Relevant bibliographies by topics / Conjoint analysis (Marketing)

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  1. Journal articles
  2. Dissertations / Theses
  3. Books
  4. Book chapters
  5. Conference papers

Academic literature on the topic 'Conjoint analysis (Marketing)'

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Published: 4 June 2021
Last updated: 31 July 2024

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Journal articles on the topic "Conjoint analysis (Marketing)"

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Martin, John, and Thomas E. Moore. "Conjoint Analysis:." Journal of Marketing for Higher Education 4, no. 1-2 (July 22, 1993): 379–403. http://dx.doi.org/10.1300/j050v04n01_26.

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Vriens, Marco. "Solving marketing problems with conjoint analysis∗." Journal of Marketing Management 10, no. 1-3 (April 1994): 37–55. http://dx.doi.org/10.1080/0267257x.1994.9964259.

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Rao, Vithala R., and Luis Eduardo Pilli. "Conjoint Analysis para Pesquisa de Marketing no Brasil." Revista Brasileira de Marketing 13, no. 4 (September 11, 2014): 25–38. http://dx.doi.org/10.5585/remark.v13i4.2707.

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Este artigo oferece uma reviso, de 1971 at a atualidade, dos mtodos de conjoint analysis que so abordagens de coleta de dados baseadas em preferncias ou escolhas declaradas pelos consumidores. Milhares de estudos foram realizados com o uso de conjoint analysis, desde a introduo do mtodo no incio da dcada de 70. Este conjunto de mtodos permite que os pesquisadores de mercado estudem trade-off entre os atributos de novos produtos, sendo til para vrias decises de marketing com design de produto, apreamento e segmentao de mercado. O conjunto atual de opes de conjoint analysis composto pela abordagem tradicional de preferncia declarada, pelas tcnicas de escolhas discretas (CBCA ou choice based conjoint analysis) que se baseiam em escolhas declaradas, pela abordagem autoexplicativa que usa elicitao direta de importncia de atributos e avaliao dos nveis dos atributos e pela abordagem adaptativa (ACA ou adaptive conjoint analysis) que implica em coleta de dados por etapas e adaptativa. Este artigo resume estes mtodos e seus desenvolvimentos recentes e apresenta uma aplicao no Mercado brasileiro. Dada a versatilidade do mtodo, existe um enorme potencial para a pesquisa de marketing no Brasil. Essencialmente, esta metodologia est viva e crescendo.
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Arora, Raj. "Formulating direct marketing offers with conjoint analysis." Journal of Direct Marketing 5, no. 1 (1991): 48–56. http://dx.doi.org/10.1002/dir.4000050108.

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Beall, Ron, and Leslie W. Perttula. "Conjoint Analysis: A Pedagogical Model." Journal of Marketing Education 13, no. 3 (December 1991): 76–82. http://dx.doi.org/10.1177/027347539101300309.

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Dubas, Khalid M., and James T. Strong. "Course Design Using Conjoint Analysis." Journal of Marketing Education 15, no. 1 (April 1993): 31–36. http://dx.doi.org/10.1177/027347539301500105.

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Fletcher, Keith. "An Analysis of Choice Criteria Using Conjoint Analysis." European Journal of Marketing 22, no. 9 (September 1988): 25–33. http://dx.doi.org/10.1108/eum0000000005298.

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Kim, Dong Soo, Roger A. Bailey, Nino Hardt, and Greg M. Allenby. "Benefit-Based Conjoint Analysis." Marketing Science 36, no. 1 (January 2017): 54–69. http://dx.doi.org/10.1287/mksc.2016.1003.

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Christian Zinkhan, F., and George M. Zinkhan. "Using Conjoint Analysis to Design Financial Services." International Journal of Bank Marketing 8, no. 1 (January 1990): 31–34. http://dx.doi.org/10.1108/02652329010136389.

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Mandloi, Anita. "Conjoint Analysis and its Applications in Marketing Research." International Journal of Mathematics Trends and Technology 68, no. 3 (March 25, 2022): 43–44. http://dx.doi.org/10.14445/22315373/ijmtt-v68i3p508.

