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Jan12
Not So Social Networks
NextStage: Predictive Intelligence, Persuasion Engineering, Interactive Analytics and Behavioral Metrics I was listening to NPR's Morning Edition this morning and was intrigued by David Kestenbaum's story "How iMet My Neighbor on iTunes". It is a cautionary tale for companies wanting to work social networking components into their websites, and an even more cautionary tale for people making use of those tools.

In a nutshell, Mr. Kestenbaum made assumptions about others in a software engineered social network and those assumptions were not shared by the others in that software engineered social network. I think this is an important example of something I described in the Online Identity arc.

I don't know much about Mr. Kestenbaum other than to hear him on NPR once in a while. Based on those few elements, though, I non-consciously make some assumptions about him. The assumptions I make about him are that he's obviously intelligent, obviously erudite, obviously well read, ... I'll throw in obviously virile and obviously handsome while I'm at it because the assumptions are really things I'd like to think are true about myself. They may or may not be, but hey, I listen to NPR and I'd like to think those things are true about myself so they must be true about other NPR listeners.

And, my god, if they're true about NPR listeners, can you just imagine how true they must be for NPR's online commentators?

The error here is actually pretty common - assuming a shared concept of one thing equals a shared concept of all things. This is similar to the Equality and Identity conundrum; Equality and Identity are not the same thing. To say all men are created equal is not to say they are recognized as being equal. Don't confuse the "what" with the "who". To say equality equals identity is to say that all men are one man, and the actions of one man are the responsibility of all men.

This error, though, can lead to a misplaced trust (think Hostage Syndrome over the internet). They like the kind of music I do so they must be just like me. Not!

They listen to NPR and to the same shows I do, so they must be just like me. Not! (trust me on this one. I've taken part in some NPR fundraisers. Not all NPR listeners are the same)

And this, of course, feeds into our arc on noisy data and designing new metrics because just as the reason I'm sitting here typing isn't the reason you're doing it, the reason I listen to NPR isn't the same reason you do (it can be, doesn't have to be) and just because I listen to the same music you do doesn't mean we like the same food or drive the same car (despite what marketers want to think).

Noisy data is, to me, like junk DNA. We use to think there was a lot of DNA that was meaningless, just taking up space in the helix. Then we found out there is no junk, all of it has meaning, you just have to know what it means.

Noisy data is very much like that and plays a crucial role in microtargeting and market segmentation. We'll be concluding the noisy data arc over this coming weekend. Stay tuned...

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» Standards and Noisy Data, Part 4 from BizMediaScience
Continuing "Are we measuring acts or the reason for the act?" with "What are we able to measure?" versus "What would we like to measure?" [Read More]

» This isn't customer service, Part 3 from BizMediaScience
Customer Service and deBranding, Part 3 [Read More]

» This isn't customer service, Finale from BizMediaScience
Customer Service and deBranding, finale [Read More]

» Mistaken Identities, Part 2 from BizMediaScience
Separating Who We Are from Whom We Want to Be, Part 2 [Read More]

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