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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 programs
• Databases a key component• Data-mining techniques
important• 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 Scor
e
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%
1
2
3
4
5
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
Customer RFM Score
Ajax 1 1 3
Bloominghams 5 1 1
Caruthers 5 4 5
Davidson 3 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 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 AnalysisIdentify Cross-Selling Opportunities?
Copyright © 2012 Pearson Education, Inc. Publishing as Prentice Hall CE15-10
Market-Basket Example: Transactions = 400
CE15-11Copyright © 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 = Yes If 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
9-17Copyright © 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 applicati
on
Decision Tree for Loan Evaluation
Copyright © 2012 Pearson Education, Inc. Publishing as Prentice Hall CE15-18
Example of Insightful Miner
CE15-19Copyright © 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 CreditScor
e is greater
than 572.6 and
If CurrentLTV is less than .94
Then, approve
loan applicati
on
Otherwise, reject
loan applicati
on
Decision Tree: If/Then Decision Rules for a Loan Evaluation
Copyright © 2012 Pearson Education, Inc. Publishing as Prentice Hall 15-20
Otherwise, reject
loan applicati
on
Otherwise, reject
loan applicati
on
Otherwise, reject
loan applicati
on
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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mechanical, photocopying, recording, or otherwise, without the prior written permission of the publisher. Printed in the United States of America.
Copyright © 2012 Pearson Education, Inc. Publishing as Prentice Hall