t20 cluster analysis

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  • 1. Cluster AnalysisBy Rama Krishna Kompella

2. Introduction In order to go from data to information, toknowledge and to wisdom, we need toreduce the complexity of the data. Complexity can be reduced on case level : cluster analysis on variable level: factor analysis 3. Cluster Anlaysis Cluster analysis is another interdependence multivariatemethod As the name implies, the basic purpose of clusteranalysis is to classify or segment objects (e.g., customers,products, market areas) into groups so that objectswithin each group are similar to one another on a varietyof variables. Cluster analysis seeks to classify segments or objectssuch that there will be as much likeness within segmentsand as much difference between segments as possible. 4. What cluster analysis does Cluster analysis generate groups which are similar homogeneous within the group and as much aspossible heterogeneous to other groups data consists usually of objects or persons segmentation based on more than two variables 5. Examples for datasets used for clusteranalysis: socio-economic criteria: income, education,profession, age, number of children, size of city ofresidence .... psychographic criteria: interest, life style,motivation, values, involvement criteria linked to the buying behaviour: pricerange, type of media used, intensity of use, choiceof retail outlet, fidelity, buyer/non-buyer, buyingintensity 6. Proximity Measures Proximity measures are used to represent thenearness of two objects relate objects with a high similarity to the samecluster and objects with low similarity to differentclusters differentiation of nominal-scaled and metric-scaled variables the calculation of the distances measures is thebasis of the cluster analysis 7. Two phases: Forming of clusters by the chosen data set resulting in a new variable that identifies clustermembers among the cases Description of clusters by re-crossing with thedata 8. Cluster Algorithm in agglomerative hierarchical clustering methods seven steps to get clusters2. each object is a independent cluster, n3. two clusters with the lowest distance are merged to one cluster. reduce the number of clusters by 1 (n-1)4. calculate the distance matrix between the new cluster and all remaining clusters5. repeat step 2 and 3, (n-1) times until all objects form one reminding cluster 9. Finally2.decide upon the number of clusters you wantto keep (decision often based on the size ofthe clusters)3.description of the clusters by means of thecluster forming variables4.appellation of the clusters with catchy titles 10. Consumers and Fair Trade Coffee 214 interviews of consumers of fair trade coffee(personal and telephone interviews) Cluster analysis in order to identify consumertypologies Identification of 6 clusters Description of these clusters by further analysis:comparison of means, crosstabs etc. 11. Description of Cluster 1 (11,6%): self-oriented fairtrade buyer : Searches satisfaction by doing the good thing Is not altruistic Buys occasionally Sticks to his conventional coffee brand High level of formal education Frequently religious (catholic or protestant) 12. Description of Cluster 2 (13,6%): less ready to takepersonal constraints States that fair trade coffee is hard to find Feels responsible for fare development issues Believes that fair trade is efficient for developingcountries Is less ready to go to special fair trade outlets Buys conventional coffee Likes the taste of fair trade coffee 13. Description of clusters Cluster 3 (18,2%): lessengaged about fair trade : Feels no personal responsibility with regard todevelopment questions Doesnt see the efficiency of the consumption of fairtrade goods The only thing that can make him change is theinfluence of friends Is older then the average fair trade buyer and hasless formal education 14. Description of clusters: Cluster 4 (32,2%): intensivebuyer Has abandoned conventional coffee brands Has started to buy fair trade quite a while ago (> 3 years) Shops frequently in fair trade stores (and not in organicretail) Is ready to act for fair development and talks to friendsabout it Relatively young, with low incomes and high educationalvalues 15. Description of clusters: Cluster 5 (18,7%):valueoriented Together with cluster 4 highly aware of developmentissues Ready to act and to constraint consumption habits Buys for altruistic reasons Highly involved in social / political action Most frequently women, highest household incomeamong all clusters Own security is the basis for solidary action 16. Description of clusters: Cluster 6 (5,6%): does not likethe taste of fair trade coffee Lowest purchase intensity of all clusters Not willing to accept constraints in consumption habitsor higher prices Most members of these group are attached to aconventional coffee brand Relatively high incomes, age within the average of allgroups, lower level of formal education Less religious than other groups. 17. Cluster Analysis Applications inMarketing Research New-product research: Clustering brands can help a firm examine its product offerings relative to competition. Brands in the same cluster often compete more fiercely with each other than with brands in other clusters. Test marketing: Cluster analysis groups test cities into homogeneous clusters for test marketing purposes. 18. Cluster Analysis Applications inMarketing Research Buyer behavior: Cluster analysis can be employed to identify similar groups of buyers who have similar choice criteria. Market segmentation: Cluster analysis can develop distinct market segments on the basis of geographic, demographic, psychographic, and behavioristic variables. 19. Advantages no special scales of measurement necessary high persuasiveness and good assignment to realisablerecommendations in practiceDisadvantages choice of cluster-forming variables often not based ontheory but at random determination of the right number of clusters often timeconsuming often decided upon arbitrarily high influence on the interpretation of the scientist,difficult to control (good documentation is needed) 20. Questions?