Why You're Buying Too Many XLs

Insights from Mary Wiegand, Founder & CEO of Boon — a demand planning and inventory management consultancy specializing in fashion and apparel brands.

There's a buy decision that looks completely reasonable on paper and almost never works in practice: splitting your order evenly across sizes.

Equal units in XS, S, M, L, XL, XXL. Full representation across the range. No customer left out.

It feels logical. It's a nearly guaranteed mismatch between what you bought and how your customers actually shop.

What happens next is predictable, and it plays out the same way across categories, price points, and brand sizes: mediums and larges blow out in the first two weeks. XLs sit. The customers who wear a medium hit your site and find "sold out." The customers who wear an XL find plenty of availability — at the end of the season, when you're clearing it at 40% off.

You've simultaneously run out of your fastest-moving sizes and overinvested in your slowest ones. And because apparel buys are often placed nine to twelve months in advance, there's limited opportunity to course-correct mid-season.

This is the size curve problem. It's one of the most preventable sources of margin erosion in apparel, and it starts with a buy room assumption that nobody has explicitly challenged.

Why the Even Split Feels Right

The logic behind equal size distribution isn't irrational. It comes from a reasonable place: you want every customer to be able to find their size. You don't want to signal that you've deprioritized any part of your customer base. And if you don't have strong historical data yet, an even split feels like the safest neutral position.

The problem is that customers don't buy in even distributions. They never have. The demand curve for most apparel categories is weighted toward the middle of the size range — with mediums and larges typically representing a disproportionate share of total units sold. The exact shape varies by brand, category, and customer demographics, but the structural pattern is consistent: an even buy will always leave you with too much on the tails and not enough in the core.

The first time most founders see this clearly is when someone pulls their actual sell-through data and lays it out visually — season over season, size by size. Seeing the pattern quantified across multiple seasons makes it undeniable in a way that gut feel never quite achieves. The founder already knew the XLs weren't moving. But seeing it in the data, consistently, across three years, is a different kind of knowing. That's usually the moment the conversation about size curves starts in earnest.

What a Size Curve Actually Is

A size curve is simply a percentage-based allocation that tells you how to distribute your total buy across sizes — based on how your customers actually purchase, not on equal representation.

Instead of buying 20% in each of five sizes, a size curve might look like this:

The curve concentrates units where demand actually is, rather than spreading them uniformly across where demand might theoretically be.

Applied to a 500-unit buy, the even split produces 100 units in every size. The size curve produces 140 units in M, 130 units in L, and 40 units in XS — matching the shape of actual customer demand rather than an optimistic assumption about it.

The difference in outcome: fewer stockouts on the sizes that drive most of your revenue, less clearance inventory on the sizes that don't.

How to Build Your Size Curve

If you have historical data:

Pull at least two to three seasons of sell-through by size for each major style or category. Calculate what percentage of total units sold fell into each size. That percentage distribution is your starting size curve.

A few things to look for as you build it:

Look at sell-through rate by size, not just units sold. If you bought 200 mediums and 200 XLs but sold 95% of the mediums and 40% of the XLs, the curve isn't just telling you mediums outsell XLs — it's telling you XLs are being overordered relative to demand. Adjust the curve down on XL, not just in absolute units but as a share of the total buy.

Check whether the curve shifts across categories. Your tops curve may look different from your bottoms curve. Your swimwear may have a different distribution than your cover-ups. Build category-specific curves rather than applying one universal curve across the whole line.

Watch for channel differences if you sell in multiple places. Your DTC customer may skew differently than your wholesale accounts or your retail doors. If you have enough data to segment, do it.

If you don't have enough history yet:

Start with category benchmarks. Apparel industry data, conversations with your buyers or wholesale partners, or size distribution guidance from your production partner can give you a starting point. Treat it as a hypothesis, not a fact. Buy to the benchmark, track your first season's sell-through carefully by size, and update the curve before the next buy.

