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

Translated title of the contribution: 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-

Jooyeon Seo, Hyojung Kim, Minjung Park

Research output: Contribution to journalArticlepeer-review

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.

Translated title of the contributionFashion Brand Sales Forecasting Analysis Using ARDL Time Series Model -Focusing on Brand and Advertising Endorser's Web Search Volume, Information Amount, and Brand Promotion-
Original languageKorean
Pages (from-to)868-889
Number of pages22
JournalJournal of the Korean Society of Clothing and Textiles
Volume46
Issue number5
DOIs
StatePublished - 2022

Bibliographical note

Publisher Copyright:
© 2022, The Korean Society of Clothing and Textiles. All rights reserved.

Keywords

  • Advertising endorser
  • Ardl time series analysis
  • Information amount
  • Information-searching behavior
  • Web search volume

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