Anita prinzie main stage - 2012

Anita Prinzie, Solutions-2, Belgium Festival of NewMR 2012 - Main Stage - Session 3

A Presenta*on from The Fes*val of NewMR – Main Stage

5 December 2012

Five steps from Big social data to Smart social data

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Anita Prinzie Solutions-2


Big Social Data From Big Data to Smart Data

Prinzie Anita


125,000 100,000 50,000

OR

150,000


48 hours 100,000 650,000 28,000

OR


http://datasift.com/stream/13991/mcdonalds#app1-preview


ORGANIC DATA DESIGNED DATA

Based on Bob Groves, former US Census director

CREATED TO GAIN SPECIFIC INSIGHTS DATA RICHNESS


BIG DATA SMART DATA ?


Define business objective


Brand perceptions Detecting crisis

Complaints & malfunctions

New product launch Consumer segments

Media campaign success

Based on Jasper Snyder Converseon

MONITORING LISTENING

Brand positioning

... ...


Identify relevant data


Who?

Which conversations?

How long?

LISTENING PURPOSE

RELEVANT DATA

MONITORING PURPOSE

BUSINESS OBJECTIVE

Which platforms?


Who Fit with business objective

IPhone 5 UK launch success (Vision Critical) “Should we listen to FB conversations of people not wanting the IPhone 5?”

27 53

65

44

8 3

All Want IPhone 5

Negative

Neutral

Positive

Battery


Profile on channel usage & engagement

15-24 25-34 35-54 55-99

Actively engaging on FB, Twitter, blogs?

Who & Which platforms


Which conversations Find the most relevant ones

Dictionary of typical actions during different phases of the software adoption process

Focus groups with B2B customers

Scoring all Twitter/blog conversations on the software adoption phases.

B2B Software Adoption Journey

Software adoption journey-conversations


How long? Find relevant time window

Natural time window

‘Enough conversations’ time

window

New product launch: 90 days

before and after (Microsoft)


Clean & Preprocess


Clean and Preprocess

Keep emoticons and markup

for detecting crisis

Context specific normalization

and annotation (e.g. Convey

API)

Keep goal in mind


Analyse with objective in mind


Detecting crisis

Identifying complaints and malfunctions

Monitoring response to new product launch

Detecting gradations of negative and evolutions

Detecting gradations of negative

Detecting gradations of positive and negative

SENTIMENT ANALYSIS MONITORING PURPOSE


Remedies

Cost-sensitive learning Undersampling of neutral class Use recall, precision and F1 measure to evaluate model

Correct 80%

7%

90%

3%

Positive Neutral Negative


Validate results


Validate social media results

The evolution of the Microsoft

software adoption index did

follow known success/failure

trends for past software

launches.

Denali

O365


SMART DATA

Define business objective

Identify relevant data

Clean & preprocess

Analyse with objective in mind

Validate results

BIG DATA


Q & A

Ray Poynter VCU

Anita Prinzie Solutions-2


risingquestions If you have any

Anita Prinzie [email protected] @AnitaPrinzie


References

¡  http://www.domo.com/blog/2012/06/how-much-data-is-created-every-minute/

¡  Snyder, J. (2012), Enriching Social Data for Market Research Converseon, New MR Webinar, Social Media Research, October 9th 2012.

¡  Woolmer, J. (2012), Is it real? Using conventional research to validate and quantify social media findings, Vision Critical, New MR Webinar, Social Media Research, October 9th 2012.

Marketing

Anita prinzie main stage - 2012