SlideShare una empresa de Scribd logo
1 de 15
Prediktiv analys och
    kundlojalitet
            Robert Moberg
Predictive Analytics Solutions Architect
Let me start with a few of qoutes...
         § ”In the factory we manufacture lipstick, in
           the store we sell hope”
            – Charles Revson
            – 1906 – 1975
            – Creater of Revlon




         § ”People don't want to buy a quarter-inch
           drill, they want a quarter-inch hole.”
            – Theodore Levitt
            – 1925 – 2006
            – Harvard Professor




         § ”We don’t sell an analytics platform – we
           provide happy, loyal and prospering
           customers”
            – Robert Moberg
            – 1969 –
            – PASA at IBM
It is not the
                 strongest of the
                 species that
                 survive, nor the
                 most intelligent,
                 but the one most
                 responsive to
Charles Darwin   change.
A Sample of   A Universe of Things That Generate
Data          Data




   ?
A Universe of   A Predictive
Data            Model
                  Attributes
                  •Married, 2 kids
                  •Home owner in Liseberg,
                  Älvsjö
                  •Has a house in Gotland
                  •Owns a car
                  •41 years old
                  •Enjoys fine wines and
                  champagne
                  •Plays golf

                  Predicted Attributes
                  •Likes Beastie boys
                  •Likes Gotland
                  •Works long hours
                  •Commutes
                  •Middle Income

                  Predicted Behavior
                  •Dines in descent restaurants
                  •Consumes a lot of electricity
                  •Buys green fees
                  •Family vacations
An Overwhelming Amount of Data to Process


                                            High-value, dynamic
              Social Media         - source of competitive differentiation          Open-Ended
               (networks)                                                             Surveys
                             Interaction data                     Attitudinal
                             - E-Mail / chat                      data
                             transcripts                          - Opinions
                             - Call center notes                  - Preferences
                             - Web Click-streams     360 degree   - Needs &
                             - In person dialogues                Desires
                                                  Customer View


                             Descriptive data                     Behavioral
                             - Attributes                         data
                             - Characteristics                    - Orders
                             - Self-declared info                 - Transactions
                    CRM      -                                    - Payment         Operational
                   Systems   (Geo)demographic                     history            Systems
                             s                    “Traditional    - Usage history
                                                       ”
IBM confidential
Evolutionary Solutions for Customer Intimacy



Differentiating   Breakaway      Foundational   Competitive

   Insight for
   Decision Makers




                                         The Next Best Action
Define the Strategy                                             Run the Business

       Year       Month         Week           Day         Hour           No
       s          s             s              s           s              w

                             Time to Business Impact




Improve senior management       Improve policy makers’           Help individual
       visibility with              decisions with            contributors take the
Key Performance Predictors    Forecasts and Optimization        Next Best Action
    Strategic                        Tactica                       Operational
                                        l
Predictive
              Customer
              Analytics



Predictive                   Predictive
Operational                 Threat and
 Analytics                 Risk Analytics
Customer Analytics

    One to One
                                   Research &
                                    Purchase
                             Purcha Product
                     Advoc
                               se
                      ate
                              More
                     Produ
                       ct


                         Get      Use
                      Customer    Prod
  The Broad Brush
                       Service     uct
Operational Analytics
          Agile
                                        Develop
                                         ment
                         Procure
                          ment




                                        Availab
                           Distributi
                                         ility
                              on
    Long term planning
Risk Analytics
       Proactive                     Defin
                                      e




                             Allow

                                              Monito
                    Preven                      r
                       t




       Corrective                     Detec
                                        t
Darwinism according to me:             It is not the
vData is key                           strongest of the
vUnderstanding data is one thing knowing what to do with it
is another                             species that
vIt’s easier to give people what they want if you know the
                                       survive, nor what
that is                                most intelligent,
vCustomer analytics, a prerequisite to but the one your
                                       be relevant to most
customers
                                       responsive to
vCustomer analytics and insights provide decision support
   Charles Darwin                      change.
vDecisions will be reliable, because they are based on facts
not on speculation
  Predictive Customer Analytics will make your
        company responsive to change!!!
TACK

