The document discusses transforming big social data into smart social data through a 5-step process: 1) define the business objective, 2) identify relevant data sources, 3) clean and preprocess the data, 4) analyze the data with the objective in mind, and 5) validate the results. It provides examples of applying these steps, such as detecting sentiment around a new product launch or identifying complaints on social media. The overall process aims to extract meaningful insights from large amounts of social data by focusing on quality over quantity.

1. Anita Prinzie, Solutions-2, Belgium
Festival of NewMR 2012 - Main Stage - Session 3
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2012
Five steps from Big social data to Smart social data
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Anita Prinzie
Solutions-2

2. Anita Prinzie, Solutions-2, Belgium
Festival of NewMR 2012 - Main Stage - Session 3
Big Social Data
From Big Data to Smart Data
Prinzie Anita

3. Anita Prinzie, Solutions-2, Belgium
Festival of NewMR 2012 - Main Stage - Session 3
125,000100,00050,000
OR
150,000

4. Anita Prinzie, Solutions-2, Belgium
Festival of NewMR 2012 - Main Stage - Session 3
48 hours100,000650,000 28,000
OR

5. Anita Prinzie, Solutions-2, Belgium
Festival of NewMR 2012 - Main Stage - Session 3
http://datasift.com/stream/13991/
mcdonalds#app1-preview

6. Anita Prinzie, Solutions-2, Belgium
Festival of NewMR 2012 - Main Stage - Session 3
ORGANIC DATA DESIGNED DATA
Based on Bob Groves, former US Census director
CREATED TO GAIN SPECIFIC INSIGHTS
DATA RICHNESS

7. Anita Prinzie, Solutions-2, Belgium
Festival of NewMR 2012 - Main Stage - Session 3
BIG DATA SMART DATA?

8. Anita Prinzie, Solutions-2, Belgium
Festival of NewMR 2012 - Main Stage - Session 3
Define business
objective

9. Anita Prinzie, Solutions-2, Belgium
Festival of NewMR 2012 - Main Stage - Session 3
Brand perceptions Detecting crisis
Complaints & malfunctions
New product launchConsumer segments
Media campaign success
Based on Jasper Snyder Converseon
MONITORINGLISTENING
Brand positioning
...
...

10. Anita Prinzie, Solutions-2, Belgium
Festival of NewMR 2012 - Main Stage - Session 3
Identify relevant data

11. Anita Prinzie, Solutions-2, Belgium
Festival of NewMR 2012 - Main Stage - Session 3
Who?
Which conversations?
How long?
LISTENING PURPOSE
RELEVANT DATA
MONITORING PURPOSE
BUSINESS
OBJECTIVE
Which platforms?

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

13. Anita Prinzie, Solutions-2, Belgium
Festival of NewMR 2012 - Main Stage - Session 3
Profile on channel usage & engagement
15-24 25-34 35-54 55-99
Actively engaging on
FB, Twitter, blogs?
Who & Which platforms

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

15. Anita Prinzie, Solutions-2, Belgium
Festival of NewMR 2012 - Main Stage - Session 3
How long?
Find relevant time window
Natural time window
‘Enough conversations’ time
window
New product launch: 90 days
before and after (Microsoft)

16. Anita Prinzie, Solutions-2, Belgium
Festival of NewMR 2012 - Main Stage - Session 3
Clean &
Preprocess

17. Anita Prinzie, Solutions-2, Belgium
Festival of NewMR 2012 - Main Stage - Session 3
Clean and Preprocess
Keep emoticons and markup
for detecting crisis
Context specific normalization
and annotation (e.g. Convey
API)
Keep goal in mind

18. Anita Prinzie, Solutions-2, Belgium
Festival of NewMR 2012 - Main Stage - Session 3
Analyse with
objective
in mind

19. Anita Prinzie, Solutions-2, Belgium
Festival of NewMR 2012 - Main Stage - Session 3
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 ANALYSISMONITORING PURPOSE

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

21. Anita Prinzie, Solutions-2, Belgium
Festival of NewMR 2012 - Main Stage - Session 3
Validate
results

22. Anita Prinzie, Solutions-2, Belgium
Festival of NewMR 2012 - Main Stage - Session 3
Validate social media results
The evolution of the Microsoft
software adoption index did
follow known success/failure
trends for past software
launches.
Denali
O365

23. Anita Prinzie, Solutions-2, Belgium
Festival of NewMR 2012 - Main Stage - Session 3
SMART DATA
Define business objective
Identify relevant data
Clean & preprocess
Analyse with objective in mind
Validate results
BIG DATA

24. Anita Prinzie, Solutions-2, Belgium
Festival of NewMR 2012 - Main Stage - Session 3
Q & A
Ray Poynter
VCU
Anita Prinzie
Solutions-2

25. Anita Prinzie, Solutions-2, Belgium
Festival of NewMR 2012 - Main Stage - Session 3
risingquestions
If you have any
Anita Prinzie
anita@solutions2.be
@AnitaPrinzie

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