Session 1 Database Marketing. Agenda Administrative Course Work – Group/Individual Syllabus Course Overview Regression Analysis Introduction to SAS.

Session 1

Database Marketing

Agenda

Administrative Course Work – Group/Individual Syllabus

Course Overview Regression Analysis Introduction to SAS

Group Work

Two Assignments Part I (3%) – run the SAS code

Email the SAS output to the TA: [email protected] More details on how to save output later!

Part II (12%) – analyze the output Answer questions in the assignment based on the results

Term Project Part I (10%) – group presentation Part II (20%) – group report

Individual Work

Midterm Exam (40%) – tested on material covered in weeks 1-4

Peer Evaluation

No Final Exam!

What the Course does NOT cover?

Hardware/Software issues related to database management systems

Building a Marketing Database Neural Networks/Genetic Algorithms

What does the course cover?

Relationship between variables (Chapters 18, 19) Simple/Multiple Regression analysis

Segmentation Analysis (Chapter 21) Cluster Analysis Data reduction technique – Factor Analysis

Response Analysis (Chapters 20, 28, 29, 30) Regression Analysis Discriminant Analysis Logistic Regression

Database Marketing? Managing a computerized relational database system, in real

time, of comprehensive up-to-date, relevant data on customers,

inquiries, prospects and suspects, to identify our most

responsive customers for the purpose of developing a high

quality, long-standing relationship of repeat business by

developing predictive models which enable us to send desired

messages at the right time in the right form to the right

people – all with the result of pleasing our customers, increasing

our response rate per marketing dollar, lowering our cost per

order, building our business and increasing our profits. -

National Center for Database Marketing

What is the Big Deal?

All customers are not created equal! 80% of all repeat business of goods and

services comes from 20% of the customer base

Lot of direct marketing efforts are misdirected resulting in lower payoffs Credit card mailings

Direct Marketing vs Database Marketing

My view: Direct Marketing done well is Database

Marketing

Read Chapter 32

Market Research Techniques?

Will always continue to be helpful Earlier …

A mutual fund company could tell that 20% of 40-45 year old males making $50-$75K were interested in investing in mutual funds

But which 20%?

Now They can probably tell

Why?

Increased

Data

Availability!

Data Sources (chapter 4)

Transaction Data Easily available with increased use of

scanners Prospect Data

Either maintained or acquired Directly Supplied Data

Data that is provided directly by customers Data acquired from third-party

Data Sources …contd.

Directly Supplied Data Demographic Attitudinal – preferences, met/unmet needs,

lifestyle preferences, values, opinions etc. Behavioral – purchase/buying habits etc.

Traditionally acquired by conducting surveys Advances in the database technology,

precipitous drop in the price of computing/storage, rapid diffusion of the internet all aid the collection of this data

Where are We Now?

Firms have access to a reasonable amount of customer data

Certainly enough to enable them to do better job of marketing

More consumer data is certainly desirable

Catalog Marketing

JCPenney Mails out fliers frequently to inform customers

about ongoing promotions Mailings are not costless – firms operate under

budget constraints and would reasonably want to maximize the payoffs from such mailings!

What criteria should they use in mailing out these fliers?

Should they include a $10 coupon or $5 coupon?

Fund Raising

Non-Profit Organizations Solicit Donations – solicitation process

costly Who should they solicit? What amount should they solicit? Is the

amount solicited too high or too low?

Admission Process

Schools may wish to manage the matriculation process efficiently

Schools send out marketing materials to prospective students – some students may seek application material

Schools invest additional (costly) marketing efforts – are these even worthwhile if the student is not likely to matriculate?

Cross-Selling

Buy tickets for a Maverick’s game from the ticket master

Ticket master will attempt to sell you something by transferring you to a potential seller

Is there a more effective way?

Communication Strategies

BMW recently introduced an SUV to compete with the Mercedes’ M-series In which magazines or cable programs

should they advertise? What should the advertising copy

emphasize? ….. countless other applications !!

