Paper Status Tracking

Journals by Title

Journals by Subject

Contact us

	[email protected]
	3275638434

	Paper Publishing WeChat

Useful Links

Creative Commons Attribution-NonCommercial 4.0 International License

Article

Open Access

Model of Simulation for Optimizing Marketing Mix Through Conjoint Analysis Case Study: Launching a Product on a New Market

Author(s)

Luminita Pistol, Rocsana Bucea-Manea-Tonis

Full-Text PDF Download XML 971 Views

DOI:10.17265/2328-7144/2017.04.003

Affiliation(s)

Spiru Haret University, Bucharest, Romania

ABSTRACT

This paper applies conjoint analysis approach to simulate and determine the optimal marketing mix for a Romanian company that struggles to face the market higher competition. The company intends to launch its product into a new market and surveys the likelihood of getting international. The survey helps the company to gather information from clients regarding product characteristics, price, method of distribution, and promotion, according to customer preferences. Having these data, it is made a marketing simulation using conjoint analysis in order to get the optimal marketing mix in launching the product on a new market. The optimal marketing mix is given by the highest mix of utility for the clients, but sometimes the marketing manager would choose a little bit lower utility mix, forced by the company restrictions. corporate responsibility program. ductive capacity, and for raising and maintaining social stability level.

KEYWORDS

marketing simulation, marketing mix, conjoint analysis, launching a product on a new market

Cite this paper

Economics World, July-Aug. 2017, Vol. 5, No. 4, 311-315 doi: 10.17265/2328-7144/2017.04.003

References

Arenoe, B., Rest, I., Van Der, J. P., & Kattuman, P. (2015). Game theoretic pricing models in hotel revenue management: An equilibrium choice-based conjoint analysis approach. Tourism Management, 51, 96-102.

Bucea-Manea-Tonis, R. (2014). Simulari de Marketing, Bucuresti (Ed.). Fundatiei Romania de Maine.

Halme, M., & Kallio, M. (2011). Estimation methods for choice-based conjoint analysis of consumer preferences. European Journal of Operational Research, 214(1), 160-167.

Hauber, A. B., Gonzales, J. M., Groothuis-Oudshoorn, C. G. M., Prior, T., Marshall, D., Cunningham, A. C., Jzerman, M. J. I., & Bridges, J. F. P. (2016). Statistical methods for the analysis of discrete choice experiments: A report of the ISPOR conjoint analysis good research practices task force. Value in Health. In Press.

Maldonado, S., Montoya, R., & Weber, R. (2015). Advanced conjoint analysis using feature selection via support vector machines. European Journal of Operational Research, 241(2), 564-574.