Buyers are the original trend forecasters. Tasked with going to shows and ‘re-sees’ to pick out the pieces they expect to be hits with consumers, part of the job is to anticipate what people will want to buy months before shoppers are able to hit purchase. (Or, at least weeks before pre-order.) Buyers, at their best, curate a compelling selection customers can’t find anywhere else.

So what role should data play? This week, trend forecasting agency WGSN launched the Fashion Buying platform, a data-driven hub that collates intelligence and forecasting, combined with WGSN’s TrendCurve AI predictive analytics (which it has been developing for three years now). The platform aims to assist buyers across the pre-planning, development and in-season hindsight phases.

WGSN’s artificial intelligence prediction tool pulls e-commerce data, which it’s found to be the best predictor of trends, alongside catwalk data and a search and social index. The output of the model is the percentage of the assortment that item or category should represent, forecasted up to two years ahead, explains Francesca Muston, VP of fashion at WGSN.