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Dissertations / Theses on the topic "Conjoint analysis (Marketing)"

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Turner, Julia P. "University preference : A conjoint analysis." Thesis, Edith Cowan University, Research Online, Perth, Western Australia, 1999. https://ro.ecu.edu.au/theses/1245.

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The business of tertiary education has become more and more competitive in recent years due to reductions in government funding and higher study fees. As the nature of the environment grows more competitive the role of marketing, previously non-existent in most universities, has grown significantly. One of the key pieces of information that would assist the marketing effort of any university is an understanding of what determines university preference. This study examines university preference in Western Australia. A form of conjoint analysis, known as Adaptive Conjoint Analysis (ACA), was used to investigate the relative importance of a number of attributes to university preference. The study involved presenting 259 Western Australian school leavers (Year 12 students) with a combination of paper and disk based questionnaires. Results indicated that the four most important determinants of university preference for Western Australian school leavers were course suitability, academic reputation, job prospects and teaching quality. The results are compared to previous research findings and their implications for the marketing of universities are discussed.
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Evans, Callie Bryan Fields Deacue. "Consumer preferences for watermelons a conjoint analysis /." Auburn, Ala, 2008. http://repo.lib.auburn.edu/EtdRoot/2008/SPRING/Agricultural_Economics_and_Rural_Sociology/Thesis/Evans_Callie_53.pdf.

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Barbosa, Eduardo Campana. "Inferência via Bootstrap na Conjoint Analysis." Universidade Federal de Viçosa, 2017. http://www.locus.ufv.br/handle/123456789/17847.

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A presente tese teve como objetivo introduzir o método de reamostragem com reposição ou Bootstrap na Conjoint Analysis. Apresenta-se no texto uma revisão conceitual (Revisão de Literatura) sobre a referida metodologia (Conjoint Analysis) e também sobre o método proposto (Bootstrap). Adicionalmente, no Capítulo I e II, define-se a parte teórica e metodológica da Conjoint Analysis e do método Bootstrap, ilustrando o funcionamento conjunto dessas abordagens via aplicação real, com dados da área de tecnologia de alimentos. Inferências adicionais que até então não eram fornecidas no contexto clássico ou frequentista podem agora ser obtidas via análise das distribuições empíricas dos estimadores das Importâncias Relativas (abordagem por notas) e das Probabilidades e Razão de Escolhas (abordagem por escolhas). De forma geral, os resultados demonstraram que o método Bootstrap forneceu estimativas pontuais mais precisas e tornou ambas as abordagens da Conjoint Analysis mais informativas, uma vez que medidas de erro padrão e, principalmente, intervalos de confiança puderam ser facilmente obtidos para certas quantidades de interesse, possibilitando a realização de testes ou comparações estatísticas sobre as mesmas.
The aim of this thesis was introduce the Booststrap resampling method in Conjoint Analysis. We present in the text a conceptual review (Literature Review) about this methodology (Conjoint Analysis) and also about the proposed method (Bootstrap). In addition, in Chapter I and II, the theoretical and methodological aspects of Conjoint Analysis and the Bootstrap method are defined, illustrating the joint operation of these approaches via real application, with data from the food technology area.. Additional inferences have not been provided in the classic or frequentist context can now be obtained by analyzing the empirical distributions of Relative Importance (ratings based approach) and Probability and Choice Ratio (choice based approach) estimators. Overall, the results demonstrated that the Bootstrap method provided more accurate point estimates and made both Conjoint Analysis approaches more informative, since standard error measures, and mainly confidence intervals, could be easily obtained for certain quantities of interest, making it possible to perform statistical tests or comparisons on them.
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Barbosa, Eduardo Campana. "Choice-Based Conjoint Analysis: um enfoque bayesiano." Universidade Federal de Viçosa, 2015. http://www.locus.ufv.br/handle/123456789/7179.

