Traditional vs. big-data fashion trend forecasting: an examination using WGSN and EDITED
Date
2020
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Publisher
University of Delaware
Abstract
Traditionally, fashion trend forecasting is conducted through a human-based process that relies heavily on designers’ artistic viewpoints. However, with the emergence of data science and the increasing availability of data inputs from consumers, the possibility of using big data tools to forecast fashion trends is attracting growing interest among the academia and practitioners in the fashion industry. ☐ This study empirically evaluated the similarities and differences of the results of traditional human-based fashion trend forecasts with the ones generated by a big data tool. Based on the comparison of 20 paired fashion trend forecasts for womenswear in the U.S. retail market during the 2018 Spring/Summer Season (S/S 2018) generated by WGSN (i.e., tradtional human-based approach for trend forecasting) and EDITED (i.e., a fashion big data tool) and by using the independent sample t-test, the study finds that: First, WGSN and EDITED were able to generate very similar trend forecasts for the pattern. Second, WGSN and EDITED were able to generate overall similar trend forecasts for the color. Third, the forecast results by WGSN and EDITED for the design details were the least similar statistically. ☐ The findings of this study fulfill a critical research gap regarding the feasibility of using big data for fashion companies’ creative activity. Particularly, the findings suggest the great potential of using big data tools to aid fashion companies’ forecasts and the creation of new products. Additionally, the results of the study significantly increase our knowledge of the benefits and limitations of using big data in fashion forecasting.
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Keywords
Big data, Fashion forecasting, Technology in fashion, Trend forecasting