Más contenido relacionado

La actualidad más candente

CIS Life Sciences Brochure
CIS Life Sciences BrochureCIS Life Sciences Brochure
CIS Life Sciences Brochure
jamieadp
 
Call Center Digital Signage
Call Center Digital SignageCall Center Digital Signage
Call Center Digital Signage
David Nelson
 
Module 3 Adapative Customer Experience Final
Module 3 Adapative Customer Experience FinalModule 3 Adapative Customer Experience Final
Module 3 Adapative Customer Experience Final
Vivastream
 
Annik research analytics deck pvd
Annik research analytics deck   pvdAnnik research analytics deck   pvd
Annik research analytics deck pvd
Atul Sharma
 
Data Center Decisions: Build Versus Buy
Data Center Decisions: Build Versus BuyData Center Decisions: Build Versus Buy
Data Center Decisions: Build Versus Buy
VISIHOSTING
 

La actualidad más candente (17)

Make Money with Big Data (TCELab)
Make Money with Big Data (TCELab)Make Money with Big Data (TCELab)
Make Money with Big Data (TCELab)
 
CIS Life Sciences Brochure
CIS Life Sciences BrochureCIS Life Sciences Brochure
CIS Life Sciences Brochure
 
NICE Fizzback - Voice of the Customer
NICE Fizzback - Voice of the CustomerNICE Fizzback - Voice of the Customer
NICE Fizzback - Voice of the Customer
 
Actian Partner Brochure
Actian Partner BrochureActian Partner Brochure
Actian Partner Brochure
 
Call Center Digital Signage
Call Center Digital SignageCall Center Digital Signage
Call Center Digital Signage
 
Module 3 Adapative Customer Experience Final
Module 3 Adapative Customer Experience FinalModule 3 Adapative Customer Experience Final
Module 3 Adapative Customer Experience Final
 
blueocean market intelligence corporate brochure
blueocean market intelligence corporate brochureblueocean market intelligence corporate brochure
blueocean market intelligence corporate brochure
 
Annik research analytics deck pvd
Annik research analytics deck   pvdAnnik research analytics deck   pvd
Annik research analytics deck pvd
 
VMTeknikleri
VMTeknikleriVMTeknikleri
VMTeknikleri
 
Business & Decision MDM Summit (english version)
Business & Decision MDM Summit (english version)Business & Decision MDM Summit (english version)
Business & Decision MDM Summit (english version)
 
Intro to Nielsen Consulting
Intro to Nielsen ConsultingIntro to Nielsen Consulting
Intro to Nielsen Consulting
 
Tech Talk SQL Server 2012 Business Intelligence
Tech Talk SQL Server 2012 Business IntelligenceTech Talk SQL Server 2012 Business Intelligence
Tech Talk SQL Server 2012 Business Intelligence
 
Managing Your It Budget In Tough Times Traverse City
Managing Your It Budget In Tough Times   Traverse CityManaging Your It Budget In Tough Times   Traverse City
Managing Your It Budget In Tough Times Traverse City
 
The Network of Truth
The Network of TruthThe Network of Truth
The Network of Truth
 
Data Center Decisions: Build Versus Buy
Data Center Decisions: Build Versus BuyData Center Decisions: Build Versus Buy
Data Center Decisions: Build Versus Buy
 
Constellation's Sneak Peak Into Social Business Trends
Constellation's Sneak Peak Into Social Business TrendsConstellation's Sneak Peak Into Social Business Trends
Constellation's Sneak Peak Into Social Business Trends
 
Customer Intelligence Appliance
Customer Intelligence ApplianceCustomer Intelligence Appliance
Customer Intelligence Appliance
 

Destacado (7)

Totally random legacy 1.0
Totally random legacy 1.0Totally random legacy 1.0
Totally random legacy 1.0
 
Invest in sri lanka
Invest in sri lankaInvest in sri lanka
Invest in sri lanka
 
IBM Cognos - Vad handlar egentligen prediktiv analys om?
IBM Cognos - Vad handlar egentligen prediktiv analys om?IBM Cognos - Vad handlar egentligen prediktiv analys om?
IBM Cognos - Vad handlar egentligen prediktiv analys om?
 
