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See It in SPSS: Data Mining with Clementine


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See It in SPSS: Data Mining with Clementine

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