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The Why Behind the What

How to Achieve Deeper Customer Insight



Jane Hendricks
Product Marketing, SPSS Inc.
Common Questions
                   1. Will I be able to get copies of the slides
                      after the event?



                   2. Is this web seminar being taped so I or
                      others can view it after the fact?



                   3. Can I ask questions during this event?




                                                                   2
Copyright 2003-4, SPSS Inc.
Common Questions
                   1. Will I be able to get copies of the slides
                      after the event?
                                         Yes

                   2. Is this web seminar being taped so I or
                      others can view it after the fact?

                                         Yes
                   3. Can I ask questions during this event?

                                          Yes


                                                                   3
Copyright 2003-4, SPSS Inc.
Topics for This Session

 A Case Study
 Completing the Customer View
 Enterprise Feedback Management
 Inside SPSS Data Collection
Cablecom
Improves Retention of Profitable Customers

Background
 Swiss Telco and TV operator
 Over 54% of Swiss television households
 receive cable service from Cablecom
                                                    Results
Business Goals
 Reduce the number of profitable customers
                                             Identification of key business
 who defect to competitors
                                             issues from customers’ own
 Understand customer behavior                words
 Anticipate market trends                    Improved effectiveness of
                                             predictive models
Solution                                     Now keep retention rates high
 Surveyed existing customers, including      51% of Cablecom dissatisfied
 loyalty metrics and open-ended questions    customers became company
                                             promoters (very satisfied
 Added text mining to data mining through
                                             customers) after two months
 Text Mining for Clementine to better
 understand the reason for churn
Data About People
                         Customer Contact Channels
   Web Site   Email   Agent   Mail   Phone   PDA     Branch    ATM


   Interaction                                                Attitudinal
   Data                                                       Data
   Click Streams                                              Opinions
   Offers                                                     Preferences
   Results                                                    Needs
   Context                                                    Desires
   Notes                                                      Moods



                                                              Behavioral
   Descriptive
                                                              Data
   Data
                                                              Orders
   Attributes
                                                              Transactions
   Characteristics
                                                              Payment History
   Self-declared Info
                                                              Usage History
   (Geo)demographics
                                                              Location


   Operational
               Attitudinal Marketing Web Call Center Social Networks
   Interaction
                            Enterprise Data Sources
Bridging Customer Insight Activities

    Attitudinal Analysis                             Behavioral Analysis
  Issue: Isolated, empty feedback                 Issue: No holistic view of the data




                          Enterprise Feedback
                              Management
                         Centralized, Standardized, Integrated
The Case For Attitudinal Data

BUYING BEHAVIOR         DEMOGRAPHIC SEGMENTS
                  Married    Single     Married       Single Men
                  Males < 30 Males < 30 Men > 30      > 30


                  564
Product A                    31          19           645

                                 Same demographics
                                  Same demographics
                    69       950–but different behavior??
Product B                                 92           22
                               – but different behavior??



Product C         602        91          887          76
The Case For Attitudinal Data

BUYING BEHAVIOR         DEMOGRAPHIC SEGMENTS
                  Married    Single     Married        Single Men
                  Males < 30 Males < 30 Men > 30       > 30


                  564
Product A                     31           19          645


                    69and different behavior
                             Same behavior
                              950 demographics??
Product B                                  92          22
                              Same
                     –– and different demographics??


                  602         91
Product C                                 887          76
The Case For Attitudinal Data

   Married Men, Under 30




    Married Men, Over 30




                                Product A
                                Product C
The Case For Attitudinal Data

   Married Men, Under 30
                                    Prefers red over yellow
                                    Prefers sweet over sour
                                    Environmentally aware
                                    A trendsetter




    Married Men, Over 30   “The Why Behind The What”



                                                              Product A
                                                              Product C
Acting With Precision
Attract                                               Risk
              Cross-sell and
                                   Assessment of Risk
              Up-sell Offers via
                                   at the Point of Data
              the Web Site
                                   Entry