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A presente dissertação teve como objetivo principal demonstrar um enfoque Bayesiano para a metodologia Choice-Based Conjoint Analysis (CBCA). Apresenta-se no texto uma ampla revisão sobre a CBCA (Capítulo 1), sobre o modelo Logit Multinomial [desenvolvimento do modelo, procedimentos de estimação de parâmetros, probabilidades e razões de escolha (Capítulo 2)] e sobre o enfoque de estimação Bayesiano [distribuição a priori utilizada, aproximação de Laplace para a função de verossimilhança, distribuições a posteriori e detalhes sobre o algoritmo MCMC empregado (Capítulo 3)]. No Capítulo 4 apresenta-se um exemplo hipotético, no intuito de demonstrar os resultados e inferências que podem ser obtidos por meio desta recente abordagem (Bayesiana), sendo também apresentados os resultados do enfoque Frequentista. O tratamento em estudo foi um tipo de refrigerante e avaliou-se o efeito de três fatores (A, B e C) na intenção de compra de 96 consumidores, por meio de dados simulados. As análises estatísticas foram conduzidas no software livre R, cujos scripts encontram-se disponibilizados nos apêndices desta dissertação. Concluiu-se que a abordagem Bayesiana para CBCA apresentou resultados interessantes e satisfatórios, com estimativas similares às Frequentistas e mostrando-se uma alternativa metodológica viável para os estudos de CBCA. Adicionalmente, a abordagem proposta possibilitou ainda ao pesquisador construir intervalos de credibilidade (percentis das distribuições a posteriori) para as probabilidades e razões de escolha, no intuito de comparar estas quantidades ou testar hipóteses sobre estas. Quanto aos resultados práticos, a maior probabilidade de escolha estava associada ao tratamento 4, composto pelo nível do fator A, nível do fator B e nível do fator C.
This dissertation main goal is to demonstrate the Bayesian approach to Choice-Based Conjoint Analysis (CBCA). We present a comprehensive review of the CBCA methodology (Chapter 1), on the Multinomial Logit model [model development, parameter estimation procedures, probabilities of choice ratios (Chapter 2)] and on the Bayesian estimation approach [prior distribution, Laplace approach to the likelihood function, posterior distributions and details about the MCMC algorithm we applied (Chapter 3)]. In Chapter 4 we present a hypothetical example, in order to demonstrate the results and inferences that can be obtained through this recent approach (Bayesian), and we also present the results of the frequentist approach. The treatment for the study was a type of refrigerant (soda or soft drink) and we evaluated the effect of three factors (volume, type and color) on purchase intention of 96 consumers, using simulated data. Statistical analyzes were conducted with the free software R, whose scripts are provided in the appendices of this dissertation. It was concluded that the Bayesian approach to CBCA presented interesting and satisfactory results, with estimates similar to the frequentist ones, therefore proved to be a viable alternative methodology for CBCA studies. Additionally, the proposed approach also allows the researcher to build credibile intervals (percentiles of the posterior distributions) for the probabilities and choice ratios, in order to compare these quantities or test hypotheses about them. In terms of practical or applied results, the highest estimated probability of choice was obtained for treatment 4, with a1 level of factor A, b2 level of factor B and C1 level of factor C.
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Winzar, Hume. "A Monte-Carlo evaluation of conjoint preference simulators." Thesis, The University of Sydney, 1994. https://hdl.handle.net/2123/27562.

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The purpose of conjoint preference simulators is to translate predicted evaluations in a conjoint analysis study into likelihood of choice (or, more correctly, likelihood of preference). The ease of use of commercially available conjoint software and rating scale techniques could mask some fundamental, and usually flawed, assumptions applications make about evaluation and choice processes. As a result, some researchers could make some costly incorrect decisions based on the results of their conjoint studies.
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Wong, Shing-tat. "Disaggregate analyses of stated preference data for capturing parking choice behavior." Click to view the E-thesis via HKUTO, 2006. http://sunzi.lib.hku.hk/hkuto/record/B36393678.

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Gustafsson, Anders. "Customer focused product development by conjoint analysis and QFD /." Online version, 1996. http://bibpurl.oclc.org/web/31484.

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Heger, Roland Helmut. "Value Measurement for New Product Category: a Conjoint Approach to Eliciting Value Structure." PDXScholar, 1996. https://pdxscholar.library.pdx.edu/open_access_etds/1305.

