Publicação
Online reviews mining to segment and advertise with meaning
| Resumo: | Online reviews have garnered considerable attention in academic research, primarily focused on product-centric studies, with less attention to reviewer-centric approaches. The current study follows a data-driven methodology to highlight how reviewer-centric insights can enhance targeted advertising via Google's advertising platform. As a use case, this study delves into Levi's Amazon customers' traits through their overall purchases on the marketplace. A sample of 1,929 verified unique Levi's Men's 559 Relaxed Straight Jeans reviewers and their respective reviews was collected from Amazon.com. Following data preprocessing, text mining techniques were applied, identifying 17 lifestyle sub-dimensions in online reviews based on the existing Activities, Interests, and Opinions (AIO) lifestyle framework. Subsequently, reviewers were grouped into 6 clusters, according to their lifestyles, and the similarity between each cluster's most prominent lifestyle mean features and existing Google advertising options was assessed. Findings show that 78% of Levi's reviewers disclosed lifestyle traits within their online reviews. Furthermore, research establishes the potential of correlating these traits to 79 unique Google advertising features across the 6 clusters. Each cluster exhibited a range of 8 to 66 distinct Google affinities, highlighting the diversity of lifestyle-related data implications for targeted advertising strategies. These findings provide insights for businesses looking to gain a competitive edge through accuracy in their advertising campaigns. |
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| Autores principais: | Coelho, Inês Moreira Neto |
| Assunto: | Online Reviews Lifestyles Text Mining Clustering Advertising |
| Ano: | 2024 |
| País: | Portugal |
| Tipo de documento: | dissertação de mestrado |
| Tipo de acesso: | acesso embargado |
| Instituição associada: | Universidade Nova de Lisboa |
| Idioma: | inglês |
| Origem: | Repositório Institucional da UNL |
| Resumo: | Online reviews have garnered considerable attention in academic research, primarily focused on product-centric studies, with less attention to reviewer-centric approaches. The current study follows a data-driven methodology to highlight how reviewer-centric insights can enhance targeted advertising via Google's advertising platform. As a use case, this study delves into Levi's Amazon customers' traits through their overall purchases on the marketplace. A sample of 1,929 verified unique Levi's Men's 559 Relaxed Straight Jeans reviewers and their respective reviews was collected from Amazon.com. Following data preprocessing, text mining techniques were applied, identifying 17 lifestyle sub-dimensions in online reviews based on the existing Activities, Interests, and Opinions (AIO) lifestyle framework. Subsequently, reviewers were grouped into 6 clusters, according to their lifestyles, and the similarity between each cluster's most prominent lifestyle mean features and existing Google advertising options was assessed. Findings show that 78% of Levi's reviewers disclosed lifestyle traits within their online reviews. Furthermore, research establishes the potential of correlating these traits to 79 unique Google advertising features across the 6 clusters. Each cluster exhibited a range of 8 to 66 distinct Google affinities, highlighting the diversity of lifestyle-related data implications for targeted advertising strategies. These findings provide insights for businesses looking to gain a competitive edge through accuracy in their advertising campaigns. |
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