1. Chapter Extension 14 Database Marketing © 2008 Pearson Prentice Hall, Experiencing MIS, David Kroenke

2. CE14-2 Study Questions What is a database marketing opportunity? How does RFM analysis classify customers? How does market-based analysis identify cross-selling opportunities? How do decision trees identify market segments?

3. © 2008 Pearson Prentice Hall, Experiencing MIS, David Kroenke CE14-3 What Is a Database Marketing Opportunity? Database marketing – Data business intelligent systems applied to planning and execution of marketing programs – Databases key component – Data-mining techniques also important

4. © 2008 Pearson Prentice Hall, Experiencing MIS, David Kroenke CE14-4 RFM Analysis RFM: – How recently customers ordered – How frequently – How much money they spent per order Program that analyzes and ranks customers according to purchases – Programs first sorts for recent purchases and ranks customers – Divides into five groups

5. © 2008 Pearson Prentice Hall, Experiencing MIS, David Kroenke CE14-5 RFM Analysis, continued – Assigns R score of 1 through 5 – Score determined by which percentage group customer is in – Repeats with frequency (F score) and money (M score) Assists companies in determining which customers to service

6. © 2008 Pearson Prentice Hall, Experiencing MIS, David Kroenke CE14-6 Example of RFM Score Data Figure CE14-1

7. © 2008 Pearson Prentice Hall, Experiencing MIS, David Kroenke CE14-7 Market-Basket Analysis Data-mining technique Determines sales patterns – Shows products that customers buy together – Estimate probability of customer purchase – Creates cross-selling opportunity Support – Probability that two items will be bought together Confidence – Conditional probability estimate Lift – Ratio of confidence to base probability of buying item

8. © 2008 Pearson Prentice Hall, Experiencing MIS, David Kroenke CE14-8 Market-Basket Example Figure CE14-2

9. © 2008 Pearson Prentice Hall, Experiencing MIS, David Kroenke CE14-9 Decision Trees Hierarchical arrangement of criteria Predicts classification or value Creating the decision tree – Gather data and attributes – Select attributes that create disparate groups More different the groups, the better the classification Transform into set of decision rules having format “if/then”

10. © 2008 Pearson Prentice Hall, Experiencing MIS, David Kroenke CE14-10 Decision Tree Figure CE14-3

11. © 2008 Pearson Prentice Hall, Experiencing MIS, David Kroenke CE14-11 Decision Tree for Loan Evaluation Common business application Classify loans by likelihood of default Rules identify loans for bank approval – Identify market segment – Structure marketing campaign – Predict problems

12. © 2008 Pearson Prentice Hall, Experiencing MIS, David Kroenke CE14-12 Credit Score Decision Tree Figure CE14-4

13. © 2008 Pearson Prentice Hall, Experiencing MIS, David Kroenke CE14-13 Active Review What is a database marketing opportunity? How does RFM analysis classify customers? How does market-based analysis identify cross-selling opportunities? How do decision trees identify market segments?