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Predictive Power of Web 2.0 Data
Predictive Power of Web 2.0 Data
Predictive Power of Web 2.0 Data
Predictive Power of Web 2.0 Data
Predictive Power of Web 2.0 Data
Predictive Power of Web 2.0 Data
Predictive Power of Web 2.0 Data
Predictive Power of Web 2.0 Data
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Predictive Power of Web 2.0 Data

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From Web 2.0 Summit (San Francisco, California), Global Research, Hitwise.

From Web 2.0 Summit (San Francisco, California), Global Research, Hitwise.

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  • 1. hitwisé Antxpovonron-ouvy Predictive Power of Web 2.0 Data Hitwise High Order Bit Bill Tancer GM, Global Research, Hitwise
  • 2. Hyper-innovation Requires More Timely and Complete Metrics for Actionable Insight
  • 3. 2 .5"£ In nwators Roger’s Adoption Curve Late Majority 34 '7; Speed of Adoption is function of: p = initial speed of adoption q = speed of later growth
  • 4. 2.0 Adoption Compression D1’ .4 Jt .4 '_‘. ' 14 1] 0: . '_ . 't .1 ‘.6 1') U : .,. . 1.. .. il ‘-1. la. .. 1.. . 1-. .. M. ., 1.. .: ..J mi r. --. '. -I : ,.. | wi~'. ‘. rc_tubs xvfi I i: eo.1:9a‘r: h.'. sho: «: (i' I ‘. i:eo. c 300 9-: orr. hl= |l"IIlllOl2lIi1Kl7'LI': VD)| >< manual! .-I<v-W tnodu Jscnqo _ _ I. -< 11:! U! I) 41.1: . -9-. .:-, ~.utIvv1.‘i. '.o.1-n~. a 2:11.. ux, n. _., ,.. , ‘, ,., ,t, .., .._; ‘.. .,. I D nnouugn
  • 5. Finding the Tipping-Point J. ‘."(- ‘ ‘. n ‘1I‘‘. ‘ , , i 41f‘. I H , , . ~ I I «. v 23.0.- t I , ‘ ‘ I . ;‘lC‘- ! I ‘ ‘ ' an In Mr J: A. ) It: 3'- C‘ (' '07 ‘I17 C’ r . .. V ‘ n. ’ , I -. 'u(| 'nIv arr nu ‘- ma -rvrarl ~- av! hat K I ' ‘ Cc'I: I.: eI: art (‘.5 '15’ 3Mv<[r; r2x I ‘ 4.. :I. ,-. ..s. .: _. . . ..'uI». -.. .'I . :.. u~. ‘ _ - _ ’ I . M -»-- _ i hit‘. . -. __ > . «nu IU . : . u;. ¢; .0“-R11 . .r-. vu . s:: . In: nu , .u. ., . . . .3; , / I I — — . . _. J .3.‘ _ 1 / T I I __ __ _. . I _/ I , _i —): :~: i5‘= I! ll 21 I; it ('9 Z) 0' :1 0| ‘.5 0'. H‘ . ':' '2'; M» In J. 'i .11.‘ JL‘ “)1 :11 54: ‘ff! ’ . ‘I“! ‘ ». ~‘. .: :.IJ"= t vi-ml mow-1| . | . m 1- Cu“. .u. ' - . n. - L . .u. I. ... : m . Is . ... ,. ‘ V ‘ -nu. . I- it w'. ."'r‘i7.r; "-. ' , . , u ~9== -mrwi» u. .. D--« ul.
  • 6. Identifying the Early Adopter :3. mosaic III USA - Yuunq Cusmupulilnns Des! describe! IL ‘zesidents are young, s nqle. C’. || e-Z19 ecutated and eamino upper-middle-class nzomes as whi: e-rollar pm"essicr. aIs, man: --Jens and ex= :r_ut «es liv my in luxury at-aitmens and -; on:1os n fast QICV»/ IT: -J cut as 2.52": I‘ IIOVIIOV5
  • 7. Filtering for Trends ‘ -J| r~. i| 'iI: i . ._, ,. I N v E H ‘ ’ -'V1~| icIAn u-VIIFIRI l‘- I _II1 ‘ , mw» ' U «. ‘ . ' r _ F . r‘. /,. -«_Hc= O!comr>mv ' / , 1 ‘ / ‘ ‘ V e-: ——--’ - ' ~ ‘ I f ' ‘ ‘ . ) 5‘. '. .,. . - __ __ ' / ’ / I I I / F ’ I I I’. __ ,1/, 5 "' ,1 '. ,-4 <5’? I ' / ___ -—-, ': ::I. «.: DiA J -7. Lily I I’, vzfl‘ CO: -| 'IOn"3 &1/M . /,4; . 'Z'Z' : v . - / .4
  • 8. Find out more: - Our Blog: www. iIovedata. com - Science of Search on T| ME. com Bill Tancer General Manager, Global Research 415 597 4201 biII. tancer@hitwise. com

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