1. Data Mining Overview

2. Lecture Objectives After this lecture, you should be able to: 1.Explain key data mining tasks in your own words. 2.Draw an overview of the Data Mining Process. 3.Discuss one broad business application of data mining. 4.Explain one way to evaluate effectiveness of a Data Mining project.

3. Data Mining Tasks 1.Prediction / Classification 2.Segmentation 3.Association

4. Course Overview/Techniques Used Data Preparation Prediction/Classification Discriminant Analysis Logistic Regression Artificial Neural Networks Classification Trees (CART, CHAID) Segmentation Judgement Factor Analysis Cluster Analysis Association Market Basket Analysis Other Correlation Based techniques

5. Data Mining Process Source: CRISP-DM (SPSS.com website)

6. Application in Financial Services Product Planning Customer Acquisition Collections and Recovery Customer Manage- ment Customer Valuation Stage 1Stage 2 Stage 4 Stage 3

7. Measuring Effectiveness: Lift/Gains Chart Dr. Satish Nargundkar Percent of population targeted Percent of potential responders captured 100 0 90 45 Targeting Random mailing

8. Discussion 1.Can you think of other applications? 2.What are some limitations of Data Mining? 3.What are future possibilities?