In this Q&A with Morgan Spaulding, Data Analyst at WGSN, we explore our Shows data, the first of our five data sources, and why this is key in our trend forecasting process.
Q: What do you do at WGSN?
My role at WGSN is to report on product mix changes and performance, emerging trends, styles and colours each season. These are data used in many of our report types, including Retail Analysis, Intelligence, Buyers’ Briefing and Debriefs. During catwalk season, I mainly focus on Catwalk Analytics reports, which use data to highlight the key trends, category product mix, designs, silhouettes, fabrics, colour and prints from the latest catwalk season.
Q: What role does catwalk (“Show”) data play in your trend forecasting process?
Show data plays a large role in helping clients understand and implement the upcoming trends in their next-season assortment plans. The data can help pinpoint trends that have been growing year-on-year (YoY) for clients to explore and which categories to lean into.
Q: What’s in your catwalk library?
The WGSN catwalk library comprises three million catwalk images and over a decade worth of catwalk images from more than 120 designers across New York, London, Milan and Paris. This includes main and pre-season collections. The images are tagged with their silhouettes, designs, fabrics, sleeve and neckline details.
Q: How do you analyse catwalk data? How do catwalk images help you predict future trends?
The Image team tags each photo from the catwalk by tagging the silhouette, details and embellishments, lengths, sleeves, fabric, colour, print and pattern at the garment level.
The tags are quantified to provide an overview YoY, which means I can see there are 25 blazer jackets in A/W 21/22 vs 75 blazer jackets in A/W 22/23. I can see a 200% increase in blazer jackets YoY, for example. If this were the case, clients could think about how a blazer works in their upcoming assortments and which silhouettes would work best for their end consumer.
Q: How do you track colour trends?
We pair our catwalk data with AI social data to help us detect and track colour trends. The AI detects each apparel garment in a photo and extracts its dominant colour(s), print/pattern and other key attributes. It can identify a blue dress, a red top and black bottoms, for example. The AI can even determine the tone, whether it’s bright, pastel, dark or medium.
Q: How can merchandisers and buyers use your insights to plan ahead?
Let’s take a specific trend example. As demonstrated by the chart below, ombré/tie-dye was the fastest growing print in S/S 22, but only 7% of styles on the catwalk were ombré/tie-dye. Using this data, we signalled to our clients that while ombré/tie-dye is a trending print to lean into, to refrain from making the majority of their inventory in the tie-dye print since it has a smaller presence on the catwalks.
WGSN Catwalk data
Another example is dresses declining during the peak of the pandemic. The penetration of dresses on the catwalks decreased, as tops and bottoms increased their share of the apparel mix. This is in line with what we saw in the e-commerce apparel market. However, as we move towards the post-pandemic era, we saw in the most recent season that dresses on the catwalks are upticking – again following suit with the e-commerce apparel market.
Catwalks can give a glimpse of what is important, desirable and sellable to the end consumer at any point in time. Consumers purchased knit tops and bottoms during the pandemic and there was a surge of these items on the catwalks. Now, as dresses uptick on the A/W 22/23 catwalks and in e-commerce, there is demand again as the world reopens. These are invaluable insights that buyers and merchandisers can use to plan ahead for the coming seasons.
WGSN Catwalk data
For more information on TrendCurve+ and our data-driven forecasting, please visit our website or request a demo here.