Identifying Customer Needs from User-Generated Content
Name
Timoshenko_Hauser Customer Needs from UGC June 2018.pdf
Description
Accepted version
Size
1.26 MB
Format
Adobe PDF
Checksum (MD5)
32ed353d9e685efc6c937ebfdac7e3b6
Author(s)
Timoshenko, Artem
Hauser, John R
Date Issued
January 2019
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
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
MIT Department
Sloan School of Management
Terms of Use
Creative Commons Attribution-Noncommercial-Share Alike
Persistent DSpace Link
DOI of Published Version
https://doi.org/10.1287/mksc.2018.1123
