Exploitation of Social Network Data for Forecasting Garment Sales

Giri, Chandadevi; Thomassey, Sébastien; Zeng, Xianyi

doi:10.2991/ijcis.d.191109.001

Cited by 9 publications

(4 citation statements)

References 30 publications

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“…With the increasing use of social media platforms in promoting Lolita fashion products, it is evident that relying on decision trees for future and present the sales volume prediction can lead the business to wrong decisions. While the model may only function with simple variables, it becomes unfit for use in the fashion industry, whereby a study [19] noted that the higher the visibility in social media, the higher the data and, hence, enhanced accuracy of sales data prediction. In essence, the precision of the prediction is defined by the availability of a broad range of social media data.…”

Section: Accuracy Of Sales Prediction Models and Implicationsmentioning

confidence: 99%

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

Chaing¹,

Rau²,

Shiang³

et al. 2021

Preprint

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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.

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Section: Accuracy Of Sales Prediction Models and Implicationsmentioning

confidence: 99%

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

Chaing¹,

Rau²,

Shiang³

et al. 2021

Preprint

View full text Add to dashboard Cite

show abstract

“…In January 2021, 4.2bn people worldwide were a user of a social networking site (SNS), an increase of 490m (13%) compared with the same period the previous year; this equates to more than 53% of the global population (Kemp, 2021a). User participation in online communities on popular SNSs-such as Facebook, Twitter and Instagram (Forssell, 2016;Giri et al, 2019;Teo et al, 2019)-is gradually increasing (Brambilla et al, 2021), and these communities are becoming integrated into people's lives (Nickerson, 2019). Because of the increasing diversity of SNSs, not all SNSs are likely to survive in the long term (Agag and El-Masry, 2016); only a limited number of online communities succeed over time (Zorina and Karanasios, 2021).…”

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Effects of member similarity on group norm conformity, group identity and social participation in the context of social networking sites

Wang

Liao

2023

INTR

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PurposeFacilitating members' continual participation in a community is crucial for ensuring the community's long-term survival. However, knowledge regarding whether member similarity is related to member participation and the mechanism underlying this relationship is limited. Drawing on similarity–attraction, social exchange and social identity theories, this study explored the influences of different facets of similarity (i.e. value, personality and goal similarity) on group norm conformity, group identity and social participation.Design/methodology/approachData were collected from 444 Taiwanese members of social networking sites (SNSs), and structural equation modeling was employed to examine the hypothesized relationships.FindingsThe results revealed that value similarity directly affected group norm conformity but did not directly affect group identity; personality similarity influenced group identity but not group norm conformity. Goal similarity had positive influences on group norm conformity and group identity. Moreover, group norm conformity had direct and positive influences on group identity and social participation; group identity also had a positive influence on social participation.Originality/valueOn the basis of the aforementioned findings, this study contributes to the understanding of factors facilitating SNS members' participation from the perspective of similarity. These findings can serve as a reference for SNS administrators to facilitate social participation by emphasizing member similarity.

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“…Most of the existing research on fashion supply chain management is devoted to developing advanced models for improving the forecast of demand for fashion apparel items [4,5]. Accurately predicting future sales and demand for fashion products remains a central problem in both industry and academia.…”

Section: Introductionmentioning

confidence: 99%

Deep Learning for Demand Forecasting in the Fashion and Apparel Retail Industry

Giri

Chen

2022

Forecasting

Self Cite

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Compared to other industries, fashion apparel retail faces many challenges in predicting future demand for its products with a high degree of precision. Fashion products’ short life cycle, insufficient historical information, highly uncertain market demand, and periodic seasonal trends necessitate the use of models that can contribute to the efficient forecasting of products’ sales and demand. Many researchers have tried to address this problem using conventional forecasting models that predict future demands using historical sales information. While these models predict product demand with fair to moderate accuracy based on previously sold stock, they cannot fully be used for predicting future demands due to the transient behaviour of the fashion industry. This paper proposes an intelligent forecasting system that combines image feature attributes of clothes along with its sales data to predict future demands. The data used for this empirical study is from a European fashion retailer, and it mainly contains sales information on apparel items and their images. The proposed forecast model is built using machine learning and deep learning techniques, which extract essential features of the product images. The model predicts weekly sales of new fashion apparel by finding its best match in the clusters of products that we created using machine learning clustering based on products’ sales profiles and image similarity. The results demonstrated that the performance of our proposed forecast model on the tested or test items is promising, and this model could be effectively used to solve forecasting problems.

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Exploitation of Social Network Data for Forecasting Garment Sales

Cited by 9 publications

References 30 publications

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

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

Effects of member similarity on group norm conformity, group identity and social participation in the context of social networking sites

Deep Learning for Demand Forecasting in the Fashion and Apparel Retail Industry

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