Launching with support for multiple fashion categories — dresses, cut-and-sewn, woven tops, denim, outerwear, trousers and shorts, skirts, accessories, and footwear — the data-driven platform aims to inform buyers and help them understand product direction, key trends and how deeply to invest in specific styles.
The firm said the platform combines its expertise in trend forecasting with AI prediction. It aims to unite pre-planning, development and in-season insight to ensure accuracy and efficiency in both planning and production.
A demonstration of WGSN ’s new fashion buying platform.
Four new forecasts are offered as part of the fashion-buying platform.
They are trend narratives, TrendCurve AI color, TrendCurve AI Materials & Details, and TikTok trading. They will sit alongside WGSN’s current fashion design reports to help buyers optimize seasonal planning and de-risk decision-making.
Francesca Muston, vice president of fashion at WGSN, said the new offering will “supercharge the platform with a data-driven solution with never-before-seen efficiencies that impact the bottom line for the retailers.”
A recent report by WGSN and OC&C research noted that shifting to a planning and buying model based on demand and data can decrease overproduction by 5 percent to 15 percent, removing one of the primary sources of unnecessary waste.
Monisha Klar, director of fashion at WGSN, believes they are ”uniquely positioned to provide buyers with the confidence and validation they need to back trends from the quantitative side.
“Buyers have been so reliant on their own internal data. But since you don’t have data on items you haven’t sold, there’s also a big element of buyers making gut-instinct decisions, that may or may not pan out,” added Klar.
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