discriminant analysis database marketing instructor:nanda kumar

Discriminant AnalysisDiscriminant Analysis

Database Marketing

Instructor:Nanda Kumar

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

Real Estate Example

How is price related to the characteristics of the house?

SAS Code

proc reg;

model price = section lotsize bed bath age other;

run;

Interpreting the Regression Output

Parameter Estimates or Slope Coefficients capture the marginal impact of explanatory variable on price

Example: the coefficient of the variable beds represents the impact of increasing the number of bedrooms by one on price

Significance of the Coefficients

Are they significantly different from zero?– Look at the T values and p values

• T value higher than 1.8 or p<0.05 good

• Sometimes p<0.10 is considered reasonably significant

Overall Goodness of Fit– Look at R2 (also refer to note in Session 1)

Where are we Now?

Behavior

Segment 1

Segment 2

Secondary

Data

Distinguishing

Characteristics Targeting

Factor Analysis Cluster

Analysis

Discriminant/Logit Analysis

Web Browsing

Identified two groups of consumers– One that visits your website frequently– One that doesn’t

Can the differences in behavior be related to socio-demographic variables?

Can we use these discriminators to classify prospects into one of these two groups?

Catalog Business

Identified two consumer segments– One which buys a lot – Other which does not buy as much

Can we find variables that help discriminate the behavior of these two groups?

Can we use these discriminators to classify other consumers into one of these two groups?

Promotional Campaigns

Identify groups based on their response to promotional campaigns– One group purchases a lot on promotion– Other does not

Identify characteristics that distinguish these two groups

Can we use these discriminators to identify price sensitive prospects from the not so price sensitive ones?

Segmentation Analysis

General Problem– Identified segments in the population based on

behavior

– Want to find targetable characteristics that discriminate these groups

– Classify prospects into different groups

DataStock # GE/A ROI Stock # GE/A ROI

1 0.158 0.182 13 -0.012 -0.0312 0.21 0.206 14 0.036 0.0533 0.207 0.188 15 0.038 0.0364 0.28 0.236 16 -0.063 -0.0745 0.197 0.193 17 -0.054 -0.1196 0.227 0.173 18 0 -0.0057 0.148 0.196 19 0.005 0.0398 0.254 0.212 20 0.091 0.1229 0.079 0.147 21 -0.036 -0.072

10 0.149 0.128 22 0.045 0.06411 0.2 0.15 23 -0.026 -0.02412 0.187 0.191 24 0.016 0.026

Good Stocks

Good Stocks

0

0.05

0.1

0.15

0.2

0.25

0 0.05 0.1 0.15 0.2 0.25 0.3

GE/A

RO

I

ROI

Bad Stocks

Bad Stocks

-0.15

-0.1

-0.05

0

0.05

0.1

0.15

-0.1 -0.05 0 0.05 0.1

GE/A

RO

I

ROI

All Stocks

All Stocks

-0.15

-0.1

-0.05

0

0.05

0.1

0.15

0.2

0.25

0.3

-0.1 0 0.1 0.2 0.3

GE/A

RO

I

Identifying the Best Discriminators

Two groups appear to be well separated on each ratio: ROI and GE/A

Also well separated in two dimensional space

But this need not always be the case!

Discriminating Variables

X1

X2

Discriminant Analysis

Identify a set of variables that best discriminate between the two groups

Does so by choosing a new line that maximizes the similarity between members of the same group and minimizing the similarity between members belonging to different groups

Discriminant Function

Z = w1 GEA + w2 ROI

Between-Group Sum of Squares – SSb

Within-Group Sum of Squares – SSw

= (SSb/SSw)

More on the Criterion

For Z to provide maximum separation between the groups, the following must be satisfied:– The means of Z for the two groups should be

as far apart as possible (or high SSb)

– Values of Z for each group should be as homogenous as possible (or low SSw)

Classification

Discriminant Function: The line that separates the members of the two groups

Methods of Classification– Cut-Off Value Method– Decision Theory Approach– Classification Function Approach– Mahalanobis Distance Method

Cut-Off Value Method

Uses the Discriminant Function line to score new observations (prospects) and classify them into one of two groups based on a cut-off value

Classification

Z

Cut-off Value

R2 R1

Classification Function Approach

Classifications based on this approach are identical to those done by Decision Theory approach

Classification functions are computed for each group:

C1 = -7.87 + 61.237*GEA + 21.027*ROI

C2 = -0.004 + 2.551*GEA – 1.404*ROI

Basic Idea

Score each new observation using these two scoring functions

The observation gets assigned to the group with the higher score

What To Look For In The Results?

Significance of the Discriminating Variables– Idea is to test whether the means of the

discriminating variables are statistically different across the two groups

– Statistic: Wilks’ Lamda must be small (Look for the p value/significance level)

Estimate of The Discriminant Function

Canonical Discriminant FunctionZ = -2.0018 + 15.0919*GEA + 5.769*ROI

It is possible that the group means are statistically different even though for all practical purposes, the differences between the groups may not be large

Look at the squared Canonical Correlation: ratio of between group SS/Total SS (High is good)

Importance of the Discriminant Variables and the Discriminant Function

How important is a variable to the Discriminant Function?

Look at the structure loadings: Pooled Within Canonical Structure– Variable with the higher loading is relatively more

important– Caution: If the variables are highly correlated relative

importance of the variables can change with sample

Classification Summary

Look at Cross-Validation results

Web Browsing

Can use the Discriminant function to classify prospects into one of these two groups

Target Appropriately

Catalog Business

Classify other consumers into one of these two groups

Do stuff!

Promotional Campaigns

Classify Prospects into price sensitive and not so price sensitive segments

Target appropriately

Summary

Discriminant Analysis Extremely Useful Segmentation Analysis

tool Intermediate step in the overall picture –

helps classify prospects and devise the appropriate targeting strategies