Our client, one of the biggest global fashion retailers, was over-indexing in a number of womenswear categories, including dresses, shirts and jumpsuits in 2020. It was also under-indexed versus the market in high-waist jeans, flare dresses and knit tops.
Using the WGSN TrendCurve AI model, we took one of its areas of concern, the dresses category, and demonstrated how we were able to predict the decline in dresses and de-risk the category by focusing on the right trends.
Using data from the TrendCurve AI model, we looked at overall dynamics for the dresses category for our client, and analysed how the size of their assortment compared to their competitor set and the market.
Drilling into the key trends shifting within the dresses category, we highlighted opportunities to pull-back from certain styles and where to invest in others. Analysis from social media and catwalk data gave further insight into when and how the specific trends would resonate with their target consumer groups.
Results
Based on this analysis, WGSN made four key recommendations:
Decrease share of dresses
We recommended this action as the pandemic had prompted change to the womenswear assortment and hit high-performing categories like dresses. Consumers had shifted attention to cut and sew, active and loungewear.
Pull back on puff sleeves
Puff sleeves have reached critical mass, so we recommended that our client reduce the breadth and depth of buy as the trend slowed down. We suggested focus should be on a curated range and considered iterations.
Rebalance the dress mix
We recommended a move towards flared and any-occassion dress styles. We suggested design with seasonless and multi-use versatility in mind to tap into cost-per-wear value and recessionary spend.
Invest in loungewear and leggings
Seasonless categories that focus on home living, health and comfort gain traction as lockdown fuels activewear interest and the rise of comfortwear. We recommended that our client develop differentiated product for this rapidly growing category.
