2. Agenda 1.Housekeeping: readings, team name & e-e-mail, etc. 2.News 1.Surveillance data miningSurveillance data mining 2.Shopping basket analysisShopping basket analysis 3.Segmentation marketing 1.Basics of segmentation 2.Geographics 3.Demographics 4.Lifecycle 5.Cohorts 6.Psychographics & behavior 4.Team discussion questions 1.Profile yourself as consumer 2.Profile your customers: How does this enable you to respond to their needs better 5.Next week: consumer behavior– why we buy what we buy

3. Use the various consumer profiling methods to: 1.Profile yourself as a consumer (use VALS-2, Prism, and other demographic, psychographic, and lifestyle descriptors). 2.What are the implications for marketers (e.g., how is this reflected in how they do/can market to you more effectively)? 3.Profile the customers in your business (or department). 4.How does this information about your customers enable you to provide better products/services to them? 5.What more do you need to know? How could you find out? Group discussion questions for tonight

4. You might understand the parts, but might miss the whole chicken What is ?

5. Demographic/Geographic refers to age, sex, income, education, race, martial status, size of household, geographic location, size of city, and profession. Life stage refers to chronological benchmarking of people's lives at different ages (e.g., pre-teens, teenagers, empty-nesters, etc.). Lifestyle refers to the collective choice of hobbies, recreational pursuits, entertainment, vacations, and other non-work time pursuits Psychographics refers to personality and emotionally based behavior linked to purchase choices; for example, whether customers are risk- takers or risk-avoiders, impulsive buyers, etc. Belief and value systems includes religious, political, nationalistic, and cultural beliefs and values. Behavior analysis includes what behaviors consumers actually engage in (after all is said and done) Methods of Seg-men-ta-tion

7. Requirements for segmentation Question: What are some criteria that could be used to ensure that a segmentation has utility? Identifiable : the differentiating attributes of the segments must be measurable so that they can be identified. Relevant/Accessible : the segments must be reachable through communication and distribution channels. Substantial: the segments should be sufficiently large to justify the resources required to target them. Unique needs : to justify separate offerings, the segments must respond differently to the different marketing mixes. Durable : the segments should be relatively stable to minimize the cost of frequent changes.

8. Pitfalls of Segmentation appeal to segments that are too small misread consumer similarities and differences become cost inefficient spin off too many imitations of their original products or brands become short-run rather than long-run oriented unable to use certain media (due to small segment size) compete in too many markets confuse people become locked in to a declining market too slow to seek innovation possibilities for new products

10. Demographic Profile

12. Business segmentation can help companies align their sales territories based on the opportunities on the ground. The BEFOREmap shows territories determine by geometry—four quadrants dividing the central area— while the AFTERmap shows territories that vary in size based on the number and potential value of target businesses (the red dots indicating the locations of target businesses). By mapping its business prospects by size and industry type in Lexington, Kentucky, a company can better realign its sales territories based on the concentrations of its high- quality prospects.

16. In online communities, who are the influencers? Social Network Analysis The Hypernetworked World

17. Profile of Motor Boat Owner Segmentation

18. Chapter Twenty-one 18 Factor and Cluster Analysis

19. Factor Analysis Combines questions or variables to create new factors Combines objects to create new groups Uses in Data Analysis ▫To identify underlying constructs in the data from the groupings of variables that emerge ▫To reduce the number of variables to a more manageable set 19

20. Factor Analysis (Contd.) Methodology Principal Component Analysis ▫Summarizes information in a larger set of variables to a smaller set of factors Common Factor Analysis ▫Uncovers underlying dimensions surrounding the original variables 20

21. Factor Analysis - Example 21

22. Export Data Set - Illustration 22 RespidWill(y1)Govt(y2)Train(x5)Size(x1)Exp(x6)Rev(x2)Years(x3) Prod(x4) 145149110005.56 234146110006.54 354154110006.07 423131030006.05 543150120006.57 654169110005.59......... 11543145120006.06 116 5 4 1 44 1 2000 5.811 11734146010007.03 11834154110007.04 11943149110006.57 12045154140006.57

23. Description of Variables 23 Variable DescriptionCorresponding Name in Output Scale Values Willingness to Export (Y 1) Will1(definitely not interested) to 5 (definitely interested) Level of Interest in Seeking Govt Assistance (Y 2 ) Govt1(definitely not interested) to 5 (definitely interested) Employee Size (X 1 )SizeGreater than Zero Firm Revenue (X 2 )RevIn millions of dollars Years of Operation in the Domestic Market (X 3 ) YearsActual number of years Number of Products Currently Produced by the Firm (X 4 ) ProdActual number Training of Employees (X 5 )Train0 (no formal program) or 1 (existence of a formal program) Management Experience in International Operation (X 6) Exp0 (no experience) or 1 (presence of experience)

