Hey,
I just finished breaking down the women’s dress market using fresh sold data. And the pattern is clearer than most resellers realize.
Most people comp dresses by brand and stop there.
The data says that is exactly where the mistakes start.
Velocity, price, and timing in this category are being driven by a small set of demand multipliers. When you understand them, the rack starts to look very different.
In the video I walk through three buckets:
• Fast movers that consistently clear inventory
• High ASP brands where patience is the play
• Demand multipliers that completely change the math
One example that surprised me:
Free People sits around 8 percent sell-through at the base level.
But in plus size, that jumps to 33 percent.
Same brand. Completely different outcome.
That gap is where most resellers are either making money or leaving it on the rack.
📊 Download the full research
The spreadsheet includes 70 plus dress brands with:
• Active vs sold counts
• 90 day sell-through
• Average sale prices
• Estimated in-season velocity
• Plus size, material, and length multipliers
Grab it here:
👉 https://docs.google.com/spreadsheets/d/14RabEWLPLpifdAYOT5yC139xF6--T4aFog9CFlQBrow/edit?usp=sharing
If you resell clothing, this is the kind of dataset you want to study.
🔎 Where BrandScout fits
One thing the spreadsheet cannot do is help you in real time at the rack.
That is exactly why I built BrandScout.
When I am thrifting, BrandScout is my first pass filter to quickly answer:
• What am I holding
• Is this version worth comping
• Does this brand usually justify deeper research
Then I confirm with comps and sell-through.
If you want to try it:
It is designed specifically for clothing resellers who want faster, data-informed decisions while sourcing.
Next week I'm pulling Levi’s apart the same way I did dresses. Cut by cut, era by era, with the data behind it.
Talk soon,
Matt