                     Interaction        Attitudinal
                         Data               Data




                                                                               Fraud
                     Descriptive        Behavioral
                        Data               Data
                                                                 Real-time Identification
                                                             of Suspicious Transactions
          Sales Offers
          Presented via
          the Call Center          Real-time Retention
                                   Actions via Chat
          Grow
                                   Messages                                Retain
Enterprise Feedback Management (EFM)
Capturing the Why
  A centralized system that allows organizations to fully
  engage with their current or prospective customers…
  through targeted feedback programs or by asking
  questions during naturally occurring interactions and…
  to utilize that information throughout the organization to
  drive customer-centricity and business improvement.
          Voice of the Customer                   Other “Enlightenment” Data
                        Opinions                  Click streams
               Needs & Desires                    Notes/text/unstructured
                     Preferences                  (Geo)demographics
                          Moods
                     Satisfaction

                                    Customer Data
                                       Location
                                    Characteristics
                                     Interactions
SPSS Supports Enterprise Feedback
Management (EFM)
      EFM DELIVERS                         SPSS PROVIDES
 Increased quality, frequency and      One survey for all respondents
 value of customer interactions with   across all channels with a single
 very little marginal cost             system asdfasdf
 Feedback cycles are greatly           Immediate, accessible insight
 reduced so action can be taken in a   across all modes and channels
                                       blah blah
 timely manner
 Data collection process is            Single feedback system providing
 standardized with necessary           secure access to data and survey
 compliance oversight                  tools across the enterprise
                                       Single feedback system for all
 Consolidation on a central feedback
 platform saves money and allows       survey needs with centralized control
 integration with IT infrastructure    over survey assets
One Survey for All Touchpoints




    Online    By Phone    In Person   On Paper
True Multi-Modal Data Collection
Single Platform for all Feedback Needs
                                                     Authoring                                            Collection


                   Accessible to non-survey expert                                                              Secure data storage
                    Assets centralized and reused                                                           Single system for all modes
                   Power and flexibility behind an                                                        Single survey for all modes and
                          intuitive interface                                                                      all languages
                                                                                                              Online
                                                                                                            By Phone
                                                     Author once,
                                                                                                            In Person
                                                   Field everywhere,
                                                                                 Data                     Any Language
                                                    In any language
                                                                            Research Tools
                                                                               Security
                                                                                Assets
      Insight Delivered                                                                                   Analysis and Integration
60%


50%


40%




                                                                           Secure access to tools
30%


20%



                                                                       and individual research projects        Data available for real-time
                Deliver insight to
10%




                                                                            on desktop or via web
0%


                                                                                                                        analysis
      Excellent   Very Good   Good   Fair   Poor

                key stakeholders
                                                                                                            Incorporate survey data with any
       quickly to incorporate feedback in
        Online or Desktop process                                                                                 other data sources
           decision making
          Any language
             Secure
       Presentation Ready
Questions?




             Jane Hendricks
             Product Marketing- Dimensions
             SPSS Inc.
             Chicago, IL
             P. 800.543.2185 extension 3026
             e-mail: jhendricks@spss.com
             website: www.spss.com

