Deep learning for discovering new product opportunities
Name
1017490273-MIT.pdf
Size
4.24 MB
Format
Adobe PDF
Checksum (MD5)
7e2ec7425993c18b3aed21438d325deb
Author(s)
Perez, Santiago(Perez Lastra)
Advisor(s)
Glen Urban.
Date Issued
2017
Publisher
Massachusetts Institute of Technology
Abstract
Modeling customer preferences over multivariate attributes has long been an endeavor of marketing research. We provide an updated approach which can learn customer preferences for complex products with multiple multivariate attributes using modern Deep Neural Networks. In turn we outline approaches for framing managerial questions in the form of inference problems. With our empirical application to product identification in credit cards, we conclude Deep Learning results in significantly better performance than the state of the art. Our approach is scalable to Big Data and can derive superior predictive power from the inexpensive unstructured data exhaust of internet commerce.
Description
Thesis: M. Eng., Massachusetts Institute of Technology, Department of Electrical Engineering and Computer Science, 2017
Cataloged from PDF version of thesis.
Includes bibliographical references (pages 65-66).
Subjects
Electrical Engineering and Computer Science.
MIT Department
Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science
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