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Fashion Brand Sales Forecasting Analysis Using ARDL Time Series Model -Focusing on Brand and Advertising Endorser's Web Search Volume, Information Amount, and Brand Promotion-

ARDL 시계열 모형을 활용한 패션 브랜드의 매출 예측 분석 -패션 브랜드와 광고모델의 웹 검색량, 정보량, 가격할인 프로모션을 중심으로-

  • Seo, Jooyeon (Dept. of Fashion Industry, Ewha Womans University) ;
  • Kim, Hyojung (Dept. of Fashion Industry, Ewha Womans University) ;
  • Park, Minjung (Dept. of Fashion Industry, Ewha Womans University)
  • 서주연 (이화여자대학교 의류산업학과) ;
  • 김효정 (이화여자대학교 의류산업학과) ;
  • 박민정 (이화여자대학교 의류산업학과)
  • Received : 2022.05.26
  • Accepted : 2022.08.02
  • Published : 2022.10.31

Abstract

Fashion companies are using a big data approach as a key strategic analysis to predict and forecast sales. This study investigated the effectiveness of the past sales, web search volume, information amount, brand promotion, and the advertising endorser on the sales forecasting model. The study conducted the autoregressive distributed lag (ARDL) time series model using the internal and external social big data of a national fashion brand. Results indicated that the brand's past sales, search volume, promotion, and amount of advertising endorser information amount significantly affected the sales forecast, whereas the brand's advertising endorser search volume and information amount did not significantly influence the sales forecast. Moreover, the brand's promotion had the highest correlation with sales forecasting. This study adds to information-searching behavior theory by measuring consumers' brand involvement. Last, this study provides digital marketers with implications for developing profitable marketing strategies on the basis of consumers' interest in the brand and advertising endorser.

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