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Języki publikacji
Abstrakty
Conjoint analysis is widely used as a marketing research technique to study consumers' product preferences and simulate customer choices. It is used in designing new products, changing or repositioning existing products, evaluating the effect of price on purchase intent, and simulating market share. In this work the possibility of conjoint analysis usage in telecommunication filed is analyzed. It is used to find optimal products which could be recommended to telecommunication customers. First, a decision problem is defined. Next, the conjoint analysis method and its connections with ANOVA as well as regression techniques are presented. After that, different utility functions that represent preferences for voice, SMS, MMS and other net services usage are formulated and compared. Parameters of the proposed conjoint measures are determined by regression methods running on behavioral data, represented by artificially generated call data records. Finally, users are split in homogenous groups by segmentation techniques applied to net service utilities derived from conjoint analysis. Within those groups statistical analyses are performed to create product recommendations. The results have shown that conjoint analysis can be successfully applied by telecommunication operators in the customer preference identification process. However, further analysis should be done on real data, other data sources for customer preference identification should be explored as well.
Rocznik
Tom
Strony
28--34
Opis fizyczny
Bibliogr. 16 poz., tab.
Twórcy
autor
- Institute of Control and Computation Engineering, Warsaw University of Technology, Nowowiejska st 15/19, 00-665 Warsaw, Poland, Piotr.Rzepakowski@elka.pw.edu.pl
Bibliografia
- [1] P. Aggarwal and R. Vaidyanathan, „Eliciting online customers’ preferences: conjoint vs. self-explicated attribute-level measurements”, J. Market. Manage., vol. 19, pp. 157–177, 2003.
- [2] J. Bissett, F. Falschini, Y. Jansen, R. Monti, and V. Massow, „Sparking connections: best practice in customer relationships and retail marketing”, 2000, http://www.bcg.com
- [3] P. J. Danaher, „Using conjoint analysis to determine the relative importance of service attributes measured in customer satisfaction surveys”, J. Retail., vol. 73, no. 2, pp. 235–260, 1997.
- [4] W. S. DeSarbo et al., „Representing heterogeneity in consumer response models”, Market. Lett., vol. 8, no. 3, pp. 335–348, 1997.
- [5] C. Douglas and P. E. Green, „Psychometric methods in marketing research: part I, conjoint analysis”, J. Market. Res., vol. 32, no. 4, pp. 385–391, 1995.
- [6] C. Douglas and P. E. Green, „Psychometric methods in marketing research: part II, multidimensional scaling”, J. Market. Res., vol. 34, no. 2, pp. 193–204, 1997.
- [7] T. Evgeniou, C. Boussios, and G. Zacharia, „Generalized robust conjoint estimation”, Market. Sci., vol. 24, pp. 415–429, 2005.
- [8] J. Granat, „Data mining and complex telecommunications problems modeling”, J. Telecommun. Inform. Technol., vol. 3, pp. 115–120, 2003.
- [9] P. E. Green, S. M. Goldberg, and M. Montemayor, „A hybrid utility estimation model for conjoint analysis”, J. Market., vol. 45, no. 1, pp. 33–41, 1981.
- [10] P. E. Green and A. M. Krieger, „Individualized hybrid models for conjoint analysis”, Manage. Sci., vol. 42, no. 6, pp. 850–868, 1996.
- [11] P. E. Green, A. M. Krieger, and Y. Wind, „Thirty years of conjoint analysis: reflections and prospects”, Interfaces, vol. 31, no. 3, part. 2, pp. 56–73, 2001.
- [12] W. F. Kuhfeld, „Conjoint analysis”, 2007, http://support.sas.com/techsup/technote/ts722h.pdf
- [13] R. Johnson, http://www.sawtoothsoftware.com/education.shtml
- [14] V. Srinivasan, „A conjunctive-compensatory approach to the self explication of multiattributed preferences”, Decis. Sci., vol. 19, no. 2, pp. 295–305, 1988.
- [15] O. Toubia, J. R. Hauser, and D. I. Simester, „Polyhedral methods for adaptive choice-based conjoint analysis”, J. Market. Res., vol. 16, pp. 116–131, 2004.
- [16] M. Walesiak and A. Bak, Conjoint analysis w badaniach marketingowych. Wrocław: WAE, 2000 (in Polish).
Typ dokumentu
Bibliografia
Identyfikator YADDA
bwmeta1.element.baztech-article-BATA-0001-0036