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...
| Principais autores: | , |
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| Formato: | Artigo |
| Idioma: | en_US |
| Publicado em: |
Elsevier BV
2019
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| Acesso em linha: | https://hdl.handle.net/1721.1/122049 |
| _version_ | 1826212270081835008 |
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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 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 |
| first_indexed | 2024-09-23T15:19:02Z |
| format | Article |
| id | mit-1721.1/122049 |
| institution | Massachusetts Institute of Technology |
| language | en_US |
| last_indexed | 2024-09-23T15:19:02Z |
| publishDate | 2019 |
| publisher | Elsevier BV |
| record_format | dspace |
| spelling | mit-1721.1/1220492022-09-29T14:11:13Z Identifying Customer Needs from User-Generated Content Timoshenko, Artem Hauser, John R Sloan School of Management Timoshenko, Artem 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 2019-09-10T19:30:34Z 2019-09-10T19:30:34Z 2018-07 Article http://purl.org/eprint/type/JournalArticle 1556-5068 https://hdl.handle.net/1721.1/122049 Timoshenko, Artem and John R. Hauser. "Identifying Customer Needs from User-Generated Content" (July 2018): 2985759 en_US http://dx.doi.org/10.2139/ssrn.2985759 Marketing Science Creative Commons Attribution-Noncommercial-Share Alike http://creativecommons.org/licenses/by-nc-sa/4.0/ application/pdf Elsevier BV 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/1721.1/122049 |
| work_keys_str_mv | AT timoshenkoartem identifyingcustomerneedsfromusergeneratedcontent AT hauserjohnr identifyingcustomerneedsfromusergeneratedcontent |