Chapter Extension 15 Database Marketing. Q1:What is a database marketing opportunity? Q2: How does RFM analysis classify customers? Q3: How does market-basket

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 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?


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


• 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?


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


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


• 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)


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?


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


• 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


• 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


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?


• Figure CE15-3

A Decision Tree for Student Performance


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


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


Active Review


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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Chapter Extension 15 Database Marketing. Q1:What is a database marketing opportunity? Q2: How does RFM analysis classify customers? Q3: How does market-basket