This talk focuses on business applications of Big Social Data Analytics, where it looks at paradigms of generating competitive advantages and business value. Ravi looks into transforming big data sets into a valuable arsenal of knowledge. He will also touch on Real Promoter Scores, predictive analytics of sales & revenues and shitstorms, and a look into Big Social Data Analytics for Public health.

1. Ravi Vatrapu
Director, Centre for Business Data Analytics (bda.cbs.dk)
Professor, Department of IT Management
Copenhagen Business School, Denmark
Email: vatrapu@cbs.dk
Web: http://www.cbs.dk/en/staff/rvitm
Centre: http://bda.cbs.dk
Transforming Big Data Sets into Business Assets

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• Phenomena
• Internet, Social Media & Society
• Challenges & Opportunties: In-House Data + Big Data
• Business Value: Big Data Sets à Business Assets
• Centre for Business Data Analytics (bda.cbs.dk)
• Meaningful Facts
• Actionable Insights
• Valuable Outcomes
• Sustainable Impacts
• Case Projects
• Predictive Models
• Prescriptive Analytics
• Visual Analytics
• Our Product and Service Portfolio
Outline

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About Me: Global Nomad
Vizag, India Blacksburg, USA
Honolulu, USA
Copenhagen, Denmark

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Part I:Phenomena

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Internet, Social Media & Society
https://en.wikipedia.org/wiki/On_the_Internet,_nobody_knows_you're_a_dog
Ramu: “On the Facebook, everybody knows I am a dog"

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Challenges: How to Combine House Data with Big Data

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Opportunities: Business Value = In-House Data + Big Data
Porta, M., House, B., Buckley, L. & Blitz, A. (2008)

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Business Value = In-House Data + Big Data
Wollan, R., Smith, N. & Zhou, C (2011)
Sony PS4 Controller: “Share” Button
Image from Kotaku

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Big Data Sets à Business Assets
Case: Product: Baby-Monitors
Master Thesis: Adele Indiane Gurrich Kristensen & Stine Sofie Bragdø

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Part II: CSSL Approach
Set-Theoretical Big Social Data Analytics

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Centre for Business Data Analytics (cbsBDA) located at the Dept. of IT
Management, Copenhagen Business School.
cbsBDA conducts transdisciplinary basic research on socio-technical
interactions with specific applications to managers in companies, teachers in
schools and residents in cities.
1 Director & Professor
2 Assistant Professors
10 PhD Students
4 Research Associates
+ 11 Faculty Collaborators at CBS,
KU and beyond
(IEEE EDOC 2014)
cbsBDA (bda.cbs.dk)

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cbsBDA’s Naïve Model for Applied Research
• Symptoms
• Diagnosis
• Therapy
• Prescription
• Proscription
• Prognosis
• Positive/Negative
MARCELLUS:
Something is rotten in the state of Denmark
http://shakespeare.mit.edu/hamlet/full.html
https://en.wikipedia.org/wiki/File:Helsing%C3%B8r_Elsinore_from_sea_01.jpg

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Class of Problems: Social Associations (Organisations)

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Social Data
Interactions Conversations
Actors ArtifactsActivitiesActions Topics EmotionsPronounsKeywords
Source: Ravi Vatrapu
Conceptual Model of Social Data

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Analytical Framework for Set-Theoretical CSS

16. McDonalds DK Actors: 266,000
Noma Actors: 4,567
McDonalds DK & Noma Actors: 203
CROSS-WALL ANALYSIS: MCDONALDS DK VS. NOMA
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• What kinds of social text do these 203 actors create, circulate, and interact with?
• What, if any, is the cross-cultural variation of actors associating with both fast food and fine dining?