DNA April 17, 2011
DNA April 17, 2011DNA April 17, 2011
DNA April 17, 2011
 
Ethans Sheet 1
Ethans Sheet 1Ethans Sheet 1
Ethans Sheet 1
 
Propuesta de energía del Partido Progresista
Propuesta de energía del Partido ProgresistaPropuesta de energía del Partido Progresista
Propuesta de energía del Partido Progresista
 
Isaac Zou's Portfolio
Isaac Zou's PortfolioIsaac Zou's Portfolio
Isaac Zou's Portfolio
 

Similar a Prediktiv analys och kundlojalitet

VC Do's and Don'ts - Jurgen Ingels
VC Do's and Don'ts  - Jurgen Ingels VC Do's and Don'ts  - Jurgen Ingels
VC Do's and Don'ts - Jurgen Ingels
Frank Gielen
 
Customer Insights Summit Toronto 2012
Customer Insights Summit Toronto 2012Customer Insights Summit Toronto 2012
Customer Insights Summit Toronto 2012
Fabiana Pereira
 
Big dataforcf os1_23_12_final
Big dataforcf os1_23_12_finalBig dataforcf os1_23_12_final
Big dataforcf os1_23_12_final
BurrPilgerMayer
 
Right Space Brief
Right Space BriefRight Space Brief
Right Space Brief
jnassour
 
Analyzing Multi-Structured Data
Analyzing Multi-Structured DataAnalyzing Multi-Structured Data
Analyzing Multi-Structured Data
DataWorks Summit
 
Manthan biim services and solutions
Manthan   biim services  and solutionsManthan   biim services  and solutions
Manthan biim services and solutions
Jaikumar Karuppannan
 
Think Big Analytics AWS for Financial Services
Think Big Analytics AWS for Financial ServicesThink Big Analytics AWS for Financial Services
Think Big Analytics AWS for Financial Services
Amazon Web Services
 
Smart analytics - Data Visualisation and Predictive Analytics solutions for F...
Smart analytics - Data Visualisation and Predictive Analytics solutions for F...Smart analytics - Data Visualisation and Predictive Analytics solutions for F...
Smart analytics - Data Visualisation and Predictive Analytics solutions for F...
Srini Bezwada
 
Analytics for fundraisers
Analytics for fundraisersAnalytics for fundraisers
Analytics for fundraisers
Srini Bezwada
 

Similar a Prediktiv analys och kundlojalitet (20)

Big Data, Hadoop, Hortonworks and Microsoft HDInsight
Big Data, Hadoop, Hortonworks and Microsoft HDInsightBig Data, Hadoop, Hortonworks and Microsoft HDInsight
Big Data, Hadoop, Hortonworks and Microsoft HDInsight
 
VC Do's and Don'ts - Jurgen Ingels
VC Do's and Don'ts  - Jurgen Ingels VC Do's and Don'ts  - Jurgen Ingels
VC Do's and Don'ts - Jurgen Ingels
 
Customer Insights Summit Toronto 2012
Customer Insights Summit Toronto 2012Customer Insights Summit Toronto 2012
Customer Insights Summit Toronto 2012
 
The Why
The WhyThe Why
The Why
 
Predictive analytics km chicago
Predictive analytics km chicagoPredictive analytics km chicago
Predictive analytics km chicago
 
Big dataforcf os1_23_12_final
Big dataforcf os1_23_12_finalBig dataforcf os1_23_12_final
Big dataforcf os1_23_12_final
 
How to become an Analytics-driven organization - and why bother? - IBM Smarte...
How to become an Analytics-driven organization - and why bother? - IBM Smarte...How to become an Analytics-driven organization - and why bother? - IBM Smarte...
How to become an Analytics-driven organization - and why bother? - IBM Smarte...
 
