Advertisement

Image Feature Selection for Market Segmentation: A Comparison of Alternative Approaches

  • Susanne RumstadtEmail author
  • Daniel Baier
Conference paper
Part of the Studies in Classification, Data Analysis, and Knowledge Organization book series (STUDIES CLASS)

Abstract

The selection of variables (e.g. socio-demographic or psychographic descriptors of consumers, their buying intentions, buying frequencies, preferences) plays a decisive role in market segmentation. The inclusion as well as the exclusion of variables can influence the resulting classification decisively. Whereas this problem is always of importance it becomes overwhelming when customers should be grouped on the basis of describing images (e.g. photographs showing holidays experiences, usually bought products), as the number of potentially relevant image features is huge. In this paper we apply several general-purpose approaches to this problem: the heuristic variable selection by Carmone et al. (1999) and Brusco and Cradit (2001) as well as the model-based approach by Raftery and Dean (2004). We combine them with k-means, fuzzy c-means, and latent class analysis for comparisons in a Monte Carlo setting with an image database where the optimal market segmentation is already known.

Keywords

Cluster Method Variable Selection Latent Class Analysis Color Histogram Market Segmentation 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

References

  1. Baier, D., & Daniel, I. (2012). Image clustering for marketing purposes. In Challenges at the interface of data analysis, computer science, and optimization. Proceedings of the Annual Conference of the Gesellschaft für Klassifikation e. V., Karlsruhe, July 21-23, 2010 (pp. 487–494). Berlin: Springer.Google Scholar
  2. Baier, D., Daniel, I., Frost, S., & Naundorf, R. (2012). Image data analysis and classification in marketing. Advances in Data Analysis and Classification, 6(4), 253–276.MathSciNetCrossRefzbMATHGoogle Scholar
  3. Brusco, M. J., & Cradit, J. D. (2001). A variable-selection heuristic for K-means clustering. Psychometrika, 66, 249–270.MathSciNetCrossRefGoogle Scholar
  4. Carmone, F. J., Kara, A., & Maxwell, S. (1999). HINoV: A new model to improve market segment definition by identifying noisy variables. Journal of Marketing Research, 36, 501–509.CrossRefGoogle Scholar
  5. Chatzichristofis, S. A., & Boutalis, Y. S. (2008a). CEDD: Color and edge directivity descriptor. A compact descriptor for image indexing and retrieval. In ICVS’08 6th International Conference on Computer Vision Systems (pp. 312–322), Santorini.Google Scholar
  6. Chatzichristofis, S. A., & Boutalis, Y. S. (2008b). FCTH: Fuzzy color and texture histogram – A low level feature for accurate image retrieval. In WIAMIS’08 9th International Workshop on Image Analysis for Multimedia Interactive Services (pp. 191–196), Klagenfurt.Google Scholar
  7. Del Bimbo, A. (1999). Visual information retrieval. San Francisco: Morgan Kaufman.Google Scholar
  8. Groves, R. M. (1989). Survey errors and survey costs. Wiley Series in probability and statistics: Probability and mathematical statistics. New York: Wiley.CrossRefGoogle Scholar
  9. Hubert, L., & Arabie, P. (1985). Comparing partitions. Journal of Classification, 2, 193–218. Japan Electronics and Information Technology Industry.Google Scholar
  10. Raftery, A. E., & Dean, N. (2004). Variable Selection for Model-Based Clustering. Technical Report No. 452, Department of Statistics, University of Washington, Seattle.Google Scholar
  11. Rand, W. (1971). Objective criteria for the evaluation of clustering methods. Journal of the American Statistical Association, 66, 846–850.CrossRefGoogle Scholar
  12. Shapiro, L. G., & Stockman, G. C. (2001). Computer vision. Upper Saddle River: Prentice Hall.Google Scholar
  13. Tamura, H., Mori, S., & Yamakawi, T. (1978). Textural features corresponding to visual perception. IEEE Transactions on Systems, Man, and Cybernetics, 8(6), 460–472.CrossRefGoogle Scholar
  14. Wedel, M., & Kamakura, W. A. (2000). Market segmentation: Conceptual and methodological foundations. Boston: Kluwer.CrossRefGoogle Scholar
  15. Weinstein, A. (1994). Market segmentation. Using demographics, psychographics and other niche marketing techniques to predict and model consumer behaviour. Burr Ridge: Irwin.Google Scholar
  16. Wells, W. D., & Tiggert, D. J. (1971). Activities, interests and opinions. Journal of Advertising Research, 11(4), 27–35.Google Scholar

Copyright information

© Springer International Publishing Switzerland 2014

Authors and Affiliations

  1. 1.Marketing and Innovation Management, Institute of Business Administration and EconomicsBrandenburg University of Technology CottbusCottbusGermany

Personalised recommendations