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This paper aims to explore factors contributing to music popularity using machine learning approaches.

A dataset comprising 204,853 songs from Spotify was used for analysis. The popularity of a song was predicted using predictive machine learning models, with the results showing the superiority of the random forest model across key performance metrics.

The analysis identifies crucial genre and audio features influencing music popularity. Additionally, genre specific analysis reveals that the impact of music features on music popularity varies across different genres.

The findings offer valuable insights for music artists, digital marketers and music platform researchers to understand and focus on the most impactful music features that drive the success of digital music, to devise more targeted marketing strategies and tactics based on popularity predictions, and more effectively capitalize on popular songs in this digital streaming age.

While previous research has explored different factors that may contribute to the popularity of music, this study makes a pioneering effort as the first to consider the intricate interplay between genre and audio features in predicting digital music popularity.

Recent years have witnessed a rise in interest in neuromarketing from academia and industry, as it offers practical tools for determining consumers' subconscious reactions to marketing stimuli. Despite this, the current state of neuromarketing research is not well supported by empirical data. To offer a thorough overview of the studies conducted on this discipline in the past few years, a bibliometric analysis of neuromarketing is carried out, taking into account its techniques, key areas and publication patterns trends from several viewpoints.

This study searched 463 documents for the web of science databases published during the previous sixteen years and visualized them. The graphical display of data was created using the VOS Viewer software.

Electroencephalogram (EEG) appeared as a predominantly tool used in neuromarketing research. EEG is either used alone or together with Human Eye-Tracking (HET). “Emotions” was identified in the study as a crucial area of neuromarketing, among other pertinent concepts. The study's results also showed that authors from the United States produced the most articles on neuromarketing, followed by those from the United Kingdom and Spain. The publishing trend, sources and major contributors in neuromarketing are identified using Web of Science data from 2006 to 2021. Overall, the research provides insight into neuromarketing's past, present and future as well as the most widely utilized analytical techniques.

The study's conclusions will be of interest to researchers in understanding the journals that publish neuromarketing research, the themes that contributors and writers have identified, and the countries where research is carried out. This is the first comprehensive study, to the authors' knowledge, that provides a general summary of the key trends in neuromarketing research throughout its history. To the authors' knowledge, this is the first thorough study that offers a broad overview of the most important developments in neuromarketing research from 2006 to 2021.