A confluence of trends from the runway, street style and social media have created new opportunities and challenges for retailers. A new data-driven platform by consumer trend forecasting firm WGSN aims to make sense of it all.
The London-based company launched WGSN Fashion Buying, a dedicated platform that supports buyers and help them understand product direction, emerging trends, and how deeply to invest in specific styles.
With insights covering pre-planning, development and in-season hindsight, the platform will deliver “future-proof trend decision intelligence and buying-specific forecasts exactly when needed, organized according to the development cycle and customized to category,” WGSN stated.
Monisha Klar, director of fashion intelligence for WGSN, said the platform is unique in how it leverages WGSN’s trend expertise with its proprietary TrendCurve AI predictive analytics.
Whereas other retail analytic platforms use historical information only and usually from a single data source, she said WGSN Fashion Buying combines retail data with social and catwalks data in its AI model.
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“Most retail analytic platforms lean heavily into one facet of the buying profession…either they’re focused on pricing, demand planning, or competitive monitoring. As a team of former buyers, we understand the complexities of the role, the timelines in which a buyer operates, and all the individual steps it takes to bring an assortment to life and trade it effectively,” she said. “We have aligned our content to fit those exact tasks, allowing for buyers to be supported at each milestone by our world-class forecasting methodology.”
At launch, WGSN Fashion Buying will provide data on key categories, including dresses, cut and sew, woven tops, denim, outerwear, trousers and shorts, skirts, accessories and footwear.
The platform introduces four new forecasts. The Trend Narratives forecast reports on key items and colors that help retailers outline seasonal stories and concepts to build out a phasing calendar. TrendCurve AI Color provides a data-backed assessment of color and two-year-out color projection. TrendCurve AI Materials and Details identifies details and their use within a specific category across several retail segments.
The TikTok Trading forecast identifies opportunities for remerchandising existing ranges to optimize sales around emerging and evolving TikTok trends linked to current seasonal trading.
Though WGSN subscribers already have the trend information on aesthetics and the items before those TikTok trend monikers are coined, the WGSN Fashion Buying aims to demystify TikTok’s role in trend forecasting.
“What TikTok is very effective at, is the naming and marketing of trends, but these can vary widely in importance from short-lived viral moments to more trends which evolve over time,” said Francesca Muston, VP of fashion at WGSN. “Not all TikTok trends are fast and viral, and not all of them translate to commercial opportunity. Many of our clients end up chasing after viral TikTok moments, air-freighting new product when they have existing product in their inventory that speaks to that trend.”
In addition to simplify the buying process. WGSN Fashion Buying may help the fashion industry’s waste issue. According to recent WGSN and OC&C research, shifting to a planning and buying model based on demand and data can decrease overproduction by 5-15 percent, removing one of the primary sources of unnecessary waste.
“WGSN has used big data to rewrite the rulebook on how trends are forecasted. The Fashion Buying innovation will supercharge the platform with a data-driven solution with never-before-seen efficiencies that impact the bottom line for the retailers,” Muston said.