Identifying Customer Needs from User-Generated Content

Artem Timoshenko () and John R. Hauser ()
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Artem Timoshenko: MIT Sloan School of Management, Massachusetts Institute of Technology, Cambridge, Massachusetts 02139
John R. Hauser: MIT Sloan School of Management, Massachusetts Institute of Technology, Cambridge, Massachusetts 02139

Marketing Science, 2019, vol. 38, issue 1, 1-20

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 (search for similar items in EconPapers)
Date: 2019
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Citations: View citations in EconPapers (79)

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