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Ability to measure value from the customer's point of view is central to the determination of market offerings: Customers will only buy the equivalent of perceived value, and companies can only offer benefits that cost less to provide than customers are willing to pay. Conjoint analysis is the most popular individual-level value measurement method to determine relative impact of product or service attributes on preferences and other dependent variables. This research focuses on how value measurement can be made more accurate and more reliable by measuring the relative influence of selected methodological variations on performance in prediction and on stability of value structure, and by grouping customers with similar value structure into segments which respond to product stimuli in a similar manner. Influences of the type of attributes included in the conjoint task, of the factorial design used to construct the product profiles, of the type and form of model, of the time of measurement, and of the type of cluster-based segmentation method, are evaluated. Data was gathered with a questionnaire that controlled for methodological variations, and with a notebook computer as the measurement object. One repeated measurement was taken. The study was conducted in two phases. In Phase I, influences of methodological variations on accuracy in prediction and on respective value structure were examined. In Phase II, different cluster-based segmentation methods--hierarchical clustering (HIC), non-hierarchical clustering (NHC), and fuzzy c-means clustering (FUC)--and according conjoint models were evaluated for their performance in prediction and in comparison with individual-level conjoint models. Results show the best models for a variety of design parameters are traditional individual-level, main-effects-only conjoint models. Neither modeling of interactions, nor segment-level conjoint models were able to improve on prediction. Best segment-level conjoint models were obtained with a fuzzy clustering method, worst models were obtained with k-means and the most fuzzy clustering approach. In conclusion, conjoint analysis reveals itself as a reliable method to measure individual customer value. It seems more rewarding for improvement of accuracy in prediction to apply repeated measures, or gather additional data about the respondent, than to attempt improvement on methodological variations with a single measurement.
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Siqueira, Jose de Oliveira. "Mensuração da estrutura de preferência do consumidor: uma aplicacao de conjoint analysis em marketing." Universidade de São Paulo, 1996. http://www.teses.usp.br/teses/disponiveis/12/12133/tde-01032005-185221/.

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O objeto desta dissertação é a estrutura de preferência do consumidor (EPC). O objetivo geral é estudar os métodos de mensuração da EPC (MMEPC) e o tema é a mensuração desta estrutura, utilizando a técnica estatística Conjoint Analysis (CA). A CA proporciona uma maneira realista de medir o impacto de cada atributo de um produto na preferência do consumidor. Esta técnica estatística tem sido cada vez mais aplicada em problemas de Marketing. Alguns softwares têm surgido e provocado o aumento de uso desta técnica. A dissertação discute uma aplicação real desta técnica num problema de definição de um tecido de linho ótimo para um conjunto de especialistas. A ênfase está no planejamento do experimento fatorial fracionário para a estimação do modelo individual cuja variável resposta é posto e os atributos são qualitativos. Modelos lineares de célula de referência e de desvios foram construídos para a análise do experimento. Utilizou-se o modelo linear aditivo não-saturado. Alguns dos principais softwares foram analisados: SPSS, SAS, ACA, CBC e CVA. O autor define um projeto de software de planejamento de experimentos fatoriais fracionários ótimos segundo a eficiência-D. O estudo dos MMEPC pode proporcionar as seguintes contribuições à Administração: otimização de projetos de produtos/serviços/conceitos, quantificação da EPC, segmentação de mercado, determinação da probabilidade de escolha ou participação esperada do mercado de produtos/serviços/conceitos num determinado cenário e simulação (predição) de preferências individuais e agregadas.
The purpose of this dissertation is the consumer’s preference structure (CPS). The general objective is to study the methods of mensurement of CPS (MMCPS) and its main purpose is to measure that structure using the statistical technique Conjoint Analysis (CA). The CA provides a realistic way to measure the impact of the attribute of a product on the consumer’s preference. This statistical technique is being used more and more in marketing problems. Some softwares have emerged and increased the use of this technique. This dissertation discusses a real application of this technique on the problem of definition of a linen tissue for a specialist group. The emphasis is on the design of a fractional factorial experiment for estimation of a individual model in wich the response variable is rank and the attributes are qualitative. Linear models of a cell of reference and deviations were constructed for the experimental analysis. Used one a non satureded linear model. Some of the main softwares were analysed: SPSS, SAS, ACA, CBC and CVA. The author defines a software project for optimum fractional factorial experimental design and analysis, according to D-efficiency. MMCPS studies can provide the following contribution to Management: products/services/concepts optimization, CPS quantification, marketing segmentation, choice probability determination on the expected participation of the products/services/concepts market in a particular scenario and the simulation (prediction) of individual and aggregate preferences.
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Tan, Donald. "The impact of numeric sub-branding on Singaporean Chinese consumers : a conjoint analysis." University of Western Australia. Graduate School of Management, 2006. http://theses.library.uwa.edu.au/adt-WU2007.0029.

