chapter20.ppt 614KB Mar 03 2002 08:06:10 PM

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Chapter XX

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Chapter Outline

1) Overview

2) Basic Concept

3) Statistics Associated with Cluster Analysis

4) Conducting Cluster Analysis

i. Formulating the Problem

ii. Selecting a Distance or Similarity Measure

iii. Selecting a Clustering Procedure

iv. Deciding on the Number of Clusters

v. Interpreting and Profiling the Clusters

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5) Applications of Nonhierarchical Clustering

6) Clustering Variables

7) Internet & Computer Applications

8) Focus on Burke

9) Summary

10) Key Terms and Concepts

11) Acronyms

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[image:4.720.135.516.124.417.2]

An Ideal Clustering Situation

Figure 20.1

Variable 2

V

ar

ia

b

le

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X

A Practical Clustering Situation

[image:5.720.194.513.188.411.2]

A Practical Clustering Situation

Figure 20.2

Variable 2

V

ar

ia

b

le

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[image:6.720.33.592.80.469.2]

Conducting Cluster Analysis

Fig. 20.3

Select a Distance Measure

Formulate the Problem

Select a Clustering Procedure

Decide on the Number of Clusters

Interpret and Profile Clusters

(7)

Case No.

V

₁

V

₂

V

₃

V

₄

V

₅

V

₆

1

6

4

7

3

2

3

2

3

1

4

5

4

3

7

2

6

4

1

3

4

6

4

5

3

6

5

1

3

2

6

4

6

4

6

3

4

7

5

3

6

3

4

8

7

3

7

4

1

4

9

2

4

3

6

3

10

3

5

3

6

4

6

11

1

3

2

3

5

3

12

5

4

5

4

2

4

13

2

1

5

4

14

4

6

4

6

4

7

15

6

5

4

2

1

4

16

3

5

4

6

4

7

17

4

7

2

5

18

3

7

2

6

4

3

19

4

6

3

7

2

7

20

2

3

2

4

7

2

[image:7.720.35.720.41.523.2]

Attitudinal Data For Clustering

Table 20.1

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[image:8.720.24.685.73.527.2]

Fig. 20.4

Clustering Procedures

A Classification of Clustering Procedures

Hierarchical

Nonhierarchical

Agglomerative

Divisive

Sequential

Threshold

Parallel

Threshold

Optimizing

Partitioning

Linkage

Methods

Variance

Methods

Centroid

Methods

Ward’s Method

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[image:9.720.136.593.51.523.2]

Linkage Methods of Clustering

Figure 20.5

Single Linkage

Minimum Distance

Complete Linkage

Maximum Distance

Average Linkage

Average Distance

Cluster 1 Cluster 2

(10)

Other Agglomerative Clustering Methods

Fig. 20.6

Ward’s Procedure

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[image:11.720.21.701.34.525.2]

Vertical Icicle Plot Using Ward’s Method

Fig. 20.7

1 1 1 1 1 2 1 1 1 1 1

8+ 1+ 4+ 5+ 6+ 7+ 2+ 3+ 11+ 12+ 13+ 14+ 9+ 10+ 16+ 19+ 17+ 18+ 15+ 9

8 9 6 4 0 4 0 1 5 3 2 8 3 5 7 2 7 6 1

Case Label and Number

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[image:12.720.26.388.73.521.2]

Results of Hierarchical Clustering

Table 20.2

Stage cluster

Clusters combined

first appears

Stage

Stage Cluster 1Cluster 1Cluster 2Cluster 2 Coefficient Cluster 1 Cluster 2 Next stage Coefficient Cluster 1 Cluster 2 Next stage 1

1 1414 1616 1.000000 1.000000 0 0 0 0 7 7 2

2 2 2 1313 2.500000 2.500000 0 0 0 0 15 15 3

3 7 7 1212 4.000000 4.000000 0 0 0 0 10 10 4

4 5 5 1111 5.500000 5.500000 0 0 0 0 11 11 5

5 3 3 8 8 7.000000 7.000000 0 0 0 0 16 16 6

6 1 1 6 6 8.500000 8.500000 0 0 0 0 10 10 7

7 1010 1414 10.166667 10.166667 0 0 1 1 9 9 8

8 9 9 2020 12.666667 12.666667 0 0 0 0 11 11 9

9 4 4 1010 15.250000 15.250000 0 0 7 7 12 12 10

10 1 1 7 7 18.250000 18.250000 6 6 3 3 13 13 11

11 5 5 9 9 22.750000 22.750000 4 4 8 8 15 15 12

12 4 4 1919 27.500000 27.500000 9 9 0 0 17 17 13

13 1 1 1717 32.700001 32.700001 1010 0 0 14 14 14

14 1 1 1515 40.500000 40.500000 1313 0 0 16 16 15

15 2 2 5 5 51.000000 51.000000 2 2 1111 18 18 16

16 1 1 3 3 63.125000 63.125000 1414 5 5 19 19 17

17 4 4 1818 78.291664 78.291664 1212 0 0 18 18 18

18 2 2 4 4 171.291656171.291656 1515 1717 19 19 19

19 1 1 2 2 330.450012330.450012 1616 1818 0 0

Agglomeration Schedule Using Ward’s Procedure

(13)

