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See It in SPSS: Data Mining with Clementine
See It in SPSS: Data Mining with Clementine
See It in SPSS: Data Mining with Clementine
See It in SPSS: Data Mining with Clementine
See It in SPSS: Data Mining with Clementine
See It in SPSS: Data Mining with Clementine
See It in SPSS: Data Mining with Clementine
See It in SPSS: Data Mining with Clementine
See It in SPSS: Data Mining with Clementine
See It in SPSS: Data Mining with Clementine
See It in SPSS: Data Mining with Clementine
See It in SPSS: Data Mining with Clementine
See It in SPSS: Data Mining with Clementine
See It in SPSS: Data Mining with Clementine
See It in SPSS: Data Mining with Clementine
See It in SPSS: Data Mining with Clementine
See It in SPSS: Data Mining with Clementine
See It in SPSS: Data Mining with Clementine
See It in SPSS: Data Mining with Clementine
See It in SPSS: Data Mining with Clementine
See It in SPSS: Data Mining with Clementine
See It in SPSS: Data Mining with Clementine
See It in SPSS: Data Mining with Clementine
See It in SPSS: Data Mining with Clementine
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See It in SPSS: Data Mining with Clementine

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  • 1. See It in SPSS: Data Mining with Clementine Prety Widjaja Systems Engineer SPSS Inc.
  • 2. Agenda
    • Data Mining Myths
    • Data Mining Definition
    • Data Mining Methodology
    • Clementine Demonstration
    • Customer Success Stories
    • Q&A
  • 3. Data Mining Myths
    • Is all about algorithms
    • Requires massive amount of data
    • Requires a data warehouse
  • 4. What is Data Mining? “ The process of discovering meaningful new relationships, patterns and trends by sifting through data using pattern recognition technologies as well as statistical and mathematical techniques.” The Gartner Group
  • 5. What is Data Mining?
    • Discovering meaningful patterns in your data
  • 6. What is Data Mining? As the data grows… the relationships become more complicated.
  • 7. Data Mining: Defined
    • Data driven approach to problem solving
    • Focused on Organizational Objectives
    • Leverages organizational data
    • Uncovers patterns using predictive analytics
    • Uses results to help improve decision making and organizational performance
  • 8. Data at the Heart of the Predictive Enterprise
    • Behavioral data
    • - Orders
    • - Transactions
    • Payment history
    • Renewal history
    • Descriptive data
    • Attributes
    • Characteristics
    • Self-declared info
    • (Geo)demographics
    • Attitudinal data
    • - Opinions
    • Preferences
    • Needs
    • Desires
    • Interaction data
    • - Offers
    • Results
    • Context
    • Click streams
    • Notes
    Customer View Enterprise Data Sources Marketing Attitudinal Interaction Web Call-center Operational Customer Contact Channels Website Email Phone Mail Branch ATM Agent Mobile… Text mining Web mining Feedback
  • 9. Common Applications in Business Enterprise
    • Customer Analytics
    • Process Improvement
    • Resource Management
    • Fraud and Risk Detection
  • 10. Common Applications in Public Sector
    • Tax and Revenue:
      • Reduce the ‘tax gap’
      • Improve audit selection
    • Law Enforcement:
      • Effective force deployment
      • Reduce crimes
    • Fraud, Waste and Abuse:
      • Detect errors and improper payments
    • Resource Management
    • Education:
      • Administration and Institutional Research
      • Donor and alumni Development
      • Educators/Teaching
  • 11.
    • Where do you start?
  • 12. CRISP-DM Methodology
      • Cross Industry Standard Process for Data Mining
      • Focused on business issues
      • Consortium of partners:
        • SPSS
        • NCR/Teradata
        • Daimler-Benz
        • OHRA
      • Application neutral
      • Industry neutral
  • 13. SPSS Data Mining Workbench: Clementine
    • Unparalleled productivity
      • Intuitive visual interface
      • Breadth of techniques for modeling
      • Multi-modeling execution
    • Leverages your IT database investment
      • Access various data formats
      • Join multiple data files
    • Full integration with SPSS Base
    • Scalable
    • Deployment
      • Various exporting formats
      • Scoring new data
  • 14. Demonstration
    • Business Challenge: identify profiles of employees that are at high risk of leaving the organization (churn).
  • 15. Results in Simple Terms:
    • Rule 4 for Employee departure (20 employees in this group, 90% confidence)
      • If Found Work Enjoyable = Yes
      • And Received Benefits = No
      • And Mentioned Compensation = Yes
      • And Mentioned Perks = No
      • And Work Facility = Facility A
    • Then Employee Departed
  • 16. Summary
    • Industry standard process
    • Open system
    • Easy to use graphic interface
    • Flexibility
    • Productivity
  • 17. More successful applications of predictive analytics
    • Some examples…
  • 18. Credit Suisse’s Marketing Campaign
    • Increase profitability
    • Retain customers
    • Reduce cost by 50% over a 2 year period
  • 19. Education Institution
    • Increased tuition revenue
    • Reduced Marketing costs
    • Improved curriculum offerings
    • Improved student retention
    Results
  • 20. Tax and Revenue Results
    • Reduced State Tax Gap
    • Recovered $400 million in unpaid taxes over a five-year period
  • 21. Data Mining Tools Leader
    • Leader: Gartner Magic Quadrant 1/2006
    • Leader MetaSpectrum Analysis 10/2004
    • Most popular data mining technology 5 years running at www.kdnuggets.com
  • 22. Recent Awards
    • SPSS Inc. was included in the listing of the annual DM Review 100, which constitutes the top 100 companies in the business intelligence space as determined by DM Review readers.
    • KDnuggets News , a data mining and knowledge discovery newsletter, polled more than 600 of its readers, to find out which data mining tool they regularly used. The #1 response was SPSS Inc.'s Clementine data mining workbench, for the 4th year in a row.
    • SPSS Inc. was ranked first in the Intelligent Enterprise “2004 Companies to Watch.” These awards highlight companies that provide the strongest vision, market leadership and technology innovation .
  • 23. Question and Answer
  • 24. For More Information
    • In case you missed it: recorded version and slides available at www.spss.com/events
    • Visit www.spss.com/ clementine to learn more about the platform
    • Call us at 1-800-543-2185 or [email_address]
    • Please fill out the post event survey

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