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[Truncated abstract] It has been argued that the demand for a product is largely dependent on price and quality (Dodds, Monroe & Grewal, 1991) and that it is possible to provide a global product with a universal brand, as consumers desire reliable and quality products at low prices (Levitt, 1983). As global companies extend their reach around the world and such “global” products become available world wide, it is important for marketers to understand if other factors play a significant role in consumers’ purchase processes, especially in Asia where cultural and social influences can be very different from Western societies . . . Since numerology is deeply rooted in Chinese culture, the present study was undertaken to examine the impact that two important numbers (4 and 8) had on Chinese consumers’ value perceptions when used in sub-brand extensions. These numbers were chosen as Ang (1997) had noted the numbers represented “death” (4) and “prosperity” (8) in Chinese numerology and, consequently, may have a negative or positive impact on the value Chinese consumers attach to a product. While there are many types of products available to consumers and possible juxtapositions of numbers are almost endless, the present study was restricted to examining the impact that the numbers 4 and 8 had on the value Chinese consumers attached to cars and mobile phones to ensure the study was manageable. The study was undertaken in Singapore, but it was hoped that the results could be translated to other Chinese markets so more cost-effective and efficient approaches could be developed for such markets.
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Books on the topic "Conjoint analysis (Marketing)"

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Reibstein, David J. Conjoint analysis reliability: Empirical findings. Cambridge, MA: Marketing Science Institute, 1987.

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Reibstein, David J. Conjoint analysis reliability: Empirical findings. Cambridge, Mass: Marketing Science Institute, 1987.

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Reibstein, David J. Conjoint analysis reliability: Empirical findings. [Stanford]: Graduate School of Business, Stanford University, 1985.

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L, Moore William. Using conjoint analysis to help design product platforms. Cambridge, Mass: Marketing Science Institute, 1998.

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Vyas, Preeta H. Measuring consumer preferences for sales promotion schemes through conjoint design in FMCG sector. Ahmedabad: Indian Institute of Management, 2005.

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Rathnow, Peter J. Integriertes Variantenmanagement: Bestimmung, Realisierung und Sicherung der optimalen Produktvielfalt. Göttingen: Vandenhoeck & Ruprecht, 1993.

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Anderson, James Lavalette. A conjoint approach to model product preferences: The New England market for fresh and frozen salmon. Kingston, RI: University of Rhode Island, Dept. of Resource Economics, 1992.

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Castro, Eduardo Anselmo de. Malthus revisited: The economics and politics of sustainable development. St. Andrews: St. Salvator's College, 1996.

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Bernd, Schubert. Entwicklung von Konzepten für Produktinnovationen mittels Conjointanalyse. Stuttgart: C.E. Poeschel, 1991.

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Sattler, Henrik. Herkunfts- und Gütezeichen im Kaufentscheidungsprozess: Die Conjoint-Analyse als Instrument der Bedeutungsmessung. Stuttgart: M & P, 1991.

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Book chapters on the topic "Conjoint analysis (Marketing)"

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Rao, Vithala R. "Applications to a Miscellany of Marketing Problems." In Applied Conjoint Analysis, 317–43. Berlin, Heidelberg: Springer Berlin Heidelberg, 2013. http://dx.doi.org/10.1007/978-3-540-87753-0_9.