Number of Clusters

Label case

Label case 44 33 22 1

1 11 11 11

2

2 22 22 22

3

3 11 11 11

4

4 33 33 22

5

5 22 22 22

6

6 11 11 11

7

7 11 11 11

8

8 11 11 11

9

9 22 22 22

10

10 33 33 22

11

11 22 22 22

12

12 11 11 11

13

13 22 22 22

14

14 33 33 22

15

15 11 11 11

16

16 33 33 22

17

17 11 11 11

18

18 44 33 22

19

19 33 33 22

20

20 22 22 22

Cluster Membership of Cases Using Ward’s Procedure

[image:13.720.197.648.85.501.2]

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Dandogram Using Ward’s Method

[image:14.720.92.486.28.526.2]

Dandogram Using Ward’s Method

Fig. 20.8

3

15 1 12 7 8

17 6 11 5 13 2

20 9 19 16

4 10

18 14

0 5 10 15 20 25

Case Label Seq

(15)

Means of Variables

Cluster No.

V

₁

V

₂

V

₃

V

₄

V

₅

V

₆

1 5.750 3.625 6.000 3.125 1.750 3.875

2 1.667 3.000 1.833 3.500 5.500 3.333

3 3.500 5.833 3.333 6.000 3.500 6.000

Cluster

[image:15.720.36.714.79.502.2]

Cluster

Centroids

Table 20.3

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Cluster

Cluster V1V1 V2V2 V3V3 V4V4 V5V5 V6V6 1

1 4.00004.0000 6.00006.0000 3.00003.0000 7.00007.0000 2.00002.0000 7.00007.0000 2

2 2.00002.0000 3.00003.0000 2.00002.0000 4.00004.0000 7.00007.0000 2.00002.0000 3

3 7.00007.0000 2.00002.0000 6.00006.0000 4.00004.0000 1.00001.0000 3.00003.0000

Initial Cluster Centers

[image:16.720.12.604.68.531.2]

Results of Nonhierarchical Clustering

Table 20.4

Classification Cluster Centers

Cluster

1 3.81353.8135 5.89925.8992 3.25223.2522 6.48916.4891 2.51492.5149 6.69576.6957 2

2 1.85071.8507 3.02343.0234 1.83271.8327 3.78643.7864 6.44366.4436 2.50562.5056 3

3 6.35586.3558 2.83562.8356 6.15766.1576 3.67363.6736 1.30471.3047 3.20103.2010

Case Listing of Cluster Membership

Case ID

Case ID ClusterCluster DistanceDistance Case IDCase ID ClusterCluster DistanceDistance 1

1 33 1.7801.780 22 22 2.2542.254 3

3 33 1.1741.174 44 11 1.8821.882 5

5 22 2.5252.525 66 33 2.3402.340 7

7 33 1.8621.862 88 33 1.4101.410 9

9 22 1.8431.843 1010 11 2.1122.112 11

11 22 1.9231.923 1212 33 2.4002.400 13

13 22 3.3823.382 1414 11 1.7721.772 15

15 33 3.6053.605 1616 11 2.1372.137 17

17 33 3.7603.760 1818 11 4.4214.421 19

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[image:17.720.28.614.51.521.2]

Final Cluster Centers

Table 20.4 contd.

Table 20.4 contd.

Cluster

1 3.50003.5000 5.83335.8333 3.33333.3333 6.00006.0000 3.50003.5000 6.00006.0000 2

2 1.66671.6667 3.00003.0000 1.83331.8333 3.50003.5000 5.50005.5000 3.33333.3333 3

3 5.75005.7500 3.62503.6250 6.00006.0000 3.12503.1250 1.75001.7500 3.87503.8750

Distances between Final Cluster Centers

Cluster

Cluster 1 1 2 2 3 3 1

1 0.00000.0000 2

2 5.56785.5678 0.00000.0000 3

3 5.73535.7353 6.99446.9944 0.00000.0000

Analysis of Variance

Variable

Variable Cluster MS df Error MS df F p Cluster MS df Error MS df F p V1

V1 29.1083 29.1083 22 0.60780.6078 17 47.8879 .000 17 47.8879 .000 V2

V2 13.5458 13.5458 22 0.62990.6299 17 21.5047 .000 17 21.5047 .000 V3

V3 31.3917 31.3917 22 0.83330.8333 17 37.6700 .000 17 37.6700 .000 V4

V4 15.7125 15.7125 22 0.72790.7279 17 21.5848 .000 17 21.5848 .000 V5

V5 24.1500 24.1500 22 0.73530.7353 17 32.8440 .000 17 32.8440 .000 V6

V6 12.1708 12.1708 22 1.07111.0711 17 11.3632 .001 17 11.3632 .001

Number of Cases in each Cluster

Cluster

Cluster Unweighted Cases Unweighted Cases Weighted Cases Weighted Cases 1

1 6 6 66

2

2 6 6 66

3

3 8 8 88

Missing

Missing 0 0

Total

(18)

