How to Reduce Markdowns With Better Fit Data

Markdowns and Returns Share a Root Cause

Markdowns feel like a separate problem from returns, but they often share a root cause: making and buying the wrong sizes. When you produce too many of the sizes your customers don't need and too few of the ones they do, the surplus sizes pile up and get marked down at the end of the season, while the right sizes sell out early. Both the discount spiral and the size-related return are symptoms of a size-demand mismatch. Fit data is the cure for both, and this guide focuses on the markdown side. Here's how.

How Size Mismatches Create Markdowns

The mechanism is straightforward and costly:

• Wrong size curve — you buy size depth based on a generic curve or last year's guess, not your real customers' bodies.
• Surplus in the wrong sizes — the sizes few of your customers wear don't sell through.
• Stockouts in the right sizes — popular sizes sell out, losing sales you can't recover.
• End-of-season markdowns — the unsold surplus sizes get discounted, eroding margin.

You lose at both ends: lost full-price sales on stockouts and lost margin on marked-down surplus. The culprit is buying size depth blind.

The Problem With Generic Size Curves

Most brands buy size depth using an industry-standard curve or historical habit, assuming their customers match a generic distribution. They rarely do — your real audience has its own body-shape profile, the "Mirror Effect" in The Mirror Effect. Buying to a generic curve systematically over-produces some sizes and under-produces others, baking in the markdown problem before the season even starts.

Use Aggregate Fit Data to Buy the Right Depth

Aggregate fit data reveals your customers' real size demand — the actual distribution of bodies engaging with your products, the inventory logic in How Fit Data Improves Inventory Planning. Buying size depth to match that real distribution, rather than a generic curve, reduces both stockouts in popular sizes and surplus in unpopular ones. Better size-curve buying is one of the most direct ways fit data cuts markdowns.

Fix Grading to Improve Sell-Through

Markdowns also come from sizes that fit poorly and therefore don't sell or come back. If a size at the edge of your range fits badly because of linear grading, it under-sells and ends up discounted, the grading issue in What Is Grading in Clothing. Using fit data to grade properly across the range means more sizes fit well, sell through, and stay out of the markdown bin. Good fit is good sell-through.

Reduce Returns to Reduce Markdowns

Returns feed the markdown problem directly: a returned garment often can't be resold at full price and ends up discounted, the depreciation cost in How to Calculate the True Cost of Reverse Logistics. So every fit-related return you prevent is also a markdown you avoid — the returned-and-discounted item never happens. Reducing returns and reducing markdowns are the same project viewed from two angles, both solved by getting fit right.

Plan by Category and Market

Size demand varies by category and by market, so refine your buying accordingly. The size curve that fits your dress customers may differ from your denim customers, and your customers in one region may skew differently than another, the localization point in fit data. Using fit data to plan size depth by segment sharpens the buy further and cuts the segment-specific surplus that drives markdowns.

Make Fit Data a Buying Input, Not an Afterthought

The strategic shift is treating fit data as a standard input to merchandising and buying decisions, alongside trend and sales forecasts. When buyers plan size depth using real customer body data rather than a generic curve, the whole organization stops baking markdowns into the buy. Fit data turns size buying from a guess into a decision, and that decision is where markdowns are won or lost.

Connect Your Buying and Fit Teams

The reason fit data doesn't reduce markdowns at most brands is organizational: the team that buys size depth and the team that sees fit and returns data rarely talk. Buyers plan the size curve months ahead using trend instinct and last year's numbers, while the fit and returns insight that should inform it lives in a different system and a different team. The data exists; it just never reaches the decision.

Bridge that gap deliberately. Give your buying and merchandising teams direct access to fit and returns data, and make customer body distribution a standard input to size-curve planning. Establish a regular handoff where fit insight — which sizes are in real demand, which products run small, where grading fails — flows into the buy. This cross-functional connection is what turns fit data from a returns-team metric into a buying decision that prevents markdowns. The brands that cut markdowns with fit data aren't the ones with the most data; they're the ones whose buyers actually use it. Breaking the silo between who has the fit insight and who makes the buying decision is the unlock, and it costs coordination rather than capital.

Where YourSizer Fits

YourSizer helps shoppers find the right size and generates the aggregate fit data — your customers' real body distribution and where products run small or large — that lets you buy the right size depth and grade better. The result is fewer stockouts, less surplus, and fewer markdowns, alongside fewer returns. See how it works for brands, the analytics view, or pricing.

Sources

• McKinsey markdown analysis
• BoF inventory management

Frequently Asked Questions

How does fit data help reduce markdowns?

Markdowns often come from buying the wrong size depth — too many of the sizes customers don't need and too few of the ones they do. Aggregate fit data reveals your customers' real size demand, so you can buy depth that matches it, reducing both stockouts in popular sizes and the surplus that gets marked down.

Why do generic size curves cause markdowns?

Because they assume your customers match an industry-average body distribution, but your real audience has its own profile. Buying to a generic curve systematically over-produces some sizes and under-produces others, creating surplus that gets discounted and stockouts that lose full-price sales.

Are markdowns and returns related?

Yes — they share the root cause of making the wrong sizes, and they reinforce each other. A returned garment often can't be resold at full price and becomes a markdown, so every fit-related return you prevent is also a markdown you avoid. Getting fit right reduces both.