Recently a friend asked me for some pointers on the measurement of social media, such as blogs.

I have found that Jeremiah Owyang has a lot of interesting things to say on this topic, as exemplified by has article What should we measure and the document Tracking the Influence of Conversations: A Roundtable Discussion on Social Media Metrics and Measurement.

Yet it isn’t sufficient that we measure conversation on the web, we must also consider potential traps hidden in the data – we need to interpret it.

Consider the case of a blog post. The extent that a post has been read and has involved a blog’s readers might be deduced from the number of comments the post has generated. Two potential problems arise using this metric.

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I couldn’t help but notice the reopening of Italy’s primary blog classification service, BlogBabel. Just over a year ago I wrote about BlogBabel:

“While it is worth keeping in mind that BlogBabel’s ranking is just one measure of the importance of a particular blog, Ludo deserves kudos for the transparency in which BlogBabel’s rankings are calculated.”

Since then, the ranking factors have changed a bit. Currently BlogBabel says the following parameters are considered¹:

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I found an interesting announcement over at 97thfloor.com of a new Web Analytics tool, BLVD Status.

Two significant impediments have historically hindered the uptake of Web Analytics by businesses. The first has been cost. Professional Web Analytics systems have been fairly expensive, both in server and hosted forms. The second issue has been the great quantity and complexity of available reports in commercial systems, sufficiently intimidating many business professionals away from Web Analytics.

Google’s Google Analytics

Google, with their release of Google Analytics in November 2005, removed the first obstacle, cost, by releasing the first free “full featured” Web Analytics system. Previous free tools, such as AWStats, lack robust visitor recognition and click stream analysis, among other things. Yet a significant obstacle still remained to general Web Analytics usage: how to find the “important” data, without getting lost in a sea of confusing and often redundant reports? In May 2007 Google released an updated Google Analytics with a significant focus on the user interface, specifically as a response to this need.

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In the course of a search engine optimization project, I’m often asked during the competitor analysis phase why one website ranks highly in Alexa, while another ranks highly in an other statistics supplier’s top websites survey.

As our two part article Web statistics for internet market research: pick a number, any number illustrates, there are a plethora of services offering web statistics. Website owners often cite their Alexa rank to demonstrate how much more important their site is compared to a competitor’s. Other website owners will pay for Nielsen//Netratings panel research, a sampling technique born in the 1930s which surveys about 0% of Italy’s adult population – no wonder then the IAB has called this technique outdated.

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Expats in Italy need to stay on top of professional and daily happenings locally while still engaging in the wider world. This task is made difficult by the vast quantity and quality of resources available in English (my native language), as exemplified by the BBC. Unfortunately, their Italian equivalents, such as the ad-infested public broadcaster RAI, just can’t compete for my attention.

It doesn’t get much easier on the web marketing front. The primary search engines in Italy are the US based Google, Yahoo!, Microsoft Live and Ask, sometimes found in their rebranded skins: Arianna (enhanced by Google) and Virgilio (listed by Google as a customer). Inevitably, most of my web marketing reading is English language centric.

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