Transcript CE14
Chapter Extension 14 Database Marketing © 2008 Pearson Prentice Hall, Experiencing MIS, David Kroenke Study Questions CE14-2 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? © 2008 Pearson Prentice Hall, Experiencing MIS, David Kroenke What Is a Database Marketing Opportunity? Database marketing – – – CE14-3 Data business intelligent systems applied to planning and execution of marketing programs Databases key component Data-mining techniques also important © 2008 Pearson Prentice Hall, Experiencing MIS, David Kroenke RFM Analysis RFM: – – – Program that analyzes and ranks customers according to purchases – – CE14-4 How recently customers ordered How frequently How much money they spent per order Programs first sorts for recent purchases and ranks customers Divides into five groups © 2008 Pearson Prentice Hall, Experiencing MIS, David Kroenke RFM Analysis, continued – – – CE14-5 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 © 2008 Pearson Prentice Hall, Experiencing MIS, David Kroenke Example of RFM Score Data Figure CE14-1 CE14-6 © 2008 Pearson Prentice Hall, Experiencing MIS, David Kroenke Market-Basket Analysis Data-mining technique Determines sales patterns – – – Support – Conditional probability estimate Lift – CE14-7 Probability that two items will be bought together Confidence – Shows products that customers buy together Estimate probability of customer purchase Creates cross-selling opportunity Ratio of confidence to base probability of buying item © 2008 Pearson Prentice Hall, Experiencing MIS, David Kroenke Market-Basket Example Figure CE14-2 CE14-8 © 2008 Pearson Prentice Hall, Experiencing MIS, David Kroenke Decision Trees Hierarchical arrangement of criteria Predicts classification or value Creating the decision tree – – Gather data and attributes Select attributes that create disparate groups CE14-9 More different the groups, the better the classification Transform into set of decision rules having format “if/then” © 2008 Pearson Prentice Hall, Experiencing MIS, David Kroenke Decision Tree Figure CE14-3 CE14-10 © 2008 Pearson Prentice Hall, Experiencing MIS, David Kroenke Decision Tree for Loan Evaluation Common business application Classify loans by likelihood of default Rules identify loans for bank approval – – – CE14-11 Identify market segment Structure marketing campaign Predict problems © 2008 Pearson Prentice Hall, Experiencing MIS, David Kroenke Credit Score Decision Tree Figure CE14-4 CE14-12 © 2008 Pearson Prentice Hall, Experiencing MIS, David Kroenke Active Review CE14-13 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? © 2008 Pearson Prentice Hall, Experiencing MIS, David Kroenke