Comments on: Traffic Truth Tricks from Seth Godin and why they don’t work http://www.webmetricsguru.com/archives/2009/12/traffic-truth-tricks-from-seth-godin-and-why-they-dont-work/ Web Analytics, Social Media and Search Marketing Tue, 24 Jan 2012 15:53:57 +0000 hourly 1 http://wordpress.org/?v=3.3.1 By: Joseph Fiore http://www.webmetricsguru.com/archives/2009/12/traffic-truth-tricks-from-seth-godin-and-why-they-dont-work/comment-page-1/#comment-3748 Joseph Fiore Mon, 14 Dec 2009 17:34:50 +0000 http://www.webmetricsguru.com/?p=6089#comment-3748 Great stuff Marshall! I think there are challenges with imparting useful measurement strategies partly because a lot of it is in our thoughts and day-to-day practices, and not all of it can be explained coherently or applied in a one-size-fits-all way. The biggest challenge with noise reduction is false positives and computational filtering which isn't suited to identify and look through the layers of context and meaning. On your calls regarding keywords and advancements in areas of topic, category or url, I think there is definitely some room for improvement. However this gets complicated by the mere fact that "automagic" hasn't worked well enough to explore topics of sentiment when "bad" is not used in the pejorative sense. The predictive or futurist elements of following a meme are noteworthy, but cumbersome on machines already ill-equipped to handle the expanded uses of sarcasm or foreign cultural or linguistic references. Furthermore, the evolve and uses of url shortening services not only makes it difficult to track references in every area of the social Web (comments, message boards, walled-gardens), to say nothing of the problems with identifying malware threats when they are used to mask suspicious link and spamming activities. No doubt that these are important layers to peel back and many questions on how we approach them, but I think we run into the danger of putting the carriage before the horse when we demand monitoring with measurable outcomes, and in doing so, leapfrog into other areas without getting foundational aspects right. Joseph @RepuTrack Great stuff Marshall! I think there are challenges with imparting useful measurement strategies partly because a lot of it is in our thoughts and day-to-day practices, and not all of it can be explained coherently or applied in a one-size-fits-all way. The biggest challenge with noise reduction is false positives and computational filtering which isn’t suited to identify and look through the layers of context and meaning.

On your calls regarding keywords and advancements in areas of topic, category or url, I think there is definitely some room for improvement. However this gets complicated by the mere fact that “automagic” hasn’t worked well enough to explore topics of sentiment when “bad” is not used in the pejorative sense. The predictive or futurist elements of following a meme are noteworthy, but cumbersome on machines already ill-equipped to handle the expanded uses of sarcasm or foreign cultural or linguistic references. Furthermore, the evolve and uses of url shortening services not only makes it difficult to track references in every area of the social Web (comments, message boards, walled-gardens), to say nothing of the problems with identifying malware threats when they are used to mask suspicious link and spamming activities.

No doubt that these are important layers to peel back and many questions on how we approach them, but I think we run into the danger of putting the carriage before the horse when we demand monitoring with measurable outcomes, and in doing so, leapfrog into other areas without getting foundational aspects right.

Joseph
@RepuTrack

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