Chapter Extension 15 Database Marketing

Q1:What is a database marketing opportunity? Q2: How does RFM analysis classify customers? Q3: How does market-basket analysis identify cross-selling opportunities? Q4: How do decision trees identify market segments? Study Questions Copyright © 2012 Pearson Education, Inc. Publishing as Prentice Hall CE15-2

Application of business intelligence systems for planning and executing marketing programsbusiness intelligence systems Databases a key component Data-mining techniques importantData-mining Process of sorting through large amounts of data and picking out relevant information Database marketing Q1: What Is a Database Marketing Opportunity? Copyright © 2012 Pearson Education, Inc. Publishing as Prentice Hall CE15-3

Retailer of trees, plants, annual flowers Can’t keep track of lost customers Lost a best customer and didn’t know it Has all sorts of sales data but needs a way to analyze it Database Marketing Scenario: Carbon Creek Gardens Copyright © 2012 Pearson Education, Inc. Publishing as Prentice Hall CE15-4

RFM program analyzes and ranks customers according to their purchase patterns How recently (R) a customer has ordered? How frequently (F) a customer has ordered? How much money (M) a customer has spent per order? RFM 1.Sorts customer records by date of most recent purchase and scores each customer 1 to 5 2.Re-sorts customers by how frequently they order and scores each customer 1 to 5 3.Sorts customers according to amount of money spent on orders and scores each customer 1 to 5 RFM Score Q2: How Does RFM Analysis Classify Customers? Copyright © 2012 Pearson Education, Inc. Publishing as Prentice Hall CE15-5

RFM Analysis Classifies Customers Copyright © 2012 Pearson Education, Inc. Publishing as Prentice Hall Top 20% Bottom 20% Middle 20% Recent orders Frequent orders Money (amount) of money spent 9-6

Example of RFM Score Data Copyright © 2012 Pearson Education, Inc. Publishing as Prentice Hall CE15-7 CustomerRFM Score Ajax1 1 3 Bloominghams5 1 1 Caruthers5 4 5 Davidson3 3 3

A good and regular customer but need to attempt to up-sell more expensive goods to Ajax Ajax ordered recently and orders frequently, average spender May have taken business to another vendor. Sales team should contact this customer immediately Bloominghams not ordered in long time; when it did, ordered frequently, and large value Interpreting RFM Score Results Copyright © 2012 Pearson Education, Inc. Publishing as Prentice Hall CE15-8

Sales team should not spend a lot of time on this customer Caruthers not ordered for long time; average frequency; average spender Set up on automated contact system or use Davidson account as a training exercise Davidson is all average Interpreting RFM Score Results (cont’d) Copyright © 2012 Pearson Education, Inc. Publishing as Prentice Hall CE15-9

Market-basket analysisMarket-basket analysis—a data-mining technique for determining sales patterns Uses statistical methods to identify sales patterns in large volumes of data Shows which products customers tend to buy together Used to estimate probabilities of customer purchases Helps identify cross-selling opportunities "Customers who bought book X also bought book Y” Q3: How Does Market-Basket Analysis Identify Cross-Selling Opportunities? Copyright © 2012 Pearson Education, Inc. Publishing as Prentice Hall CE15-10

Market-Basket Example: Transactions = 400 CE15-11 Copyright © 2012 Pearson Education, Inc. Publishing as Prentice Hall

P(Fins and Mask) = 250/400, or 62% P(Fins and Fins) = 280/400, or 70% Support: Probability that Two Items Will Be Bought Together Copyright © 2012 Pearson Education, Inc. Publishing as Prentice Hall CE15-12

Probability of buying Fins = 250 Probability of buying Mask = 270 P(After buying Mask, then will buy Fins) Confidence = 250/270 or 93% Confidence = Conditional Probability Estimate Copyright © 2012 Pearson Education, Inc. Publishing as Prentice Hall CE15-13

Lift = P(Fins|Mask)/P(Fins) Purchase of Masks lifts probability of also purchasing Fins by.93/.62, or 1.32 Lift: How Much Base Probability Increases or Decreases When Other Product(s) Purchased Copyright © 2012 Pearson Education, Inc. Publishing as Prentice Hall CE15-14 Lift = Confidence/Support

Decision tree Hierarchical arrangement of criteria that predict a classification or value Unsupervised data-mining technique Basic idea of a decision tree Select attributes most useful for classifying something on some criteria that will create “pure groups” Q4: How Do Decision Trees Identify Market Segments? Copyright © 2012 Pearson Education, Inc. Publishing as Prentice Hall CE15-15

Figure CE15-3 A Decision Tree for Student Performance Copyright © 2012 Pearson Education, Inc. Publishing as Prentice Hall If Senior = YesIf Junior = Yes Lower-level groups more similar than higher-level groups CE15-16 GPAs of Students from Past MIS Class (Hypothetical Data)

If student is a junior and works in a restaurant, Then predict grade 3.0 If student is a senior and is a nonbusiness major, Then predict grade 3.0 If student is a junior and does not work in a restaurant, Then predict grade 3.0 If student is a senior and is a business major, Then make no prediction Create Set of If/Then Decision Rules 9-17 Copyright © 2012 Pearson Education, Inc. Publishing as Prentice Hall

Classify loan applications by likelihood of default Rules identify loans for bank approval Identify market segment Structure marketing campaign Predict problems Common business application Decision Tree for Loan Evaluation Copyright © 2012 Pearson Education, Inc. Publishing as Prentice Hall CE15-18

Example of Insightful Miner CE15-19 Copyright © 2012 Pearson Education, Inc. Publishing as Prentice Hall

If loan is more than half paid, then approve loan If loan is less than half paid and If CreditScore is greater than and If CurrentLTV is less than.94 Then, approve loan application Otherwise, reject loan application Decision Tree: If/Then Decision Rules for a Loan Evaluation Copyright © 2012 Pearson Education, Inc. Publishing as Prentice Hall Otherwise, reject loan application

Classifying people can raise serious ethical issues. What about classifying applicants for college when more applicants than positions? Using decision-tree data-mining program to derive statistically valid measures. No human judgment involved. Analysis might not include important data; results could reinforce social stereotypes. Might not be organizationally, legally, or socially feasible. Ethics Guide: The Ethics of Classification Copyright © 2012 Pearson Education, Inc. Publishing as Prentice Hall CE15-21

Active Review Copyright © 2012 Pearson Education, Inc. Publishing as Prentice Hall Q1:What is a database marketing opportunity? Q2: How does RFM analysis classify customers? Q3: How does market-basket analysis identify cross-selling opportunities? Q4: How do decision trees identify market segments? CE15-22

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