Right Space Brief
Right Space BriefRight Space Brief
Right Space Brief
 
Analyzing Multi-Structured Data
Analyzing Multi-Structured DataAnalyzing Multi-Structured Data
Analyzing Multi-Structured Data
 
ExactTarget & Crown Audience Builder
ExactTarget & Crown Audience BuilderExactTarget & Crown Audience Builder
ExactTarget & Crown Audience Builder
 
Evoke final 2013 berkeley
Evoke final 2013 berkeleyEvoke final 2013 berkeley
Evoke final 2013 berkeley
 
How to make data actionable for business
How to make data actionable for businessHow to make data actionable for business
How to make data actionable for business
 
Big Data Marketing Analytics
Big Data Marketing AnalyticsBig Data Marketing Analytics
Big Data Marketing Analytics
 
EDF2013: Selected Talk: Bryan Drexler: The 80/20 Rule and Big Data
EDF2013: Selected Talk: Bryan Drexler: The 80/20 Rule and Big Data EDF2013: Selected Talk: Bryan Drexler: The 80/20 Rule and Big Data
EDF2013: Selected Talk: Bryan Drexler: The 80/20 Rule and Big Data
 
Manthan biim services and solutions
Manthan   biim services  and solutionsManthan   biim services  and solutions
Manthan biim services and solutions
 
Think Big Analytics AWS for Financial Services
Think Big Analytics AWS for Financial ServicesThink Big Analytics AWS for Financial Services
Think Big Analytics AWS for Financial Services
 
Smart analytics - Data Visualisation and Predictive Analytics solutions for F...
Smart analytics - Data Visualisation and Predictive Analytics solutions for F...Smart analytics - Data Visualisation and Predictive Analytics solutions for F...
Smart analytics - Data Visualisation and Predictive Analytics solutions for F...
 
Communities for Innovation
Communities for Innovation Communities for Innovation
Communities for Innovation
 
Analytics for fundraisers
Analytics for fundraisersAnalytics for fundraisers
Analytics for fundraisers
 
Data Activation For (Not So Much) Dummies
Data Activation For (Not So Much) DummiesData Activation For (Not So Much) Dummies
Data Activation For (Not So Much) Dummies
 

Más de IBM Sverige

Más de IBM Sverige (20)

Trender, inspirationer och visioner - Mikael Haglund #ibmbpsse18
Trender, inspirationer och visioner - Mikael Haglund #ibmbpsse18Trender, inspirationer och visioner - Mikael Haglund #ibmbpsse18
Trender, inspirationer och visioner - Mikael Haglund #ibmbpsse18
 
AI – hur långt har vi kommit? – Oskar Malmström, IBM #ibmbpsse18
AI – hur långt har vi kommit? – Oskar Malmström, IBM #ibmbpsse18AI – hur långt har vi kommit? – Oskar Malmström, IBM #ibmbpsse18
AI – hur långt har vi kommit? – Oskar Malmström, IBM #ibmbpsse18
 
#ibmbpsse18 - The journey to AI - Mikko Hörkkö, Elinar

#ibmbpsse18 - The journey to AI - Mikko Hörkkö, Elinar
#ibmbpsse18 - The journey to AI - Mikko Hörkkö, Elinar

#ibmbpsse18 - The journey to AI - Mikko Hörkkö, Elinar

 
#ibmbpsse18 - Koppla säkert & redundant till IBM Cloud - Magnus Huss, Interexion
#ibmbpsse18 - Koppla säkert & redundant till IBM Cloud - Magnus Huss, Interexion#ibmbpsse18 - Koppla säkert & redundant till IBM Cloud - Magnus Huss, Interexion
#ibmbpsse18 - Koppla säkert & redundant till IBM Cloud - Magnus Huss, Interexion
 
