Prediction of customer engagement behaviour response to marketing posts based on machine learning
With the prevalence of social media platforms, the way customers engage with brands has greatly been altered. Choosing appropriate social media marketing strategies to stimulate customer engagement in different forms is an important issue. In order to better understand customer behaviours in the soc...
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| Main Authors: | , |
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| Format: | Article |
| Language: | English |
| Published: |
Taylor & Francis Group
2021-10-01
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| Series: | Connection Science |
| Subjects: | |
| Online Access: | http://dx.doi.org/10.1080/09540091.2021.1912710 |
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| Summary: | With the prevalence of social media platforms, the way customers engage with brands has greatly been altered. Choosing appropriate social media marketing strategies to stimulate customer engagement in different forms is an important issue. In order to better understand customer behaviours in the social media marketing context, we draw on the Stimulus-Organism-Response theory, and conceptualise and characterise marketing posts from six dimensions to get various features as stimuli, which induce or activate customers’ cognitive and affective states to varying levels, and ultimately lead to different behaviour responses. Machine learning algorithms are applied to the customer engagement behaviour choice prediction when facing marketing posts. It is proved that the post features designed by humans can be used to get good predictions, while the best results are achieved by combining the human-designed features with the high-dimensional features automatically extracted from post texts by the BERT model. Our research provides insights for firms to effectively conduct social media marketing design and customer engagement behaviour choice prediction. |
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| ISSN: | 0954-0091 1360-0494 |