Economics of Database Marketing

Trade-offs Short-term costs vs long-term pay-offs Inter-relationship between costs and pay-

offs

Benefits of Segmentation

Big Picture Understand customer behavior Segment customers based on behavior Establish link between (possible)

behavior and identifiable (or targetable) characteristics of customers

Target using above

Hot Picture

Behavior

Segment 1

Segment 2

Secondary

Data

Distinguishing

Characteristics Targeting

Hotter Picture

Behavior

Segment 1

Segment 2

Secondary

Data

Distinguishing

Characteristics Targeting

Factor Analysis Cluster

Analysis

Discriminant/Logit Analysis

Putting things in Perspective

Core Marketing Concepts Segmentation Targeting Positioning

Agenda

Relationship between variables Review of Regression Analysis (chapter

18) Introduction to SAS programming

Data (chapter 18)

Catalog New Old

A 50 0

B 0 50

RelationshipP

erce

ntag

e bu

ying

A

New Old

Data

Catalog Old New

A 500 500

B 500 500

RelationshipP

erce

ntag

e bu

ying

A

New Old

Data

Catalog New Old

A 110,300 11,500

B 20,700 76,600

RelationshipP

erce

ntag

e bu

ying

A

New Old

Correlation Coefficient (r)

Statistical measure of the strength of relationship between two variables

r [-1,1] r [0,1] indicates a positive relationship r [-1,0] indicates a negative relationship

Know your Data

Sample should be representative of the population data

Reason why experts advocate the use of random samples

Regression Analysis

What does it do? Uncovers the relationship between a set of

variables

Simple Regression

y = f(x) Regression sets out to find the f(x) that

best fits the data

Assumptions:

f(x) is known up to some parameters So f(x) = a + bx Problem: Find a, b that best fit the data

An Example: Weight = a + b*Height

How does it Work?

Finds a, b that best fit the data Further assumptions:

Weight = a + b*Height + error Error is distributed normally: N(0, 2) Criteria – finds a, b that minimize the sum

of squared errors.

Picture

Return to Catalog Example

Hypothesis: Customers who purchase more

frequently also buy bigger ticket items

Data (Table 18-7, pg. 238)Number of Purchases (X)

Largest Dollar Item (Y)

1 2

2 3

3 10

4 15

5 26

6 35

7 50

Regression Model

Y = a + b X + error

Estimates: a = -18.22 b = 10 Goodness of Fit Measure: R2 = 0.946

Diagnostics

Linearity Assumption Y is linear in X – does this hold? If not transform the variables to ensure that

the linearity assumption holds Common Transforms: Log, Square-root,

Square etc.

Plot Y vs. X (r=0.97)

Y vs X

0102030405060

0 5 10

X

Y Series1

Plot Y1/2 vs. X (r=0.99)

Sqrt(Y) vs X

0

1

2

3

4

5

6

7

8

0 2 4 6 8

X

Sq

rt(Y

)

Series1

Regression Model

Y 1/2= a + b X + error

Estimates: a = 0.108845 b = 0.984 Goodness of Fit Measure: R2 = 0.9975

Obsession with R2

Can be a misleading statistic R2 can be increased by increasing the

number of explanatory variables R2 of a bad model can be higher than

that of a good model (one with better predictive validity)

Multiple Regression

Y = b0 + b1 X1 + b2 X2 + …+ bn Xn

Same as Simple Regression in principle

New Issues: Each Xi must represent something unique

Variable selection

Multiple Regression

Example 1: Spending = a + b income + c age

Example 2: weight = a + b height + c sex + d age

Introduction to SAS

SAS is an integrated system of software products that enables you to perform: Data entry, retrieval and management Statistical and Mathematical analysis Report writing Other stuff

SAS Dataset

variable

observation

DATA Step and SAS Procedures DATA step

Consists of a group of statements that read/manipulate raw data or operates on existing SAS data sets to create a SAS data set

SAS Procedures Work with SAS data sets to help in data

management, statistical analysis etc.

How to Read a raw data (ASCII) file

libname mylib “some directory name”;data mylib.dat1;infile “name of the file” linesize=100 missover;length charvar1 $ 20;length charvar2 $ 5;input numvar1 numvar2 charvar1 $ numvar3 charvar2

$;

You have just read a raw data file with 3 numeric and 2 character/string variables into a SAS data set called dat1

Transforming Variables

newvar = log(oldvar); natural log

newvar = sqrt(oldvar); square root

newvar = oldvar**2; square

Standard Arithmetic Operators

Data Management

Sorting a data setProc sort data=mydata; by <descending> varname;

Merging data setsProc sort data=mydata1; by <descending> varname;

Proc sort data= mydata2; by <descending> varname;

Data targetdataset;

Merge mydata1(in=g) mydata2; by varname;

If g;

Statistical Procedures

Proc Reg data=indata <options>;

Model y = x1 x2 x3;

Proc Factor data= indata <options>;

Var x1 x2 x3;

Proc Cluster data= indata <options>;

Var x1 x2 x3;

SAS Exercises

Read in an Excel Sheet Create some data in excel (you may wish to

replicate the data we used in our regression example)

Use the import feature in SAS to read in the file

Write a regression program to: Estimate the relationship between Y and X Sqrt(Y) and X Y and X2