Preprint Article Version 1 Preserved in Portico This version is not peer-reviewed

Social Media Marketing in the Sales Volume Prediction for the Lolita Fashion Brand

Version 1 : Received: 11 November 2021 / Approved: 12 November 2021 / Online: 12 November 2021 (14:54:04 CET)

How to cite: Chaing, T.; Rau, H.; Shiang, J.W.; Chiang, L.J. Social Media Marketing in the Sales Volume Prediction for the Lolita Fashion Brand. Preprints 2021, 2021110227 (doi: 10.20944/preprints202111.0227.v1). Chaing, T.; Rau, H.; Shiang, J.W.; Chiang, L.J. Social Media Marketing in the Sales Volume Prediction for the Lolita Fashion Brand. Preprints 2021, 2021110227 (doi: 10.20944/preprints202111.0227.v1).

Abstract

Despite extensively investigating the impact of social media on fashion products’ marketing, little evidence is available on how the platforms influence sales prediction. Focusing on Lolita fashion, this study investigates the impact of social media marketing on the sales volume prediction of fashion products. Essentially, we analyzed marketing data, including comments, likes, and shares from the Weibo social platform, to forecast future sales, examine how to enhance profit performance, and make production decisions. Using a quantitative approach, we tested three different prediction models, including multiple regression, decision tree, and XGBoost. The results revealed that increasing comments and decreasing the number of likes could significantly improve the sales volumes of Lolita products. In contrast, shares exerted a less significant impact on sales. Regarding prediction models, XGBoost was found to be the best method. In the fashion industry, social media is a useful tool for forecasting market trend. A limitation of this study is that only one social media platform was used to extract data, which might limit the generalization of the findings.

Keywords

Lolita fashion; multiple regression; decision tree; social media; XGBoost

Subject

SOCIAL SCIENCES, Marketing

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