Finans IT 2016

BIG DATARavi Vatrapu, Professor, Department of Information

Technology Management, Copenhagen Business School

Computational Social Science Laboratory (http://cssl.cbs.dk)

Ravi Vatrapu1,2

1Computational Social Science Laboratory, Dept. of ITM, Copenhagen Business School, Denmark

2Westerdals Olso School of Arts, Communication and Technology, Norway

vatrapu@cbs.dk

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• Overview of the Computational Social Science Laboratory (cssl.cbs.dk)

• Business Value (Big Social Data)

• Our CSSL Approach

• Example Projects on Business Value from Big Social Data

• Our Product and Service Portfolio

Outline

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Computational Social Science Laboratory (CSSL)is located at the Dept. of IT Management, Copenhagen Business School.

CSSL conducts transdisciplinary basic research on socio-technical interactions with specific applications to managers in companies, teachers in schools and residents in cities.

1 Professor2 Assistant Professor10 PhD Students3 Research Associates+11 CBS Faculty Collaborators

(IEEE EDOC 2014)

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Part I: Business Value (Big Social Data)

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Business Value = In-House Data + Big Data

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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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Business Value = In-House Data + Big Data

Product: Baby-Monitors

Confidential

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Part II: Our Approach

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CSSL’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 SentimentsPronounsKeywords

Source: Ravi Vatrapu

Conceptual Model of Social Data

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Analytical Framework for Set-Theoretical CSS

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Part III: Examples

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• Datasets

• CRM

• Interviews

• Facebook

Example Project: Loyalty Club Programs

© Temperaturen på danske loyalitetsklubber anno 2015 -II 17

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

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

Text Classification: Multi-Dimensional Models

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

Big Five Personality Traits

0 0,2 0,4 0,6 0,8 1

BR

matas

coop

Forbrugsf…

IKEA

imerco

lOplus

Sportsmas…

Big Five Personality Traits: Intensity

Openness Intensity Conscientiousness Intensity Extraversion Intensity

Agreeableness Intensity Neuroticism Intensity

0,00% 10,00%20,00%30,00%40,00%50,00%60,00%70,00%80,00%90,00%100,00%

BR

matas

coop

Forbrugsforeningen

IKEA

imerco

lOplus

Sportsmaster

Big Five Personality Traits: Proportion

Openness % Conscientiousness % Extraversion %

Agreeableness % Neuroticism %

Consumer Decision-Making Stage

0 0,2 0,4 0,6 0,8 1

BR

matas

coop

Forbrugsforeningen

IKEA

imerco

lOplus

Sportsmaster

Consumer Decison-Making Stage: Intensity

Awareness Intensity Knowledge Intensity Liking Intensity

Preference Intensity Conviction Intensity Purchase Intensity

0,00% 20,00% 40,00% 60,00% 80,00% 100,00%

BR

matas

coop

Forbrugsforeningen

IKEA

imerco

lOplus

Sportsmaster

Consumer Decison-Making Stage: Proportion

Awareness % Knowledge % Liking %

Preference % Conviction % Purchase %

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

Predicting Net Promotor Score From Big Social Data

R² = 0,9581

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60

0,00 0,10 0,20 0,30 0,40 0,50 0,60 0,70

NPS Poly. (NPS)

McDonalds DK Actors: 266,000Noma 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?

CROSS-WALL ANALYSIS: USER/CUSTOMER SEGMENTATION

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DK 2011 US 2008

ENGAGEMENT DIMENSIONS & USER SEGMENTS

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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.

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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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Business Impact: Social Media Crisis

(ACM CABS 2014) (IEEE EDOC 2015) (IEEE Big Data 2015)

During Crisis :05-19 February, 2014Artefacts: All Data: Wall beginning to last collected timeActors: All Facebook users on Copenhagen Zoo PageActions: LIKE

Activity: Positive AssociationSociological ImportanceOrganizational Relevance

Interpretation: Computational Social Science: Set TheoryLIKEs 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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Business Impact: CSR Crises

IEEE EDOC 2015 IEEE Big Data 2015

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• Big Social Data

• Complete Corpus for Facebook

• Multi-Channel and Multi-Tool

• Analytics Software

• Social Data Analytic Tool (SODATO)

• Social Set Visualiser (SoSeVi)

• Social Business Investigator (SB-INT)

• Social Business Predictor (SB-PRE)

• Research Consultancy Reports

• Management Research Analysis

• Project Time Horizons

• Fixed

• Incremental

• Continuous

• Analytics Mode

• Historic

• Near Real-Time

• Real-Time

Our Product Portfolio

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Questions

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TOOLS & R-Scripts

Social Data Analytics Tool (SODATO)http://sodato.net/Software/SODATOV3ins5/Web/

Username/Password: CSSWS-Cologne

Social Set Visualiser (SoSeVi)(Safari or Chrome recommended)

http://144.76.62.168:2999/Username/Password: bigdata

R-Scripts for Temporal & Social Set Diagramshttp://tinyurl.com/ssa-cologne

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Social Data Analytics Tool

(AnalyzingSocialNetworks, 2014) (IEEE EDOC 2014)(DESRIST 2014)

http://sodato.net/Software/SODATOV3ins5/Web/Username/Password: CSSWS-Cologne

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R-Scripts for Temporal Dynamics and Venn Diagrams

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Social Set Visualizer

IEEE EDOC 2015 IEEE Big Data 2015