24. Factors Factor ▫A variable or construct that is not directly observable but needs to be inferred from the input variables ▫All included factors (prior to rotation) must explain at least as much variance as an “average variable” Eigenvalue Criteria ▫Represents the amount of variance in the original variables that is associated with a factor ▫Sum of the square of the factor loadings of each variable on a factor represents the eigenvalue ▫Only factors with eigenvalues greater than 1.0 are retained 24

25. How Many Factors - Criteria Scree Plot Criteria ▫A plot of the eigenvalues against the number of factors, in order of extraction. ▫The shape of the plot determines the number of factors 25

26. How Many Factors: Criteria (Contd.) Percentage of Variance Criteria ▫The number of factors extracted is determined so that the cumulative percentage of variance extracted by the factors reaches a satisfactory level Significance Test Criteria ▫Statistical significance of the separate eigenvalues is determined, and only those factors that are statistically significant are retained 26

27. Extraction using Principal Component Method - Unrotated 27 Factor Loadings Factor Score Coefficient

28. Extraction using Principal Component Method - Factor Rotation 28 Not significantly different from unrotated values

29. Common Factor Analysis ▫The factor extraction procedure is similar to that of principal component analysis except for the input correlation matrix ▫Communalities or shared variance is inserted in the diagonal instead of unities in the original variable correlation matrix ▫The total amount of variance that can be explained by all the factors in common factor analysis is the sum of the diagonal elements in the correlation matrix ▫The output of common factor analysis depends on the amount of shared variance 29

30. Common Factor Analysis – Results (Contd.) 30

31. Common Factor Analysis - Results 31

32. Common Factor Analysis – Results (Contd.) 32

33. Cluster Analysis Technique for grouping individuals or objects into unknown groups. The typical criterion used in cluster analysis is distance between clusters or the error sum of squares. The input is any valid measure of similarity between objects, such as: ▫Correlations ▫Distance measures (Euclidean distance) ▫Association coefficients ▫The number of clusters or the level of clustering 33

34. Steps in Cluster Analysis Define the problem Decide on the appropriate similarity measure Decide on how to group the objects Decide the number of clusters Interpret, describe, and validate the clusters 34

35. Cluster Analysis (Contd.) Hierarchical Clustering ▫Can start with all objects in one cluster and divide and subdivide them until all objects are in their own single-object cluster ( ‘top-down’ or decision approach) ▫Can start with each object in its own single-object cluster and systematically combine clusters until all objects are in one cluster (‘bottom-up’ or agglomerative approach) Non-hierarchical Clustering ▫Permits objects to leave one cluster and join another as clusters are being formed ▫A cluster center is initially selected and all the objects within a pre-specified threshold distance are included in that cluster 35

36. Hierarchical Clustering Single Linkage ▫Clustering criterion based on the shortest distance Complete Linkage ▫Clustering criterion based on the longest distance 36

37. Hierarchical Clustering (Contd.) Average Linkage ▫Clustering criterion based on the average distance Ward's Method ▫Based on the loss of information resulting from grouping of the objects into clusters (minimize within cluster variation) 37

38. Hierarchical Clustering (Contd.) Centroid Method ▫Based on the distance between the group centroids (the point whose coordinates are the means of all the observations in the cluster) 38

39. Hierarchical Cluster Analysis - Example 39

40. Hierarchical Cluster Analysis (Contd.) 40 A dendrogram for hierarchical clustering of bank data

41. Hierarchical Cluster Analysis (Contd.) 41

42. ▫ Number of clusters is specified by the analyst for theoretical or practical reasons. ▫ Level of clustering with respect to clustering criterion is specified. ▫ Determine the number of clusters from the pattern of clusters generated. The distances between clusters or error variability measure at successive steps can be used to decide the number of clusters (from the plot of error sum of squares with the number of clusters). ▫ The ratio of total within-group variance to between group variance is plotted against the number of clusters and the point at which an elbow occurs indicates the number of clusters. 42

43.  Assumptions ◦ The basic measure of similarity on which the clustering is based is a valid measure of the similarity between the objects. ◦ There is theoretical justification for structuring the objects into clusters  Limitations ◦ It is difficult to evaluate the quality of the clustering ◦ It is difficult to know exactly which clusters are very similar and which objects are difficult to assign. ◦ It is difficult to select a clustering criterion and program on any basis other than availability. 43