We all know, women sizes differs between brands, models and countries. Men had the war to tha k for standardize sizing due to uniform requirement. Need a pair of pants? Hip size and leg lenght is all you need! For wonen though, it depends wich country, company, cut of pants. You’vegot hips sizing, waist size, various non descriptive leg lenght ( Regular, for short or tall legs), etc. I think it is just tedious to shop for clothing at this point. What’s yourtakeon a solution to simplify all this spaguetti of size clothing?
I’ve always suspected places do this. My body fits well in River Island, Ted Baker, Yumi, a few others, but really badly in Prada, Topshop, Mulberry, anything that expects a woman to be more straight. You have to learn “your shops”. But the sizes are still wild even within brands.
Yeah. It is a Buyer’s nightmare too. If anyone tried to produce something that covered all sizes and body types, it would look even worse than Walmart generic men’s clothing.
I Buyer’s real goal is to minimize overburden and keep turning inventory as fast as possible. The easiest way to illustrate this is if you are stocking something simple like socks. The noob mistake is to order something like XS-2, S-4, M-6, L-6, XL-4. A week after stocking this you’ll have something like: XS-2, S-3, M-1, L-0, XL-3. The remaining inventory is overburden and a major problem. It won’t sell well and the center/majority of the customer base is angry they found something they like but not in their size. The better order would be something like: XS-2, S-3, M-15, L-20, XL-3. This requires data to plan for well, but is a realistic order I might have placed.
The more broad the size range gets, or the more trendy/bold the style, the more risk there is in tying up cash flow. If no one likes the product it is trouble. If you’re stuck with oddballs it’s trouble. When I am spending hundreds of thousands of dollars at a time, I am very conservative and I want as much historical data as possible. I would much rather see something like sales figures over the last 10 years show shorter middle aged women have consistently purchased a certain fit, in a certain size range, at a certain time of year, in muted darker color tones. There is a lot of risk when I start trying to fit a broad range of demographics against data that appears to show VERY specific buying choices.
Mind you, I am applying my experience with a chain of high end bicycle shops to something like a department store. Most retail is the same though, and I carried women’s clothing at a small scale of a few racks worth. Most buying happens 6 months to a year in advance and it involves spending a ton of money at one time. It is always something of a conservative gamble about trends, styles, and very local demographics. One really bad decision will cost you your job. It is quite agonizing at times. The farther from center a customer gets, the more they fall outside of the available data a buyer has available. This is why most retail seems clickish or type oriented. Overburden inventory and bad cash flow is what kills most retail businesses.
Like with bike shops, you won’t find many shops that sell high end bikes that stay open for more than a decade unless they are a hobby business owned by someone with enough money to not care about profitability. Overburden kills bike shops consistently. With 3 stores I spent between 1.3-1.5 million dollars in preseason orders total for the 6 years I was the Buyer. It is fun, but it is stressful to show you what you want to see, things that are interesting but not quite right, and always have the thing you really came for, all at the same time, all the time, for the majority of customers.