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The Why

  • 1. The Why Behind the What How to Achieve Deeper Customer Insight Jane Hendricks Product Marketing, SPSS Inc.
  • 2. Common Questions 1. Will I be able to get copies of the slides after the event? 2. Is this web seminar being taped so I or others can view it after the fact? 3. Can I ask questions during this event? 2 Copyright 2003-4, SPSS Inc.
  • 3. Common Questions 1. Will I be able to get copies of the slides after the event? Yes 2. Is this web seminar being taped so I or others can view it after the fact? Yes 3. Can I ask questions during this event? Yes 3 Copyright 2003-4, SPSS Inc.
  • 4. Topics for This Session A Case Study Completing the Customer View Enterprise Feedback Management Inside SPSS Data Collection
  • 5. Cablecom Improves Retention of Profitable Customers Background Swiss Telco and TV operator Over 54% of Swiss television households receive cable service from Cablecom Results Business Goals Reduce the number of profitable customers Identification of key business who defect to competitors issues from customers’ own Understand customer behavior words Anticipate market trends Improved effectiveness of predictive models Solution Now keep retention rates high Surveyed existing customers, including 51% of Cablecom dissatisfied loyalty metrics and open-ended questions customers became company promoters (very satisfied Added text mining to data mining through customers) after two months Text Mining for Clementine to better understand the reason for churn
  • 6. Data About People Customer Contact Channels Web Site Email Agent Mail Phone PDA Branch ATM Interaction Attitudinal Data Data Click Streams Opinions Offers Preferences Results Needs Context Desires Notes Moods Behavioral Descriptive Data Data Orders Attributes Transactions Characteristics Payment History Self-declared Info Usage History (Geo)demographics Location Operational Attitudinal Marketing Web Call Center Social Networks Interaction Enterprise Data Sources
  • 7. Bridging Customer Insight Activities Attitudinal Analysis Behavioral Analysis Issue: Isolated, empty feedback Issue: No holistic view of the data Enterprise Feedback Management Centralized, Standardized, Integrated
  • 8. The Case For Attitudinal Data BUYING BEHAVIOR DEMOGRAPHIC SEGMENTS Married Single Married Single Men Males < 30 Males < 30 Men > 30 > 30 564 Product A 31 19 645 Same demographics Same demographics 69 950–but different behavior?? Product B 92 22 – but different behavior?? Product C 602 91 887 76
  • 9. The Case For Attitudinal Data BUYING BEHAVIOR DEMOGRAPHIC SEGMENTS Married Single Married Single Men Males < 30 Males < 30 Men > 30 > 30 564 Product A 31 19 645 69and different behavior Same behavior 950 demographics?? Product B 92 22 Same –– and different demographics?? 602 91 Product C 887 76
  • 10. The Case For Attitudinal Data Married Men, Under 30 Married Men, Over 30 Product A Product C
  • 11. The Case For Attitudinal Data Married Men, Under 30 Prefers red over yellow Prefers sweet over sour Environmentally aware A trendsetter Married Men, Over 30 “The Why Behind The What” Product A Product C
  • 12. Acting With Precision Attract Risk Cross-sell and Assessment of Risk Up-sell Offers via at the Point of Data the Web Site Entry Interaction Attitudinal Data Data Fraud Descriptive Behavioral Data Data Real-time Identification of Suspicious Transactions Sales Offers Presented via the Call Center Real-time Retention Actions via Chat Grow Messages Retain
  • 13. Enterprise Feedback Management (EFM) Capturing the Why A centralized system that allows organizations to fully engage with their current or prospective customers… through targeted feedback programs or by asking questions during naturally occurring interactions and… to utilize that information throughout the organization to drive customer-centricity and business improvement. Voice of the Customer Other “Enlightenment” Data Opinions Click streams Needs & Desires Notes/text/unstructured Preferences (Geo)demographics Moods Satisfaction Customer Data Location Characteristics Interactions
  • 14. SPSS Supports Enterprise Feedback Management (EFM) EFM DELIVERS SPSS PROVIDES Increased quality, frequency and One survey for all respondents value of customer interactions with across all channels with a single very little marginal cost system asdfasdf Feedback cycles are greatly Immediate, accessible insight reduced so action can be taken in a across all modes and channels blah blah timely manner Data collection process is Single feedback system providing standardized with necessary secure access to data and survey compliance oversight tools across the enterprise Single feedback system for all Consolidation on a central feedback platform saves money and allows survey needs with centralized control integration with IT infrastructure over survey assets
  • 15. One Survey for All Touchpoints Online By Phone In Person On Paper
  • 16.
  • 17. True Multi-Modal Data Collection
  • 18. Single Platform for all Feedback Needs Authoring Collection Accessible to non-survey expert Secure data storage Assets centralized and reused Single system for all modes Power and flexibility behind an Single survey for all modes and intuitive interface all languages Online By Phone Author once, In Person Field everywhere, Data Any Language In any language Research Tools Security Assets Insight Delivered Analysis and Integration 60% 50% 40% Secure access to tools 30% 20% and individual research projects Data available for real-time Deliver insight to 10% on desktop or via web 0% analysis Excellent Very Good Good Fair Poor key stakeholders Incorporate survey data with any quickly to incorporate feedback in Online or Desktop process other data sources decision making Any language Secure Presentation Ready
  • 19. Questions? Jane Hendricks Product Marketing- Dimensions SPSS Inc. Chicago, IL P. 800.543.2185 extension 3026 e-mail: jhendricks@spss.com website: www.spss.com