Identifying Customer Needs from User-Generated Content
Name
Timoshenko_Hauser Customer Needs from UGC June 2018.pdf
Description
Main article
Size
1.26 MB
Format
Adobe PDF
Checksum (MD5)
92b0ff7fb5555f7e645afd7a67ec9917
Author(s)
Timoshenko, Artem
Hauser, John R
Date Issued
July 2018
Journal
Marketing Science
Publisher
Elsevier BV
Citation
Timoshenko, Artem and John R. Hauser. "Identifying Customer Needs from User-Generated Content" (July 2018): 2985759
Version
Author's final manuscript
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 non-informative 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 non-informative 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 dataset 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, than 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
MIT Department
Sloan School of Management
Terms of Use
Creative Commons Attribution-Noncommercial-Share Alike
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DOI of Published Version
https://doi.org/10.2139/ssrn.2985759