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Wedel, Michel, and Wagner A. Kamakura. "Product-Specific Unobservable Bases: Conjoint Analysis." In International Series in Quantitative Marketing, 295–321. Boston, MA: Springer US, 2000. http://dx.doi.org/10.1007/978-1-4615-4651-1_18.

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Hauser, John R., and Vithala R. Rao. "Conjoint Analysis, Related Modeling, and Applications." In International Series in Quantitative Marketing, 141–68. Boston, MA: Springer US, 2004. http://dx.doi.org/10.1007/978-0-387-28692-1_7.

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Sattari, Setayesh, Tim Foster, Kaveh Peighambari, and Arash Kordestani. "Preferences of Young News Consumers: A Conjoint Analysis." In Developments in Marketing Science: Proceedings of the Academy of Marketing Science, 595–98. Cham: Springer International Publishing, 2014. http://dx.doi.org/10.1007/978-3-319-10951-0_216.

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Green, Paul E., Abba M. Krieger, and Yoram Wind. "Thirty Years of Conjoint Analysis: Reflections and Prospects." In International Series in Quantitative Marketing, 117–39. Boston, MA: Springer US, 2004. http://dx.doi.org/10.1007/978-0-387-28692-1_6.

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Mulye, Rajendra. "Commercial Use of Conjoint Analysis in Australia and New Zealand." In Developments in Marketing Science: Proceedings of the Academy of Marketing Science, 95–100. Cham: Springer International Publishing, 2015. http://dx.doi.org/10.1007/978-3-319-17320-7_24.

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Agarwal, Manoj K. "An Empirical Comparison of Traditional Full-Profile Conjoint and Adaptive Conjoint Analysis." In Proceedings of the 1991 Academy of Marketing Science (AMS) Annual Conference, 351–55. Cham: Springer International Publishing, 2015. http://dx.doi.org/10.1007/978-3-319-17049-7_72.

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Green, Paul E., and Abba M. Krieger. "Product Line Price Optimization With Conjoint Analysis." In Proceedings of the 1992 Academy of Marketing Science (AMS) Annual Conference, 273–77. Cham: Springer International Publishing, 2014. http://dx.doi.org/10.1007/978-3-319-13248-8_57.

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Hille, Stefanie Lena, Andrea Tabi, and Rolf Wüstenhagen. "Market Segmentation for Green Electricity Marketing Results of a Choice-Based Conjoint Analysis with German Electricity Consumers." In Marketing Renewable Energy, 91–108. Cham: Springer International Publishing, 2017. http://dx.doi.org/10.1007/978-3-319-46427-5_5.

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Aurifeille, Jacques-Marie, and Pascale G. Quester. "A Conjoint Clusterwise Regression Analysis of Business Ethical Tolerance." In New Meanings for Marketing in a New Millennium, 131. Cham: Springer International Publishing, 2014. http://dx.doi.org/10.1007/978-3-319-11927-4_38.

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Conference papers on the topic "Conjoint analysis (Marketing)"

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Prasetyo, Yogi Tri, Krisna Chandra Susanto, Sheree Mae A. Asiddao, Omar Paolo Benito, Jui-Hao Liao, Michael Nayat Young, Satria Fadil Persada, and Reny Nadlifatin. "Determining Marketing Strategy for Coffee Shops with Conjoint Analysis." In 2023 IEEE International Conference on Industrial Engineering and Engineering Management (IEEM). IEEE, 2023. http://dx.doi.org/10.1109/ieem58616.2023.10406308.

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Ren, Yi, and Panos Y. Papalambros. "On the Use of Active Learning in Engineering Design." In ASME 2012 International Design Engineering Technical Conferences and Computers and Information in Engineering Conference. American Society of Mechanical Engineers, 2012. http://dx.doi.org/10.1115/detc2012-70624.

