1. Chapter XX Cluster Analysis

2. Chapter Outline Chapter Outline 1) Overview 2) Basic Concept 3) Statistics Associated with Cluster Analysis 4) Conducting Cluster Analysis i. Formulating the Problem i. Formulating the Problem ii. Selecting a Distance or Similarity Measure ii. Selecting a Distance or Similarity Measure iii. Selecting a Clustering Procedure iii. Selecting a Clustering Procedure iv. Deciding on the Number of Clusters iv. Deciding on the Number of Clusters v. Interpreting and Profiling the Clusters v. Interpreting and Profiling the Clusters vi. Assessing Reliability and Validity vi. Assessing Reliability and Validity

3. 5) Applications of Nonhierarchical Clustering 5) Applications of Nonhierarchical Clustering 6) Clustering Variables 6) Clustering Variables 7) Internet & Computer Applications 7) Internet & Computer Applications 8) Focus on Burke 8) Focus on Burke 9) Summary 9) Summary 10) Key Terms and Concepts 11) Acronyms

4. An Ideal Clustering Situation Figure 20.1 Variable 2 Variable 1

5. X A Practical Clustering Situation Figure 20.2 Variable 2 Variable 1

6. 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 Assess the Validity of Clustering

7. Case No.V 1 V 2 V 3 V 4 V 5 V 6 1647323 2231454 3726413 4464536 5132264 6646334 7536334 8737414 9243363 10353646 11132353 12545424 13221544 14464647 15654214 16354647 17447225 18372643 19463727 20232472 Attitudinal Data For Clustering Table 20.1

8. 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 Single Complete Average

9. Linkage Methods of Clustering Figure 20.5 Single Linkage Minimum Distance Complete Linkage Maximum Distance Average Linkage Average Distance Cluster 1Cluster 2 Cluster 1Cluster 2 Cluster 1Cluster 2

10. Other Agglomerative Clustering Methods Fig. 20.6 Ward’s Procedure Centroid Method

11. Vertical Icicle Plot Using Ward’s Method Fig. 20.7 1 11211 11 111 8+ 1+ 4+ 5+ 6+ 7+ 2+ 3+ 11+ 12+ 13+ 14+ 9+ 10+ 16+ 19+ 17+ 18+ 15+ 9 8 404096 328315 7 627 5 1 Case Label and Number Number of Clusters

12. Results of Hierarchical Clustering Table 20.2 Stage cluster Stage cluster Clusters combined first appears StageCluster 1Cluster 2 Coefficient Cluster 1 Cluster 2 Next stage 11416 1.000000 0 0 7 2 213 2.500000 0 0 15 3 712 4.000000 0 0 10 4 511 5.500000 0 0 11 5 3 8 7.000000 0 0 16 6 1 6 8.500000 0 0 10 71014 10.166667 0 1 9 8 920 12.666667 0 0 11 9 410 15.250000 0 7 12 10 1 7 18.250000 6 3 13 11 5 9 22.750000 4 8 15 12 419 27.500000 9 0 17 13 117 32.70000110 0 14 14 115 40.50000013 0 16 15 2 5 51.000000 211 18 16 1 3 63.12500014 5 19 17 418 78.29166412 0 18 18 2 4171.2916561517 19 19 1 2330.4500121618 0 Agglomeration Schedule Using Ward’s Procedure

13. Number of Clusters Label case432 1111 2222 3111 4332 5222 6111 7111 8111 9222 10332 11222 12111 13222 14332 15111 16332 17111 18432 19332 20222 Cluster Membership of Cases Using Ward’s Procedure Table 20.2 Contd.

14. 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 152025510 Case Label Seq Rescaled Distance Cluster Combine

15. Means of Variables Cluster No.V 1 V 2 V 3 V 4 V 5 V 6 15.7503.6256.0003.1251.7503.875 21.6673.0001.8333.5005.5003.333 33.5005.8333.3336.0003.5006.000 Cluster Centroids Table 20.3

16. ClusterV1V2V3V4V5V6 14.00006.00003.00007.00002.00007.0000 22.00003.00002.00004.00007.00002.0000 37.00002.00006.00004.00001.00003.0000 Initial Cluster Centers Results of Nonhierarchical Clustering Table 20.4 Classification Cluster Centers ClusterV1V2V3V4V5V6 13.81355.89923.25226.48912.51496.6957 21.85073.02341.83273.78646.44362.5056 36.35582.83566.15763.67361.30473.2010 Case Listing of Cluster Membership Case IDClusterDistanceCase IDClusterDistance 131.780222.254 331.174411.882 522.525632.340 731.862831.410 921.8431012.112 1121.9231232.400 1323.3821411.772 1533.6051612.137 1733.7601814.421 1910.8532020.813

17. Final Cluster Centers Table 20.4 contd. ClusterV1V2V3V4V5V6 13.50005.83333.33336.00003.50006.0000 21.66673.00001.83333.50005.50003.3333 35.75003.62506.00003.12501.75003.8750 Distances between Final Cluster Centers Cluster 1 2 3 10.0000 25.56780.0000 35.73536.99440.0000 Analysis of Variance Variable Cluster MS df Error MS df F p V1 29.108320.6078 17 47.8879.000 V2 13.545820.6299 17 21.5047.000 V3 31.391720.8333 17 37.6700.000 V4 15.712520.7279 17 21.5848.000 V5 24.1500 20.7353 17 32.8440.000 V6 12.170821.0711 17 11.3632.001 Number of Cases in each Cluster ClusterUnweighted Cases Weighted Cases 1 66 2 66 3 88 Missing 0 Total 20 20

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 patterns of parity perceptions.

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 is of course the emergence of the global market.

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? RIP 20.2 Contd.