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Part III: Case Projects

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Case Project #1: Loyalty Club Programs

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• Datasets
• CRM
• Interviews
• Facebook
Case Project #1: Loyalty Club Programs

20. © Temperaturen på danske loyalitetsklubber anno 2015 -II 20
Big Social Data Analytics

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For a given social media action, we want to analyse and model:
• User Characteristics
• Emotion
• Personality
• User/Consumer Characteristics
• Consumer Decision-Making Stage
• Organisational Consequences
• Brand Sentiment
• Social Media Consequences
• Social Engagement Potential
Beyond Social Media-->Towards Social Business

22. Beyond Social Media-->Towards Social Business
“Heres an idea. If you like their food eat there. If you dont like their food eat somewhere else or make your own meal.
I really dont understand what the big deal is.”
User Consumer
Organisation
Social Influence

23. Text Classification: Multi-Dimensional Models

24. Basic Emotions
0.00% 20.00% 40.00% 60.00% 80.00% 100.00%
BR
matas
coop
Forbrugsforeningen
IKEA
imerco
lOplus
Sportsmaster
Basic Emotions: Proportion
Joy % Sadness % Surprise % Fear % Disgust % Anger %
0 0.2 0.4 0.6 0.8 1
BR
matas
coop
Forbrugsforeningen
IKEA
imerco
lOplus
Sportsmaster
Basic Emotions: Intensity
Joy Intensity Sadness Intensity Surprise Intensity
Fear Intensity Disgust Intensity Anger Intensity

25. Brand Parameters:
Historical Development: User Emotions vs. Brand Sentiment:

26. Predicting Net Promotor Score From Big Social Data
R² = 0.95813
0
10
20
30
40
50
60
0.00 0.10 0.20 0.30 0.40 0.50 0.60 0.70
NPS Poly. (NPS)

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Case Project #2: Market & User Segmentation

28. CROSS-WALL ANALYSIS: USER/CUSTOMER SEGMENTATION
DK 2011 US 2008

29. ENGAGEMENT DIMENSIONS & USER SEGMENTS
SOCIAL NETWORK DIALOG SPACE
Robertson, S., Vatrapu, R., & Medina, R. (2010). Off the Wall Political Discourse: Facebook Use in the 2008 U.S. Presidential Election.
Information Polity, 15(1-2), 11-31.

30. BUSINESS VALUE: REAL PROMOTER SCORE
Product Advocates are
champions for products in
general.
Product Enthusiasts are
the users that aspire for
the product category.
Brand Loyalists are
champions of a particular
brand.
Brand Tourists are in the
early stages of brand
consideration.

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Case Project #3: Sales & Revenue Forecasters

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Business Value: Sales and Revenue Predictive Models
(IEEE EDOC 2014) (ICCSS 2015)
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H&M sales, billion SEK per Quarter
Sales Predicted Sales
Company Data Source Time Period Size of Dataset
Apple Twitter 2007 ® October 12,
2014
500 million+ tweets
containing “iPhone”
H & M Facebook January 01, 2009 ®
October 12, 2014
~15 million Facebook
events

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Case Project #4: Social Media Crises (“Shitstorms”)

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CSR Crises: Bangladesh Factory Accidents & Volkswagen
IEEE EDOC 2015 IEEE Big Data 2015

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Business Impact: Social Media Crisis
(ACM CABS 2014) (IEEE EDOC 2015) (IEEE Big Data 2015)
During Crisis :05-19 February, 2014
Artefacts: All Data: Wall beginning to last collected time
Actors: All Facebook users on Copenhagen Zoo Page
Actions: LIKE
Activity: Positive Association
Sociological Importance
Organizational Relevance
Interpretation: Computational Social Science: Set Theory
LIKEs were a way of expressing cultural solidarity and in-
group support to a Danish institution perceived to be under
undeserved out-group criticism
Likes on Zoo’s Posts & CommentsUnique Actors on Zoo’s FB Wall

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Case Project #5: EU Immigration Crisis

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EU Immigration Crisis
BDA Course Project: Jensen, Brock, Hody, Christensen & Al Humaidan

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• Big Social Data
• Complete Corpus for Facebook
• Multi-Channel, Multi-Language & Multi-Domain
• Analytics Software
• Social Data Analytic Tool (SODATO)
• Social Set Visualiser (SOSEVI)
• Multi-Dimensional Text Analytics (MUDITA)
• Social Business Predictor (SB-PRE)
• Social Business Integrator (SB-INT)
• Research & Consultancy Reports
• Analytics Time Horizons
• Fixed
• Incremental
• Continuous
• Analytics Mode
• Historic
• Near Real-Time
• Real-Time
• Projects
• Research
• Research Projects
• Industrial PhD Projects
• Consultancy
• Real Promoter Score
• Strategic Management
Our Product Portfolio

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Interested?
Contact us!