Transforming Big Data Sets into Business Assets by Prof. Ravi Vatapu, CBS

Ravi VatrapuDirector, Centre for Business Data Analytics (bda.cbs.dk)

Professor, Department of IT ManagementCopenhagen Business School, Denmark

Email: vatrapu@cbs.dkWeb: http://www.cbs.dk/en/staff/rvitm

Centre: http://bda.cbs.dk

Transforming Big Data Sets into Business Assets

2

• 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

3

About Me: Global Nomad

Vizag,India Blacksburg,USA

Honolulu,USACopenhagen,Denmark

4

Part I:Phenomena

5

Internet, Social Media & Society

https://en.wikipedia.org/wiki/On_the_Internet,_nobody_knows_you're_a_dogRamu:“OntheFacebook,everybodyknowsIamadog"

6

Challenges: How to Combine House Data with Big Data

7

Opportunities: Business Value = In-House Data + Big Data

Porta,M.,House,B.,Buckley,L.&Blitz,A.(2008)

8

Business Value = In-House Data + Big Data

Wollan,R.,Smith,N.&Zhou,C(2011)SonyPS4Controller:“Share”Button

ImagefromKotaku

9

Big Data Sets à Business AssetsCase: Product: Baby-Monitors

MasterThesis:AdeleIndianeGurrich Kristensen&StineSofieBragdø

10

Part II: CSSL ApproachSet-Theoretical Big Social Data Analytics

11

CentreforBusinessDataAnalytics(cbsBDA)locatedattheDept.ofITManagement,CopenhagenBusinessSchool.cbsBDA conductstransdisciplinarybasicresearchonsocio-technicalinteractions withspecificapplicationstomanagersincompanies,teachersinschoolsandresidentsincities.

1Director&Professor2AssistantProfessors10PhDStudents4ResearchAssociates+11FacultyCollaboratorsatCBS,KUandbeyond

(IEEEEDOC2014)

cbsBDA (bda.cbs.dk)

12

cbsBDA’s Naïve Model for Applied Research

• Symptoms

• Diagnosis

• Therapy• Prescription• Proscription

• Prognosis• Positive/Negative

MARCELLUS:

SomethingisrotteninthestateofDenmark

http://shakespeare.mit.edu/hamlet/full.html

https://en.wikipedia.org/wiki/File:Helsing%C3%B8r_Elsinore_from_sea_01.jpg

13

Class of Problems: Social Associations (Organisations)

14

SocialData

Interactions Conversations

Actors ArtifactsActivitiesActions Topics EmotionsPronounsKeywords

Source:RaviVatrapu

Conceptual Model of Social Data

15

Analytical Framework for Set-Theoretical CSS

McDonaldsDKActors:266,000Noma Actors:4,567McDonaldsDK&Noma Actors:203

CROSS-WALL ANALYSIS:MCDONALDS DKVS.NOMA

9

• Whatkindsofsocialtextdothese203 actorscreate,circulate,andinteractwith?

• What,ifany,isthecross-culturalvariationofactorsassociatingwithbothfastfoodandfinedining?

17

Part III: Case Projects

18

Case Project #1: Loyalty Club Programs

19

• Datasets• CRM

• Interviews

• Facebook

Case Project #1: Loyalty Club Programs

© Temperaturenpådanskeloyalitetsklubberanno2015-II 20

BigSocialDataAnalytics

21

Foragivensocialmediaaction,wewanttoanalyse andmodel:

• UserCharacteristics• Emotion• Personality

• User/ConsumerCharacteristics• ConsumerDecision-MakingStage

• Organisational Consequences• BrandSentiment

• SocialMediaConsequences• SocialEngagementPotential

Beyond Social Media-->Towards Social Business

BeyondSocialMedia-->TowardsSocialBusiness“Heres anidea.Ifyouliketheirfoodeatthere.Ifyoudont liketheirfoodeatsomewhereelseormakeyourownmeal.

Ireallydont understandwhatthebigdealis.”

User Consumer

Organisation

SocialInfluence

TextClassification:Multi-DimensionalModels

BasicEmotions

0.00% 20.00% 40.00% 60.00% 80.00% 100.00%

BRmatascoop

ForbrugsforeningenIKEA

imercolOplus

Sportsmaster

BasicEmotions:Proportion

Joy% Sadness% Surprise% Fear% Disgust% Anger%

0 0.2 0.4 0.6 0.8 1

BRmatascoop

ForbrugsforeningenIKEA

imercolOplus

Sportsmaster

BasicEmotions:Intensity

JoyIntensity SadnessIntensity SurpriseIntensity

FearIntensity DisgustIntensity AngerIntensity

BrandParameters:HistoricalDevelopment:UserEmotionsvs.BrandSentiment:

PredictingNetPromotorScoreFromBigSocialData

R²=0.95813

0

10

20

30

40

50

60

0.00 0.10 0.20 0.30 0.40 0.50 0.60 0.70NPS Poly.(NPS)

27

Case Project #2: Market & User Segmentation

CROSS-WALL ANALYSIS:USER/CUSTOMER SEGMENTATION

28

DK2011 US2008

ENGAGEMENT DIMENSIONS &USER SEGMENTS

29

SOCIAL NETWORK DIALOG SPACE

Robertson,S.,Vatrapu,R.,&Medina,R.(2010).OfftheWallPoliticalDiscourse:FacebookUseinthe2008U.S.Presidential Election.InformationPolity,15(1-2),11-31.

BUSINESS VALUE:REAL PROMOTER SCOREProduct 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.

31

Case Project #3: Sales & Revenue Forecasters

32

Business Value: Sales and Revenue Predictive Models

(IEEEEDOC2014)(ICCSS2015)

28

30

32

34

36

38

40

42

44

46

Q1'10

Q2'10

Q3'10

Q4'10

Q1'11

Q2'11

Q3'11

Q4'11

Q1'12

Q2'12

Q3'12

Q4'12

Q1'13

Q2'13

Q3'13

Q4'13

Q1'14

Q2'14

Q3'14

Q4'14

Q1'15

H&Msales,billionSEKperQuarter

Sales PredictedSales

Company DataSource TimePeriod SizeofDatasetApple Twitter 2007® October12,

2014500million+tweetscontaining“iPhone”

H&M Facebook January01,2009®October12,2014

~15millionFacebookevents

33

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

34

CSR Crises: Bangladesh Factory Accidents & Volkswagen

IEEEEDOC2015 IEEEBigData2015

35

Business Impact: Social Media Crisis

(ACMCABS2014) (IEEEEDOC2015)(IEEEBigData2015)

DuringCrisis:05-19February,2014Artefacts:AllData:WallbeginningtolastcollectedtimeActors:AllFacebookusersonCopenhagenZooPageActions:LIKE

Activity:PositiveAssociationSociologicalImportanceOrganizationalRelevance

Interpretation:ComputationalSocialScience:SetTheoryLIKEswereawayofexpressingculturalsolidarityandin-groupsupporttoaDanishinstitutionperceivedtobeunderundeservedout-groupcriticism

LikesonZoo’sPosts&CommentsUniqueActorsonZoo’sFBWall

36

Case Project #5: EU Immigration Crisis

37

EU Immigration Crisis

BDACourseProject:Jensen,Brock,Hody,Christensen&AlHumaidan

38

• 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

39

Interested?Contact us!