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Active learning refers to the mechanism of querying users to accomplish a classification task in machine learning or a conjoint analysis in econometrics with minimum cost. Classification and conjoint analysis have been introduced to design research to automate design feasibility checking and to construct marketing demand models, respectively. In this paper, we review active learning algorithms from computer and marketing science, and establish the mathematical commonality between the two approaches. We compare empirically the performance of active learning and static D-optimal design on simulated classification and conjoint analysis test problems with labelling noise. Results show that active learning outperforms D-optimal design when query size is large or noise is small.
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Ren, Yi, and Panos Y. Papalambros. "Enhanced Adaptive Choice-Based Conjoint Analysis Incorporating Engineering Knowledge." In ASME 2014 International Design Engineering Technical Conferences and Computers and Information in Engineering Conference. American Society of Mechanical Engineers, 2014. http://dx.doi.org/10.1115/detc2014-34790.

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Conjoint analysis from marketing has been successfully integrated with engineering analysis in design for market systems. The long questionnaires needed for conjoint analysis in relatively complex design decisions can become cumbersome to the human respondents. This paper presents an adaptive questionnaire generation strategy that uses active learning and allows incorporation of engineering knowledge in order to identify efficiently designs with high probability to be optimal. The strategy is based on viewing optimal design as a group identification problem. A running example demonstrates that a good estimation of consumer preference is not always necessary for finding the optimal design and that conjoint analysis could be configured more effectively for the specific purpose of design optimization. Extending the proposed method beyond a homogeneous preference model and noiseless user responses is also discussed.
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Razu, Swithin S., and Shun Takai. "An Approach to Modeling Customer Preference Uncertainty by Applying Bootstrap to Choice-Based Conjoint Analysis Data." In ASME 2010 International Design Engineering Technical Conferences and Computers and Information in Engineering Conference. ASMEDC, 2010. http://dx.doi.org/10.1115/detc2010-28231.

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Analysis of customer preferences is among the most important tasks in a new product development. How customers come to appreciate and decide to purchase a new product affects the products market share and therefore its success or failure. Unfortunately, when designers select a product concept early in the product development process, customer preference response to the new product is unknown. Conjoint analysis is a statistical marketing tool that has been used to estimate market shares of new product concepts by analyzing data on the product ratings, rankings or concept choices of customers. This paper proposes an alternative to traditional conjoint analysis methods that provide point estimates of market shares. It proposes two approaches to model market share uncertainty; bootstrap and binomial inference applied to choice-based conjoint analysis data. The proposed approaches are demonstrated and compared using an illustrative example.
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Shafranskaya, Irina N. "APPLICATION OF CONJOINT-ANALYSIS FOR THE ESTIMATION OF MULTI-ATTRIBUTIVE PRODUCT’S UTILITY." In Bridging Asia and the World: Globalization of Marketing & Management Theory and Practice. Global Alliance of Marketing & Management Associations, 2014. http://dx.doi.org/10.15444/gmc2014.05.10.10.

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Amarchinta, Hemanth K., and Ramana V. Grandhi. "Combining Marketing and Engineering Tools for Multi-Attribute Optimization." In ASME 2007 International Design Engineering Technical Conferences and Computers and Information in Engineering Conference. ASMEDC, 2007. http://dx.doi.org/10.1115/detc2007-34556.

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Multidisciplinary design optimization has been an active topic of research in the past two decades in developing algorithms for reducing computational cost of re-analysis and also in developing efficient ways of calculating sensitivities. Most of the efforts were aimed at single objective function (attribute). Also very little work is done to include designer’s preferences inside the optimization. In this paper, conjoint analysis, a popular marketing technique to assess consumer preferences is used to involve the preferences of the designer. The optimization is driven by the designer’s preferences and a preferred design is obtained. Here, a novel way of combining tools from marketing and engineering is shown. A cantilever beam, and a composite lightweight torpedo are used as examples to demonstrate the method.
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Aprilianty, Fitri, and Mustika Sufiati Purwanegara. "USING NEURAL RESPONSE (EEG) AND CONJOINT ANALYSIS TO UNDERSTAND THE EFFECT OF UNDERWEAR’S PRODUCT CUES ON CONSUMER CHOICE." In Bridging Asia and the World: Globalization of Marketing & Management Theory and Practice. Global Alliance of Marketing & Management Associations, 2014. http://dx.doi.org/10.15444/gmc2014.08.05.01.

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