The curve you build in your first season will be imprecise. The curve you build in your third season — after three iterations of comparing your assumption to what actually happened — will be materially more accurate. The brands that get good at size planning aren't the ones with the best instincts. They're the ones that built the feedback loop.

The Curve Is a Living Input, Not a One-Time Decision

This is the part that brands most often miss once they've built their first size curve: it doesn't stay fixed.

The curve you set at the beginning of the season was your best estimate of demand distribution at the time of the buy. By the time four weeks of sell-through data are in, you have real signal. If mediums are running 30% faster than your curve projected and XLs are running slower, your replenishment order — if you have one — should reflect that reality, not your original assumption.

The buy room curve and the replenishment curve are different decisions made at different points in the season, with different amounts of information. Treat them that way. Don't let the original assumption anchor a reorder decision when actual data is telling you something different.

You Don't Have to Buy Every Size Every Time

There's a tendency in apparel buying to feel like every size needs to be represented in every order. Leaving a size at zero on a PO feels wrong — like you're abandoning a customer segment or exposing yourself to a gap.

This is worth pushing back on.

If you have sufficient on-hand inventory of XL to carry through to end of season based on your expected sell-through, there's no reason to add more XL units on a replenishment order — even if you're buying additional mediums and larges. Buying three core sizes and skipping the tails when you already have the tails covered is a legitimate planning decision that reduces unnecessary inventory without creating a stockout risk.

The goal isn't even coverage across sizes on every order. The goal is having the right units available to customers at the right time. Sometimes that means skipping a size on a given order because you already have what you need.

What the Size Curve Problem Actually Costs

The direct cost is visible in your end-of-season inventory: XLs and XXLs going to markdown while mediums sold out weeks earlier.

The less visible cost is what the stockout on core sizes actually lost you. A customer who comes to your site looking for a medium and finds it sold out doesn't just miss that purchase, they may go to a competitor, may not come back, and in a word-of-mouth driven brand, may tell others. Stockouts on your best sizes aren't just lost revenue on a single transaction. They're a customer experience failure that compounds.

The markdown on the oversize inventory has a direct margin impact, but it also signals to the customer that the brand has availability problems — which creates its own long-term perception issue.

Both costs are avoidable with a size curve that's built from data, updated as the season progresses, and applied consistently to every buy decision.

A Note on Color and Size Together

Size planning and color planning interact in ways that matter.

If you're buying a style in three colorways and applying a size curve, you need to decide whether to apply the same curve uniformly across all colors or to vary it. In most cases, a uniform curve is the right starting point — there's rarely enough color-specific size data to justify separate curves by colorway.

But watch for one specific pattern: if a particular colorway is trending significantly above plan, its size stockouts will hit earlier and harder than your aggregate curve predicts. When a color is running hot, check its by-size sell-through specifically, not just the style average. A medium that's three weeks from stockout on the aggregate level might already be sold out in your top colorway.

Starting Points for This Season

If your current buy plan uses an even size split, here are the three things to do before the next PO goes out:

Pull your historical sell-through by size. Even one season of data is enough to start building a curve. If you have three seasons, you have enough to build a reliable one.

Calculate your actual sell-through rate by size, not just units sold. The rate tells you where you're overbuying relative to demand, which is as important as where you're underbuying.

Apply the curve to your next buy and track what happens. The first season you run with a deliberate size curve, you'll likely still have some mismatch — the curve is a better guess, not a perfect one. Track the results, update the curve, and apply the improved version next season.

The brands that stop leaving money on the table from size mistakes aren't doing something complicated. They pulled their own data, looked at what it told them, and stopped buying on instinct when evidence was available.

About the Contributor

Mary Wiegand is the Founder & CEO of Boon, a demand planning and inventory management consultancy that works with fashion, apparel, and lifestyle brands. Mary specializes in helping founder-led brands build the planning infrastructure to make smarter buy decisions — before the PO goes out.

Want to know if your size buy is costing you margin?

Izba works with apparel and consumer brands to build planning processes grounded in data. We start with a demand planning audit where we'll look at your current buy logic and tell you where the gaps are.

Book your audit at izba.co