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Segmentation

Segmentation. Mersiha Spahic Urban Studies and Planning Portland State University. Agenda. Segmentation approaches Cluster Analysis Basics Limitations of Cluster Analysis Segmentation Study-approach & findings Conclusions. Segmentation Approaches.

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Segmentation

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  1. Segmentation Mersiha Spahic Urban Studies and Planning Portland State University

  2. Agenda • Segmentation approaches • Cluster Analysis Basics • Limitations of Cluster Analysis • Segmentation Study-approach & findings • Conclusions

  3. Segmentation Approaches • Psychographic (e.g. Values, Attitudes & Lifestyle Segmentation (VALS) -see http://www.strategicbusinessinsights.com/) • Behavioral (e.g. based on purchasing decisions) Statistical techniques used for segmentation: cluster analysis, Latent Class Analysis (LCA), factor analysis, tree-based segmentation, etc.

  4. Cluster Analysis-Basics • Cluster formation – In agglomerative hierarchical clustering two cases with highest similarity are combined into cluster. Then third case with highest similarity to either the first two cases is considered next, and if it is closer to fourth case than the first two cases, the third and fourth cases become a cluster. The process is repeated until all cases are evaluated. Divisive clustering works in the opposite direction, where you start with all cases in one large cluster, and then begin to assign cases into other clusters based on similarity or distance. Non-hierarchical clustering involves moving cases iteratively from one cluster to another from an initial partition. • Distance or similarity between cases– measure of how apart two cases are or how alike cases are in a population. For continuous variables, there are many distance measures: Euclidean distance, Pearson’s Correlation, etc. For categorical variables, one can use chi-square measure, binary matching, etc.

  5. Cluster Analysis-Basics 2 • Cluster validity: size, meaningfulness, and validity. Size: if one or more very small clusters are observed, then there may be too many clusters (general rule). Meaningfulness: the meaning of each cluster should be reasonable from the observed attributes used to create those clusters. Criterion validity: The cross-tabulation of clusters by other variables known from theory or prior research to correlate with the concept which clustering is supposed to reflect, should result in the expected level of association.

  6. Clustering Algorithms: SPSS offers three: • Hierarchical clustering- appropriate for smaller samples (typically < 250); must select a definition of distance and a linking method for forming clusters. • K-means clustering- must specify the number of clusters in advance, then the algorithm assigns cases to the K clusters by the distance to the cluster mean; it is much less computer intensive and appropriate for datasets that are large (ex., > 1,000). • Two-step clustering- creates pre-clusters, then it clusters the pre-clusters using hierarchical methods. Two step clustering handles very large datasets with data that are categorical and continuous.

  7. Limitations of Cluster Analysis • Some notable ones are: • Clustering solution can be affected by various factors, such as variable selection or dropped cases. • Sensitivity to outliers (K-means particularly) • Sequence of observations in the dataset can affect clustering solution (only k-means and two-step); randomization of cases is recommended.

  8. Segmentation Study • Energy Awareness Survey of 904 Oregonians, Summer 2009 • Developing household segments for targeting energy efficiency programs. • Who • Research Into Action, Inc. and • Energy Trust of Oregon • Method • Telephone Survey using RDD and list • samples conducted in May-July 2009. • Authors • Research Into Action, Inc. • • Jane Peters, Ph.D. • • Jun Suzuki, MPA • • MersihaSpahic • Energy Trust of Oregon • • Philipp Degens, Ph.D. • • Sarah Castor

  9. Factor Analysis

  10. Regresssion

  11. Regression Continued

  12. Cluster Analysis

  13. Segmentation

  14. Composition of the cluster

  15. Looking at the Relationship with Other Variables

  16. A few References: • Aldenderfer, Mark S. and Roger K. Blashfield (1984). Cluster analysis. Thousand Oaks, CA: Sage Publications, Quantitative Applications in the Social Sciences Series No. 44. • Jain, A. K.& Dubey, R. C. (1988). Algorithms for clustering data. Englewood Cliffs, NJ: Prentice Hall. • Rapkin, B. D., & Luke, D. A. (1993). Cluster analysis in community research: Epistemology and practice. American Journal of Community Psychology 21, 247-277. • Sireci, S. G. & Geisinger , K. F. (1992). Analyzing test content using cluster analysis and multidimensional scaling. Applied Psychological Measurement 16(1), 17-31. • SPSS, Inc. (2001). The SPSS twostep cluster component. Chicago, IL: SPSS. SPSS white papers/technical report TSCPWP-0101. • Theodoridis, S. & Koutroumbas, K. (1999). Pattern recognition. NY: Academic Press.

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