Optimizing Marketing Strategy by Predicting Fear of Missing Out: Machine Learning Approach
Abstract
Fear of Missing Out (FoMO) is one of the most popular concepts recently due to the ease of access to social media. This concept surprisingly contributed significantly to purchase intentions and customer decision-making. Thus, this study aimed to classify FoMO classes based on the factors that influence them. The data in this study was analyzed using a machine learning predictive classification model, Naïve Bayes, utilizing RapidMiner. Based on these research findings, gender and educational level had almost no influence, age group had a weak influence, daily social media use duration and peer comparison had a moderate influence, and the most used social media platform had a strong influence on FoMO. Accordingly, it was found that individuals ranging from 36 to 45 years old, with longer durations of daily social media use and moderate peer comparison frequency, using TikTok and Facebook as platforms, are more likely to be classified within a group experiencing FoMO. These findings differed from other studies, as no other study discussed the factors of FoMO feelings from a holistic view, and there was a lack of studies utilizing machine learning. By understanding this concept, marketers could gain a better view of how to categorize the FoMO feelings of their customers and use this knowledge to develop effective digital marketing strategy that could enhance customer purchase intention.
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DOI: http://dx.doi.org/10.33087/ekonomis.v9i1.2326
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