How do consumers in different countries perceive brands in

different product categories? Surprisingly, the answer is that the

product perception parity rate is quite high. Perceived product

parity means that consumers perceive all/most of the brands in a

product category as similar to each other or at par. A new study

by BBDO Worldwide shows that two-thirds of consumers

surveyed in 28 countries considered brands in 13 product

categories to be at parity. The product categories ranged from

airlines to credit cards to coffee.

Perceived Product Parity - Once

Rarity - Now Reality

RIP 20.1

(19)

Perceived parity averaged 63% for all

categories in all countries. The Japanese

have the highest perception of parity

across all product categories at 99% and

Colombians the lowest at 28%. Viewed by

product category, credit cards have the

highest parity perception at 76% and

cigarettes the lowest at 52%.

BBDO clustered the countries based on

product parity perceptions to arrive at

clusters that exhibited similar levels and

(20)

The highest perception parity figure came from Asia/Pacific region

(83%) which included countries of Australia, Japan, Malaysia, and

South Korea, and also France. It is no surprise that France was in this

list since for most products they use highly emotional, visual

advertising that is feelings oriented. The next cluster was

U.S.-influenced markets (65%) which included Argentina, Canada, Hong

Kong, Kuwait, Mexico, Singapore, and the U.S. The third cluster,

primarily European countries (60%) included Austria, Belgium,

Denmark, Italy, the Netherlands, South Africa, Spain, the U.K., and

Germany.

RIP 20.1 Contd.

(21)

What all this means is that in order to

differentiate the product/brand,

advertising can not just focus on product

performance, but also must relate the

product to the person's life in an

important way. Also, much greater

marketing effort will be required in the

Asia/Pacific region and in France in

order to differentiate the brand from

competition and establish a unique

image. A big factor in this growing parity

(22)

Cluster analysis can be used to explain differences in ethical

perceptions by using a large multi-item, multi-dimensional scale

developed to measure how ethical different situations are. One

such scale was developed by Reidenbach and Robin. This scale

has 29 items which compose five dimensions that measure how a

respondent judges a certain action. For example, a given

respondent will read about a marketing researcher that has

provided proprietary information of one of his clients to a second

client. The respondent is then asked complete the 29 item ethics

scale. For example, to indicate

if this action is:

Just :___:___:___:___:___:___:___: Unjust

Traditionally :___:___:___:___:___:___:___: Unacceptable

acceptable

Violates :___:___:___:___:___:___:___: Does not violate an

unwritten contract

Clustering Marketing Professionals

Based on Ethical Evaluations

RIP 20.2

(23)

This scale could be administered to a sample of marketing

professionals. By clustering respondents based on these 29 items,

two important questions should be investigated. First, how do the

clusters differ with respect to the five ethical dimensions; in this

case, Justice, Relativist, Egoism, Utilitarianism, Deontology (see

Chapter 24). Second, what types of firms compose each cluster?

The clusters could be described in terms of industry classification

(SIC), firm size, and firm profitability. Answers to these two

questions should provide insight into what type of firms use what

dimensions to evaluate ethical situations. For instance, do large

firms fall in to a different cluster than small firms? Do more

profitable firms perceive questionable situations more acceptable

than less-profitable firms?

chapter20.ppt 614KB Mar 03 2002 08:06:10 PM

Chapter XX

ii. Selecting a Distance or Similarity Measure

5) Applications of Nonhierarchical Clustering

Figure 20.1

Interpret and Profile Clusters

Methods

Other Agglomerative Clustering Methods

Table 20.2

Dandogram Using Ward’s Method

Case Label Seq

Means of Variables

Case Listing of Cluster Membership

Table 20.4 contd.

Distances between Final Cluster Centers

How do consumers in different countries perceive brands in

by BBDO Worldwide shows that two-thirds of consumers

have the highest perception of parity

clusters that exhibited similar levels and

advertising that is feelings oriented. The next cluster was

What all this means is that in order to

order to differentiate the brand from

such scale was developed by Reidenbach and Robin. This scale

client. The respondent is then asked complete the 29 item ethics

two important questions should be investigated. First, how do the

(SIC), firm size, and firm profitability. Answers to these two

RIP 20.2 Contd.