Profiling the AI speaker user: Machine learning insights into consumer adoption patterns.
The objective of this study is to identify the characteristics of users of AI speakers and predict potential consumers, with the aim of supporting effective advertising and marketing strategies in the fast-evolving media technology landscape. To do so, our analysis employs decision trees, random for...
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| Main Authors: | , |
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| Format: | Article |
| Language: | English |
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Public Library of Science (PLoS)
2024-01-01
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| Series: | PLoS ONE |
| Online Access: | https://doi.org/10.1371/journal.pone.0315540 |
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| _version_ | 1850033627195244544 |
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| author | Yunwoo Choi Changjun Lee |
| author_facet | Yunwoo Choi Changjun Lee |
| author_sort | Yunwoo Choi |
| collection | DOAJ |
| description | The objective of this study is to identify the characteristics of users of AI speakers and predict potential consumers, with the aim of supporting effective advertising and marketing strategies in the fast-evolving media technology landscape. To do so, our analysis employs decision trees, random forests, support vector machines, artificial neural networks, and XGboost, which are typical machine learning techniques for classification and leverages the 2019 Media & Consumer Research survey data from the Korea Broadcasting and Advertising Corporation (N = 3,922). The final XGboost model, which performed the best among the other machine learning models, specifically forecasts individuals aged 45-50 and 60-65, who are active on social networking platforms and have a preference for varied programming content, as the most likely future users. Additionally, the model reveals their distinct lifestyle patterns, such as higher internet usage during weekdays and increased cable TV viewership on weekends, along with a better understanding of 5G technology. This pioneering effort in IoT consumer research employs advanced machine learning to not just predict, but intricately profile potential AI speaker consumers. It elucidates critical factors influencing technology uptake, including media consumption habits, attitudes, values, and leisure activities, providing valuable insights for creating focused and effective advertising and marketing strategies. |
| format | Article |
| id | doaj-art-903bde416e814192b515caf8c359df89 |
| institution | DOAJ |
| issn | 1932-6203 |
| language | English |
| publishDate | 2024-01-01 |
| publisher | Public Library of Science (PLoS) |
| record_format | Article |
| series | PLoS ONE |
| spelling | doaj-art-903bde416e814192b515caf8c359df892025-08-20T02:58:08ZengPublic Library of Science (PLoS)PLoS ONE1932-62032024-01-011912e031554010.1371/journal.pone.0315540Profiling the AI speaker user: Machine learning insights into consumer adoption patterns.Yunwoo ChoiChangjun LeeThe objective of this study is to identify the characteristics of users of AI speakers and predict potential consumers, with the aim of supporting effective advertising and marketing strategies in the fast-evolving media technology landscape. To do so, our analysis employs decision trees, random forests, support vector machines, artificial neural networks, and XGboost, which are typical machine learning techniques for classification and leverages the 2019 Media & Consumer Research survey data from the Korea Broadcasting and Advertising Corporation (N = 3,922). The final XGboost model, which performed the best among the other machine learning models, specifically forecasts individuals aged 45-50 and 60-65, who are active on social networking platforms and have a preference for varied programming content, as the most likely future users. Additionally, the model reveals their distinct lifestyle patterns, such as higher internet usage during weekdays and increased cable TV viewership on weekends, along with a better understanding of 5G technology. This pioneering effort in IoT consumer research employs advanced machine learning to not just predict, but intricately profile potential AI speaker consumers. It elucidates critical factors influencing technology uptake, including media consumption habits, attitudes, values, and leisure activities, providing valuable insights for creating focused and effective advertising and marketing strategies.https://doi.org/10.1371/journal.pone.0315540 |
| spellingShingle | Yunwoo Choi Changjun Lee Profiling the AI speaker user: Machine learning insights into consumer adoption patterns. PLoS ONE |
| title | Profiling the AI speaker user: Machine learning insights into consumer adoption patterns. |
| title_full | Profiling the AI speaker user: Machine learning insights into consumer adoption patterns. |
| title_fullStr | Profiling the AI speaker user: Machine learning insights into consumer adoption patterns. |
| title_full_unstemmed | Profiling the AI speaker user: Machine learning insights into consumer adoption patterns. |
| title_short | Profiling the AI speaker user: Machine learning insights into consumer adoption patterns. |
| title_sort | profiling the ai speaker user machine learning insights into consumer adoption patterns |
| url | https://doi.org/10.1371/journal.pone.0315540 |
| work_keys_str_mv | AT yunwoochoi profilingtheaispeakerusermachinelearninginsightsintoconsumeradoptionpatterns AT changjunlee profilingtheaispeakerusermachinelearninginsightsintoconsumeradoptionpatterns |