Identifying Customer Needs from User-Generated Content
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...
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| Other Authors: | |
| Format: | Article |
| Language: | English |
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Institute for Operations Research and the Management Sciences (INFORMS)
2020
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| Online Access: | https://hdl.handle.net/1721.1/124203 |
| _version_ | 1811094285545635840 |
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| author | Timoshenko, Artem Hauser, John R |
| author2 | Sloan School of Management |
| author_facet | Sloan School of Management Timoshenko, Artem Hauser, John R |
| author_sort | Timoshenko, Artem |
| collection | MIT |
| description | 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 |
| first_indexed | 2024-09-23T15:57:36Z |
| format | Article |
| id | mit-1721.1/124203 |
| institution | Massachusetts Institute of Technology |
| language | English |
| last_indexed | 2024-09-23T15:57:36Z |
| publishDate | 2020 |
| publisher | Institute for Operations Research and the Management Sciences (INFORMS) |
| record_format | dspace |
| spelling | mit-1721.1/1242032022-10-02T05:23:03Z Identifying Customer Needs from User-Generated Content Timoshenko, Artem Hauser, John R Sloan School of Management 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 2020-03-23T20:41:51Z 2020-03-23T20:41:51Z 2019-01 2017-04 2020-03-19T16:36:23Z Article http://purl.org/eprint/type/JournalArticle 0732-2399 1526-548X https://hdl.handle.net/1721.1/124203 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 en http://dx.doi.org/10.1287/mksc.2018.1123 Marketing Science Creative Commons Attribution-Noncommercial-Share Alike http://creativecommons.org/licenses/by-nc-sa/4.0/ application/pdf Institute for Operations Research and the Management Sciences (INFORMS) Prof. Hauser |
| spellingShingle | Timoshenko, Artem Hauser, John R Identifying Customer Needs from User-Generated Content |
| title | Identifying Customer Needs from User-Generated Content |
| title_full | Identifying Customer Needs from User-Generated Content |
| title_fullStr | Identifying Customer Needs from User-Generated Content |
| title_full_unstemmed | Identifying Customer Needs from User-Generated Content |
| title_short | Identifying Customer Needs from User-Generated Content |
| title_sort | identifying customer needs from user generated content |
| url | https://hdl.handle.net/1721.1/124203 |
| work_keys_str_mv | AT timoshenkoartem identifyingcustomerneedsfromusergeneratedcontent AT hauserjohnr identifyingcustomerneedsfromusergeneratedcontent |