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Identifying Customer Needs from User-Generated Content

Author(s)
Timoshenko, Artem; Hauser, John R
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Abstract
Firms traditionally rely on interviews and focus groups to identify customer needs for marketing strategy and product development. User-generated content (UGC) is a promising alternative source for identifying customer needs. However, established methods are neither efficient nor effective for large UGC corpora because much content is noninformative or repetitive. We propose a machine-learning approach to facilitate qualitative analysis by selecting content for efficient review. We use a convolutional neural network to filter out noninformative content and cluster dense sentence embeddings to avoid sampling repetitive content. We further address two key questions: Are UGC-based customer needs comparable to interview-based customer needs? Do the machine-learning methods improve customer-need identification? These comparisons are enabled by a custom data set of customer needs for oral care products identified by professional analysts using industry-standard experiential interviews. The analysts also coded 12,000 UGC sentences to identify which previously identified customer needs and/or new customer needs were articulated in each sentence. We show that (1) UGC is at least as valuable as a source of customer needs for product development, likely more valuable, compared with conventional methods, and (2) machine-learning methods improve efficiency of identifying customer needs from UGC (unique customer needs per unit of professional services cost). Keywords: customer needs; online reviews; machine learning; voice of the customer; user-generated content; market research; text mining; deep learning; natural language processing
Date issued
2019-01
URI
https://hdl.handle.net/1721.1/124203
Department
Sloan School of Management
Journal
Marketing Science
Publisher
Institute for Operations Research and the Management Sciences (INFORMS)
Citation
Timoshenko, Artem and John R. Hauser. "Identifying Customer Needs from User-Generated Content." Marketing Science 38, 1 (January 2019): 1-192, ii-ii © 2019 INFORMS
Version: Author's final manuscript
ISSN
0732-2399
1526-548X

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