#ibmbpsse18 - Den svenska marknaden, Andreas Lundgren, CMO, IBM
#ibmbpsse18 - Den svenska marknaden, Andreas Lundgren, CMO, IBM#ibmbpsse18 - Den svenska marknaden, Andreas Lundgren, CMO, IBM
#ibmbpsse18 - Den svenska marknaden, Andreas Lundgren, CMO, IBM
 
Multiresursplanering - Karolinska Universitetssjukhuset
Multiresursplanering - Karolinska UniversitetssjukhusetMultiresursplanering - Karolinska Universitetssjukhuset
Multiresursplanering - Karolinska Universitetssjukhuset
 
Solving Challenges With 'Huge Data'
Solving Challenges With 'Huge Data'Solving Challenges With 'Huge Data'
Solving Challenges With 'Huge Data'
 
Blockchain explored
Blockchain explored Blockchain explored
Blockchain explored
 
Blockchain architected
Blockchain architectedBlockchain architected
Blockchain architected
 
Blockchain explained
Blockchain explainedBlockchain explained
Blockchain explained
 
Grow smarter project kista watson summit 2018_tommy auoja-1
Grow smarter project  kista watson summit 2018_tommy auoja-1Grow smarter project  kista watson summit 2018_tommy auoja-1
Grow smarter project kista watson summit 2018_tommy auoja-1
 
Bemanningsplanering axfood och houston final
Bemanningsplanering axfood och houston finalBemanningsplanering axfood och houston final
Bemanningsplanering axfood och houston final
 
Power ai nordics dcm
Power ai nordics dcmPower ai nordics dcm
Power ai nordics dcm
 
Nvidia and ibm presentation feb18
Nvidia and ibm presentation feb18Nvidia and ibm presentation feb18
Nvidia and ibm presentation feb18
 
Hwx introduction to_ibm_ai
Hwx introduction to_ibm_aiHwx introduction to_ibm_ai
Hwx introduction to_ibm_ai
 
Ac922 watson 180208 v1
Ac922 watson 180208 v1Ac922 watson 180208 v1
Ac922 watson 180208 v1
 
Watson kista summit 2018 box
Watson kista summit 2018 box Watson kista summit 2018 box
Watson kista summit 2018 box
 
Watson kista summit 2018 en bättre arbetsdag för de många människorna
Watson kista summit 2018   en bättre arbetsdag för de många människornaWatson kista summit 2018   en bättre arbetsdag för de många människorna
Watson kista summit 2018 en bättre arbetsdag för de många människorna
 
Iwcs and cisco watson kista summit 2018 v2
Iwcs and cisco   watson kista summit 2018 v2Iwcs and cisco   watson kista summit 2018 v2
Iwcs and cisco watson kista summit 2018 v2
 
Ibm intro (watson summit) bkacke
Ibm intro (watson summit) bkackeIbm intro (watson summit) bkacke
Ibm intro (watson summit) bkacke
 

Último

Cloud Frontiers: A Deep Dive into Serverless Spatial Data and FME
Cloud Frontiers:  A Deep Dive into Serverless Spatial Data and FMECloud Frontiers:  A Deep Dive into Serverless Spatial Data and FME
Cloud Frontiers: A Deep Dive into Serverless Spatial Data and FME
Safe Software
 

Último (20)

Polkadot JAM Slides - Token2049 - By Dr. Gavin Wood
Polkadot JAM Slides - Token2049 - By Dr. Gavin WoodPolkadot JAM Slides - Token2049 - By Dr. Gavin Wood
Polkadot JAM Slides - Token2049 - By Dr. Gavin Wood
 
MINDCTI Revenue Release Quarter One 2024
MINDCTI Revenue Release Quarter One 2024MINDCTI Revenue Release Quarter One 2024
MINDCTI Revenue Release Quarter One 2024
 
Cyberprint. Dark Pink Apt Group [EN].pdf
Cyberprint. Dark Pink Apt Group [EN].pdfCyberprint. Dark Pink Apt Group [EN].pdf
Cyberprint. Dark Pink Apt Group [EN].pdf
 
