The size run question is one of the most practically consequential decisions in a merch drop — and one of the hardest to get right without historical data. Order too many larges and you sell out of mediums. Order too many smalls and you're left with a pile of XLs that never move. Without the purchase history that established brands have, you're making an educated guess. Here's how to make that guess as educated as possible.
Start with what you know about your audience
Even without purchase data, you know things about your audience that are useful for size prediction. Age distribution, gender distribution, and community context all provide meaningful priors.
Instagram analytics, YouTube demographics, and podcast listener data (if available) can tell you the age and gender breakdown of your audience. This is imperfect proxy data for clothing size, but it's better than nothing. A primarily female audience in the 25–35 age range in Australia will have a different size distribution to a primarily male audience in the same age range.
Community context matters too. A fitness community will skew toward more fitted sizes and lower average body weight than a general interest community. A food and hospitality community may skew toward more average sizing. These are generalisations, but they're useful ones when you're making decisions with no better data available.
Default size curves for AU/NZ
In the absence of audience-specific data, the following size curves provide a reasonable starting point for Australian and New Zealand creator merch audiences:
Unisex tee or sweat, mixed-gender general audience:
- XS: 5%
- S: 20%
- M: 30%
- L: 25%
- XL: 15%
- 2XL: 5%
Unisex tee or sweat, predominantly female audience:
- XS: 10%
- S: 30%
- M: 30%
- L: 20%
- XL: 8%
- 2XL: 2%
Unisex tee or sweat, predominantly male audience:
- XS: 2%
- S: 10%
- M: 30%
- L: 35%
- XL: 18%
- 2XL: 5%
These are starting points, not rules. Adjust based on what you know about your specific audience.
The fit factor
Fit specification dramatically affects size distribution. A product described as "oversized — size down for a fitted look" will see more sales in smaller sizes as buyers intentionally order their usual size or below. A product described as "slim fit — size up for comfort" will see more large and XL sales as buyers accommodate.
Communicate fit clearly before the drop opens and account for the skew in your size run. If your product is an oversized fit, your S demand will be higher and your XL demand lower than a standard-fit equivalent.
The pre-order solution
The cleanest solution to size run uncertainty is a pre-order model that collects sizes before production is committed. This converts size prediction from a guess to a known quantity. The practical limitations (extended timeline to buyers, operational complexity) are real but often worth it for first drops where uncertainty is highest.
Even a partial pre-order — opening a two-day size registration window (no payment required) to gauge size demand before ordering — provides useful data without the full commitment of a pre-order model. The registration data tells you the size distribution of interested buyers. You may choose to order slightly more than registered (to cover additional buyers who didn't register) but with a much more accurate baseline.
Buffer stock and sell-through targets
Order a buffer of 10–15% above your target sell-through quantity in your middle sizes (S, M, L). These are the sizes that move fastest and that you're most at risk of selling out of before the drop window closes. Having a small buffer in these sizes is worth the inventory cost for the avoid-the-sold-out problem it solves.
For XS and 2XL, order conservatively. These extremes of the size curve are the most likely to have leftover stock. A small quantity — enough to serve the actual demand — without a buffer is the right call for the tail sizes.
Planning your next merch drop? Subscribe to Retail — Printwear's newsletter for creators, brand founders, and independent labels across Australia and New Zealand.