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Customer Insight e Social Analyticsdai BIG data ai RELEVANT data

Big Data Analytics ConerenceRoma !" #tto$re %&!'

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I numeri della (arteci(a+ione

22

30%iscri i a

Brand Community

46%Iscri i a Forum

83%

iscri i aSocial Network 

17%Bloer

Base: Totale ca mpione – 1.500 (wave Marzo 2013)) Dati in / 

!8"6 mio utenti internet

0 Accedono tu i i gg: 12/)(ostano !* gg: '!/

0 

3i4 da cellulare che 3C: !!/

0 3ostano loro stessi s(esso: !%/0 Con (ro(rio nome: '5/) di antasia:

2'/

0 

6re7uentano ogni giorno: 8/0 

Circa una *olta a se imana: %9/

0 Aggiornano contenuti alm- !* se imana: !%/0 Seguono ! o (i4 $logger con continuit: '!/

0 Gli ambienti soc ial vivono i ro!tine spesso "!otiiane# immetteno in retecontrib!ti i vario $enere# tra iee# pensieri# proprie ientit%

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3arlanodi

me;

Chi;

Cosadicono;

3arlano$eneomale;

<e$ Listening

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= una com#rensione delledinamiche di mercato

che attiene alle moti$aioni umane)

che si maniesta come una sor#resa)reale e rile$ante (er il

consumatore-

3ro*iene dal consumatore >dal suo &in'si(t)?

ed @ in grado di guidare i risultati di$usiness-

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3arlanodime;

Chi;

Cosadicono;

Cosa(ensano;

3erch;

ualeim(a o;

Digi tal I nsights

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Il (rocesso di consumer insight generation

!- 

Si (arte dagli o$ietti*i

%- Si deCnisce larchitettura della Social Eedia Analysis

'- 

Si identiCcano le tecniche di raccolta e analisi

2- Si sceglie il craFler e il sF di analisi

1- 

Si studiano le modalit di deli*ery

8- Si generano consumer insights

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10

Il rischio di adottare strumenti inadeguati>tro((o o tro((o (oco soCsticati tecnologicamente?:

aumentare la com(lessit e il caosG

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Lim(ortan+a degli standard

H 

DeCni+ioni e tassonomie

H Eodalit di sele+ione delle inorma+ioni

H 6rames inter(retati*i

H 

 Tecniche di analisi

H Sistema di 3Is

H 

Routine o(erati*e e controllo 7ualit

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La tecnica di ricerca a$ilita lagenera+ione di insight

JBuel che *edi di(ende dal tuo (unto di *ista-3er riuscire a *edere il tuo (unto di *ista) de*icam$iare (unto di *ista-K Marianella &clavi

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