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Glen L. Urban. Ko, Ryan Massachusetts Institute of Technology. Dept. of Electrical Engineering and Computer Science. Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science 2013-03-01T15:05:44Z 2013-03-01T15:05:44Z 2012 2012 http://hdl.handle.net/1721.1/77444 826515142 Thesis (M. Eng.)--Massachusetts Institute of Technology, Dept. of Electrical Engineering and Computer Science, 2012. Cataloged from PDF version of thesis. Includes bibliographical references (p. 89-90). Prospective meta-analysis, pioneered in the biomedical field, is the meta-analysis of multiple studies conducted using similar protocols and under similar conditions. To eliminate bias, the inclusion of individual studies in the meta-analysis is agnostic of the findings of the individual experiment. In this thesis, I adapt prospective meta-analysis for use in the field of marketing science. Specifically, I design and create a database for prospective marketing meta-analysis that encourages and facilitates international collaboration and scale-up of marketing science studies and use this platform as the basis for a novel web-based media-mix modeling experiment that aims to model the relative effects of a variety of media. I detail the design and implementation of this web-based media-mix modeling experiment, which introduces the use of a browser extension to modify the media experience for test subjects based on their responses to pre-survey questions. I present preliminary results from a 50-user trial run of the system and analyze improvements and next steps, both for the current experiment and scale-up for future studies to include in the meta-analysis. by Ryan Ko. M.Eng. 90 p. eng Massachusetts Institute of Technology M.I.T. theses are protected by copyright. They may be viewed from this source for any purpose, but reproduction or distribution in any format is prohibited without written permission. See provided URL for inquiries about permission. http://dspace.mit.edu/handle/1721.1/7582 Electrical Engineering and Computer Science. Prospective marketing meta-analysis and a novel web-based media-mix modeling experiment Thesis application/pdf Publication unknown unknown <Publication xmlns="https://www.openaire.eu/cerif-profile/1.1/" id="94fc2ce2-1828-4e66-949c-92d0ab716a8c"> <Type xmlns="https://www.openaire.eu/cerif-profile/vocab/COAR_Publication_Types">http://purl.org/coar/resource_type/c_1843</Type> <Language>eng</Language> <Title>Prospective marketing meta-analysis and a novel web-based media-mix modeling experiment</Title> <PublishedIn> <Publication> </Publication> </PublishedIn> <PublicationDate>2012</PublicationDate> <Authors> <Author> <DisplayName>Ko, Ryan</DisplayName> <Affiliation> <OrgUnit> </OrgUnit> </Affiliation> </Author> </Authors> <Editors> </Editors> <Publishers> <Publisher> <DisplayName>Massachusetts Institute of Technology</DisplayName> <OrgUnit /> </Publisher> </Publishers> <License>http://dspace.mit.edu/handle/1721.1/7582</License> <Keyword>Electrical Engineering and Computer Science.</Keyword> <Abstract>Prospective meta-analysis, pioneered in the biomedical field, is the meta-analysis of multiple studies conducted using similar protocols and under similar conditions. To eliminate bias, the inclusion of individual studies in the meta-analysis is agnostic of the findings of the individual experiment. In this thesis, I adapt prospective meta-analysis for use in the field of marketing science. Specifically, I design and create a database for prospective marketing meta-analysis that encourages and facilitates international collaboration and scale-up of marketing science studies and use this platform as the basis for a novel web-based media-mix modeling experiment that aims to model the relative effects of a variety of media. I detail the design and implementation of this web-based media-mix modeling experiment, which introduces the use of a browser extension to modify the media experience for test subjects based on their responses to pre-survey questions. I present preliminary results from a 50-user trial run of the system and analyze improvements and next steps, both for the current experiment and scale-up for future studies to include in the meta-analysis.</Abstract> <Access xmlns="http://purl.org/coar/access_right" > </Access> </Publication>