Supporting telecommunication product sales by conjoint analysis
DOI:
https://doi.org/10.26636/jtit.2008.3.883Keywords:
decision analysis, multiple criteria analysis, utility theory, preference measurement, conjoint analysis, consumer behavior, purchase intent, marketing, marketing toolsAbstract
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 marketshare. 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.
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References
[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.
View in Google Scholar
[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
View in Google Scholar
[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.
View in Google Scholar
[4] W. S. DeSarbo et al., „Representing heterogeneity in consumer response models”, Market. Lett., vol. 8, no. 3, pp. 335-348, 1997.
View in Google Scholar
[5] J. D. Carroll and P. E. Green, „Psychometric methods in marketing research: part I, conjoint analysis”, J. Market. Res., vol. 32, no. 4, pp. 385-391, 1995.
View in Google Scholar
[6] J. D. Carroll and P. E. Green, „Psychometric methods in marketing research: part II, multidimensional scaling”, J. Market. Res., vol. 34, no. 2, pp. 193-204, 1997.
View in Google Scholar
[7] T. Evgeniou, C. Boussios, and G. Zacharia, „Generalized robust conjoint estimation”, Market. Sci., vol. 24, pp. 415-429, 2005.
View in Google Scholar
[8] J. Granat, „Data mining and complex telecommunications problems modeling”, J. Telecommun. Inform. Technol., no. 3, pp. 115-120, 2003.
View in Google Scholar
[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.
View in Google Scholar
[10] P. E. Green and A. M. Krieger, „Individualized hybrid models for conjoint analysis”, Manage. Sci., vol. 42, no. 6, pp. 850-868, 1996.
View in Google Scholar
[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.
View in Google Scholar
[12] W. F. Kuhfeld, „Conjoint analysis”, 2007, http://support.sas.com/techsup/technote/ts722h.pdf
View in Google Scholar
[13] R. Johnson, http://www.sawtoothsoftware.com/education.shtml
View in Google Scholar
[14] V. Srinivasan, „A conjunctive-compensatory approach to the self explication of multiattributed preferences”, Decis. Sci., vol. 19, no. 2, pp. 295-305, 1988.
View in Google Scholar
[15] O. Toubia, J. R. Hauser, and D. I. Simester, „Polyhedral methods for adaptive choice-based conjoint analysis”, J. Market. Res., vol. 41, pp. 116-131, 2004.
View in Google Scholar
[16] M. Walesiak and A. Bak, Conjoint analysis w badaniach marketingowych. Wrocław: WAE, 2000 (in Polish).
View in Google Scholar
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