Exploring Multimodal Embeddings with Milvus
Exploring Multimodal Embeddings with MilvusExploring Multimodal Embeddings with Milvus
Exploring Multimodal Embeddings with Milvus
 
TrustArc Webinar - Unlock the Power of AI-Driven Data Discovery
TrustArc Webinar - Unlock the Power of AI-Driven Data DiscoveryTrustArc Webinar - Unlock the Power of AI-Driven Data Discovery
TrustArc Webinar - Unlock the Power of AI-Driven Data Discovery
 
Apidays New York 2024 - The Good, the Bad and the Governed by David O'Neill, ...
Apidays New York 2024 - The Good, the Bad and the Governed by David O'Neill, ...Apidays New York 2024 - The Good, the Bad and the Governed by David O'Neill, ...
Apidays New York 2024 - The Good, the Bad and the Governed by David O'Neill, ...
 
Ransomware_Q4_2023. The report. [EN].pdf
Ransomware_Q4_2023. The report. [EN].pdfRansomware_Q4_2023. The report. [EN].pdf
Ransomware_Q4_2023. The report. [EN].pdf
 
Boost Fertility New Invention Ups Success Rates.pdf
Boost Fertility New Invention Ups Success Rates.pdfBoost Fertility New Invention Ups Success Rates.pdf
Boost Fertility New Invention Ups Success Rates.pdf
 
DEV meet-up UiPath Document Understanding May 7 2024 Amsterdam
DEV meet-up UiPath Document Understanding May 7 2024 AmsterdamDEV meet-up UiPath Document Understanding May 7 2024 Amsterdam
DEV meet-up UiPath Document Understanding May 7 2024 Amsterdam
 
Strategies for Landing an Oracle DBA Job as a Fresher
Strategies for Landing an Oracle DBA Job as a FresherStrategies for Landing an Oracle DBA Job as a Fresher
Strategies for Landing an Oracle DBA Job as a Fresher
 
CNIC Information System with Pakdata Cf In Pakistan
CNIC Information System with Pakdata Cf In PakistanCNIC Information System with Pakdata Cf In Pakistan
CNIC Information System with Pakdata Cf In Pakistan
 
Rising Above_ Dubai Floods and the Fortitude of Dubai International Airport.pdf
Rising Above_ Dubai Floods and the Fortitude of Dubai International Airport.pdfRising Above_ Dubai Floods and the Fortitude of Dubai International Airport.pdf
Rising Above_ Dubai Floods and the Fortitude of Dubai International Airport.pdf
 
How to Troubleshoot Apps for the Modern Connected Worker
How to Troubleshoot Apps for the Modern Connected WorkerHow to Troubleshoot Apps for the Modern Connected Worker
How to Troubleshoot Apps for the Modern Connected Worker
 
Spring Boot vs Quarkus the ultimate battle - DevoxxUK
Spring Boot vs Quarkus the ultimate battle - DevoxxUKSpring Boot vs Quarkus the ultimate battle - DevoxxUK
Spring Boot vs Quarkus the ultimate battle - DevoxxUK
 
ProductAnonymous-April2024-WinProductDiscovery-MelissaKlemke
ProductAnonymous-April2024-WinProductDiscovery-MelissaKlemkeProductAnonymous-April2024-WinProductDiscovery-MelissaKlemke
ProductAnonymous-April2024-WinProductDiscovery-MelissaKlemke
 
Artificial Intelligence Chap.5 : Uncertainty
Artificial Intelligence Chap.5 : UncertaintyArtificial Intelligence Chap.5 : Uncertainty
Artificial Intelligence Chap.5 : Uncertainty
 
[BuildWithAI] Introduction to Gemini.pdf
[BuildWithAI] Introduction to Gemini.pdf[BuildWithAI] Introduction to Gemini.pdf
[BuildWithAI] Introduction to Gemini.pdf
 
MS Copilot expands with MS Graph connectors
MS Copilot expands with MS Graph connectorsMS Copilot expands with MS Graph connectors
MS Copilot expands with MS Graph connectors
 
Navigating the Deluge_ Dubai Floods and the Resilience of Dubai International...
Navigating the Deluge_ Dubai Floods and the Resilience of Dubai International...Navigating the Deluge_ Dubai Floods and the Resilience of Dubai International...
Navigating the Deluge_ Dubai Floods and the Resilience of Dubai International...
 
