Traditional vs. big-data fashion trend forecasting: an examination using WGSN and EDITED

ABSTRACT

This study empirically evaluated the similarities and differences of the results of traditional human-based fashion trend forecasts with the ones generated by big data tools. A comparison of 20 paired fashion trend forecasts for womenswear in the U.S. retail market during the spring 2018 season generated by WGSN and EDITED, which represent the two approaches respectively, was conducted. The results show that while WGSN and EDITED were able to generate very similar trend forecasts for the pattern, followed by the colour, the forecast results for the design details were the least similar statistically. The findings suggest the overall feasibility and the great potential of using big data tools to aid fashion companies’ creation of new products. However, the findings also illustrate the limits of using big data tools for trend forecasting as a creative activity.

KEYWORDS:

• Fashion forecasting
• big data
• design

Disclosure statement

No potential conflict of interest was reported by the author(s).