8 comments for Influential Authorities on Blog Marketing

1. How I was listed as #2 is quite a bit of a mystery. We’ll see if they publish any additional details on their research.

   Comment by Josh Hallett — July 23, 2006 @ 12:09 pm

   
   

2. Some great names on this list, but it feels like some great ones are mising too. Never room enough for everyone I know.

   Big congrats to Mitch Joel for making the list.

   Comment by C.C. Chapman — July 23, 2006 @ 6:31 pm

   
   

3. When they published the report on influential authorities on “business blogging” they did provide more details on the Onalytica.com site. Am not sure if they are going to re-publish more methodology information or not. I would hope that they would.

   Either way, good exposure for you Josh.

   Comment by Lee Odden — July 23, 2006 @ 7:52 pm

   
   

4. It’s all left me feeling a little bizarre for two reasons.

   1. I don’t really get their methodology
   2. As far as I can see there are at least 5 or 6 ways the term ‘blog marketing’ are being used on the blogs and sites listed. Is it measuring people talking about marketing blogs (thats how I use it) or talking about marketing blogs or is it blogs talking about marketing…. etc

   It’s interesting but a little odd also as far as I’m concerned.

   Comment by Darren — July 23, 2006 @ 8:36 pm

   
   

5. […] For more on the study you might also like to read hyku (2nd most influential – congratulations Josh) and Business Blog Consulting. If you enjoyed this post Subscribe to the Free ProBlogger Newsletter […]

   Pingback by Analyzing Influence and Popularity of “Blog Marketing” — July 23, 2006 @ 8:40 pm

   
   

6. Methodology: The methodology is described in full detail in the previous analysis on “business blogging� which is referenced.
   In short, however it goes like this:
   1) Anything our crawlers could find on the internet that had the phrase “blog marketing� in it was downloaded.
   2) Links and textual references (in all: citations) from one stakeholder to another was extracted. (This is mainly an automatic process but includes some manual consolidation. Example: http://www.nytimes.com and http://www.times.com are in fact that same.)
   3) The citations were turned into equations as done in an input/output model.
   4) Equations solved with respect to influence.
   5) Popularity established by counting number of citations. Popularity normalised to fit with influence range.
   6) Influence and popularity compared.

   Re 3: Imagine a blogger references 3 others in the context of “blog marketing�.
   We then assume that this blogger is (to some smaller or larger extent) are influenced, on this topic, by these 3.

   The blogger is also influenced by a big blurry cloud of other stuff and other influences. We can disregard this group IF it doesn’t contain the same sources of influence for all stakeholders since it is then not systematic.

   Said bloggers contribution to the influence of the 3 he is referencing is now [his own influence] times on third.

   But what is his own influence? That is what we get when we solve the simultaneous equations.

   For a more technical explanation, read this [http://en.wikipedia.org/wiki/Input-output_analysis] article and imagine the sectors of the economy substituted with stakeholders (represented by their website)

   Comment by Flemming Madsen — July 24, 2006 @ 2:54 am

   
   

7. I emailed Fleming Madsen of Onalytica about the comments in this post and here is his reply:

   Methodology: The methodology is described in full detail in the previous analysis on “business blogging” which is referenced.

   In short, however it goes like this:
   1) Anything our crawlers could find on the internet that had the phrase “blog marketing” in it was downloaded.
   2) Links and textual references (in all: citations) from one stakeholder to another were extracted. (This is mainly an automatic process but includes some manual consolidation. Example: http://www.nytimes.com and http://www.times.com are in fact that same.)
   3) The citations were turned into equations as done in an input/output model.
   4) Equations solved with respect to influence.
   5) Popularity established by counting number of citations. Popularity normalised to fit with influence range.
   6) Influence and popularity compared.

   Re 3: Imagine a blogger references 3 others in the context of “blog marketing”. We then assume that this blogger is (to some smaller or larger extent) are influenced, on this topic, by these 3.

   The blogger is also influenced by a big blurry cloud of other stuff and other influences. We can disregard this group IF it doesn’t contain the same sources of influence for all stakeholders since it is then not systematic.

   Said bloggers contribution to the influence of the 3 he is referencing is now [his own influence] times on third.

   But what is his own influence? That is what we get when we solve the simultaneous equations.

   For a more technical explanation, read this: http://en.wikipedia.org/wiki/Input-output_analysis article and imagine the sectors of the economy substituted with stakeholders (represented by their website)

   Flemming

   Comment by Lee Odden — July 24, 2006 @ 6:54 am

   
   

8. Yours truly, another faithful BBC contributor, was #8 in Onalytica’s Feb. 2006 study of most influential authorities on business blogging. I’ll have to look at the two studies side by side to see what the differences are. Interesting…

   Comment by Debbie Weil — July 24, 2006 @ 1:59 pm

   
   

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