Identifying customer needs from user-generated content
Name
987002329-MIT.pdf
Description
Full printable version
Size
2.79 MB
Format
Adobe PDF
Checksum (MD5)
c2a19b19811b59a568f424a9305955a8
Author(s)
Timoshenko, Artem
Advisor(s)
John R. Hauser.
Alternative Title
Identifying customer needs from UGC
Date Issued
2017
Publisher
Massachusetts Institute of Technology
Abstract
Understanding customer needs is an important part of marketing strategy, product development, and marketing research. The explosive growth of user-generated content (UGC) creates an opportunity to enhance industry-standard interview-based approaches for identifying customer needs. However, the traditional manual review approach is neither efficient nor effective when applied to a large UGC corpus because non-informative and repetitive content crowd out information about customer needs. We identify customer needs from UGC by combining machine learning methods to select content for review with human judgement to formulate customer needs. In particular, we use a convolutional neural network to filter out non-informative content and dense sentence representations to identify sufficiently different sentences for manual review. An empirical proof-of-concept compares customer needs for oral care products identified from online reviews (UGC) with customer needs identified by a third-party professional consulting firm using industry-standard methods. In this application, UGC identifies additional customer needs, unreachable by the interview-based approach. Our approach improves efficiency of manual review in terms of a number of unique customer needs per unit effort.
Description
Thesis: S.M. in Management Research, Massachusetts Institute of Technology, Sloan School of Management, 2017.
Cataloged from PDF version of thesis.
Includes bibliographical references (pages 23-24).
Subjects
Sloan School of Management.
MIT Department
Sloan School of Management
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