Cloud Frontiers: A Deep Dive into Serverless Spatial Data and FME
Cloud Frontiers:  A Deep Dive into Serverless Spatial Data and FMECloud Frontiers:  A Deep Dive into Serverless Spatial Data and FME
Cloud Frontiers: A Deep Dive into Serverless Spatial Data and FME
 

Prediktiv analys och kundlojalitet

  • 1.
  • 2. Prediktiv analys och kundlojalitet Robert Moberg Predictive Analytics Solutions Architect
  • 3. Let me start with a few of qoutes... § ”In the factory we manufacture lipstick, in the store we sell hope” – Charles Revson – 1906 – 1975 – Creater of Revlon § ”People don't want to buy a quarter-inch drill, they want a quarter-inch hole.” – Theodore Levitt – 1925 – 2006 – Harvard Professor § ”We don’t sell an analytics platform – we provide happy, loyal and prospering customers” – Robert Moberg – 1969 – – PASA at IBM
  • 4. It is not the strongest of the species that survive, nor the most intelligent, but the one most responsive to Charles Darwin change.
  • 5. A Sample of A Universe of Things That Generate Data Data ?
  • 6. A Universe of A Predictive Data Model Attributes •Married, 2 kids •Home owner in Liseberg, Älvsjö •Has a house in Gotland •Owns a car •41 years old •Enjoys fine wines and champagne •Plays golf Predicted Attributes •Likes Beastie boys •Likes Gotland •Works long hours •Commutes •Middle Income Predicted Behavior •Dines in descent restaurants •Consumes a lot of electricity •Buys green fees •Family vacations
  • 7. An Overwhelming Amount of Data to Process High-value, dynamic Social Media - source of competitive differentiation Open-Ended (networks) Surveys Interaction data Attitudinal - E-Mail / chat data transcripts - Opinions - Call center notes - Preferences - Web Click-streams 360 degree - Needs & - In person dialogues Desires Customer View Descriptive data Behavioral - Attributes data - Characteristics - Orders - Self-declared info - Transactions CRM - - Payment Operational Systems (Geo)demographic history Systems s “Traditional - Usage history ” IBM confidential
  • 8. Evolutionary Solutions for Customer Intimacy Differentiating Breakaway Foundational Competitive Insight for Decision Makers The Next Best Action
  • 9. Define the Strategy Run the Business Year Month Week Day Hour No s s s s s w Time to Business Impact Improve senior management Improve policy makers’ Help individual visibility with decisions with contributors take the Key Performance Predictors Forecasts and Optimization Next Best Action Strategic Tactica Operational l
  • 10. Predictive Customer Analytics Predictive Predictive Operational Threat and Analytics Risk Analytics
  • 11. Customer Analytics One to One Research & Purchase Purcha Product Advoc se ate More Produ ct Get Use Customer Prod The Broad Brush Service uct
  • 12. Operational Analytics Agile Develop ment Procure ment Availab Distributi ility on Long term planning
  • 13. Risk Analytics Proactive Defin e Allow Monito Preven r t Corrective Detec t
  • 14. Darwinism according to me: It is not the vData is key strongest of the vUnderstanding data is one thing knowing what to do with it is another species that vIt’s easier to give people what they want if you know the survive, nor what that is most intelligent, vCustomer analytics, a prerequisite to but the one your be relevant to most customers responsive to vCustomer analytics and insights provide decision support Charles Darwin change. vDecisions will be reliable, because they are based on facts not on speculation Predictive Customer Analytics will make your company responsive to change!!!
  • 15. TACK