to convert all the URLs into lower case or upper case. To do that, login to Google Analytics and click on Filter Manager, then add a filter as shown in the following screenshot. This filter will turn all URLs in Google Analytics to lowercase eliminating the duplication on the content and goal reports. Please note, this only works form the moment you create the filter and not in retrospect.

We recommend that all websites running on Windows IIS should have such a filter to avoid the problem caused by case sensitivity.

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http://browsersize.googlelabs.com/

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Looks like marketers are getting worked up about behavioural targeting without reason but Google could probably do a better job at promoting their opt-out service and raising awareness about the option as well.

Nine months ago, when Google first introduced behavioral ad targeting, it also rolled out an ad preferences manager. The page allows Web site visitors to edit their advertising interest categories -- for instance confirm an interest in cars or entertainment -- or to opt out of behavioral targeting altogether.

As it turns out, relatively few users visit the page, and among those who do only a small fraction opt out, according to Google. The finding suggests that those who seek out the page are predominantly comfortable with Google's behavioral ad practices.

"A good percentage of users are saying they'd rather control [behavioral targeting] than opt out," said Wong.

A rough calculation suggests that at the most, about 6,600 of Google's users are opting out of ad targeting per week. The figure is based on a generous estimate that the site captures 99,000 visitors on an average week -- the most possible if the site is indeed capturing tens of thousands yet not more than 100,000 visitors, as Google indicated to ClickZ.

Under this estimate: 6,600 (number of users opting out) x 4 = 26,400 (number of users editing preferences) x 10 = 66,000 (number of users taking no action). The sum of those three is 99,000 -- again, the maximum number of weekly visitors to page, according to Google.

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A little while back, Google discretely introduced a “Show Options” tab that lets you filter your results by media, time or in other interesting ways. Filters are not new, but combined with Google’s awesome search, have become quite useful to me.

Filtering by Visited Pages. Google is being used more and more as a navigational device towards content you already know. Google is good, but if you’re deep in research sometimes it’s hard to find that site again, and this is a brilliant tool for this purpose. 

This behaviour is not uncommon, and an interesting trend I don’t feel is getting enough attention. Search has become less and less about discovery, and more about meeting basic usability requirements about finding existing content... Something search marketers are adjusting for. How often do you already know exactly what content you are looking for before you search? What chance do the other sites have of capturing your click for that impression? if you’re a search marketer, how do you adjust your optimisation metrics to cater for navigational search and usability from something traditionally focused on acquisition?

Here is my search for Domain names. I searched for this last week and couldn’t remember the name of the site which I decided was the way to go. 

Another feature in the options is the wonder wheel – we haven’t seen a lot of people using this from our analytics data, and I am thinking it probably would be more useful for internal search where you have a more defined content set. There are some implications for search, and with adoption will come more users entering your site via more specific keywords and the reduction in broad search terms.

We’ll be interested to see adoption rates of these filters  and any considerations for search marketing – we’re monitoring our logs and see about 5% of visits contain at least 1 filter, and will be looking to use this data in the future to enrich insights around keywords and what users are looking for when they use them.

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All marketers would have seen the below graph before and understand how the theory behind it impacts their marketing campaigns but most might wonder in which phase certain products actually are. Some might know for their own products based on sales numbers but these might not be that easy to come by in large organisations and when it comes to competitor products the guesswork really starts.

So why not use search term trends provided by Google free of charge to establish what lifecycle stage a particular product is in? The search term volume over time shows the change in interest in the product pretty well. Check out the below search term trends for the most popular N-Series Nokia products and how closely each resembles the lifecycle curve vs. the iPhone.

The below graphs shows nicely how the interest in the respective Nokia product grows over time with adoption and then finally drops back down with new products being introduced. Overall sales numbers probably correlating quite nicely with search term volume.

Interesting is the trend for the iPhone. As you can see it doesn't follow the standard product lifecycle curve at all but so far manages to keep growing rather than declining which is a prime example for how you can keep products alive by introducing additional features and services. Just think about the iPhone integration with iTunes and the growing number of applications and you get the drift.

Read some more about the product lifecycle theory here
http://en.wikipedia.org/wiki/Product_life_cycle_management

Check out the original Nokia Google Trends data here
http://www.google.com/trends?q=n95%2C+n73%2C+n96%2C+n70%2C+n82&ctab=0&geo=all&geor=all&date=all&sort=0

Check out the original iPhone Google Trends data here
http://www.google.com/trends?q=iphone&ctab=0&geo=all&geor=all&date=all&sort=0

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It seems to me that more and more businesses now have corporate twitter accounts and given that most blog publishing platforms now have Twitter integrations, this has become the channel of choice to follow business news updates rather than RSS. The below Google Trends search term data doesn't prove that but at least supports the theory to some extend. Your thoughts?

http://www.google.com/trends?q=twitter%2C+rss&ctab=0&geo=au&geor=all&date=all&sort=0

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