# Optimizing size, fit, and inventory to cut returns and waste
A customer orders the same dress in three sizes, keeps one, and ships two back. Multiply that by millions of orders and you have the single most expensive problem in online apparel: returns. In some fashion categories, return rates climb toward 40%, and much of that is driven by one thing: shoppers guessing at fit.
Zalando, Europe's largest online fashion platform, tackled this head on. By building fit prediction into the buying journey (recommending a size based on what actually fit similar bodies), the company reduced size-related returns on participating products. Fewer returns means less shipping, less repackaging, less landfill, and higher margin. This lesson shows you how that machinery works and how to deploy it.
Returns are not free. Each returned garment carries reverse logistics cost (shipping back), inspection labor, repackaging, and markdown risk because a returned item may miss its selling window. Industry estimates put the cost of processing a single return at a meaningful fraction of the item's price, sometimes enough to erase the profit on the sale entirely.
There is also a sustainability cost. A share of returned apparel never gets resold. It is discounted, liquidated, or in some cases destroyed. Cutting returns is therefore both a margin play and a waste-reduction play, which is why it now sits on the CFO's and the Chief Sustainability Officer's desks alike.
The root causes split roughly into two buckets:
AI attacks both.
The most direct way to recommend size is to know the body. Several approaches exist, ranging from low friction to high accuracy:
The catch: body data is sensitive personal data. Under the EU's General Data Protection Regulation (GDPR), and depending on interpretation, body scans can qualify as biometric data requiring explicit consent and careful handling. Build privacy in from day one: minimize what you store, get clear consent, and let users delete their profile.
Most large platforms do not rely on body scans alone. The workhorse is a recommendation engine trained on purchase and return history. The core insight: you learn a garment's true fit from how it behaved on thousands of past customers.
Zalando's approach, built partly on its acquisition of the fit specialist Fits.me and later research teams, models both the customer and the article in a shared "size space." If customers who bought Article A and kept it also tend to keep Article B in the same size, the system infers those articles fit similarly.
A simplified version of the logic:
# Pseudo-logic for a size recommendation signal
# For a given customer + candidate article:
def recommend_size(customer, article, purchase_history):
# 1. Estimate customer's latent "true size" from kept items
true_size = infer_true_size(purchase_history) # items kept, not returned
# 2. Estimate this article's fit bias (runs small / large / true)
fit_bias = article.fit_offset # learned from aggregate return reasons
# 3. Adjust and score each available size
scores = {}
for size in article.available_sizes:
expected_fit = true_size + fit_bias - size_to_scale(size)
scores[size] = probability_of_keep(expected_fit)
return max(scores, key=scores.get)The critical training signal is the return reason code. When a customer returns an item and selects "too small" or "too large," that label is gold. It tells the model which direction a garment's fit is biased. Retailers that capture granular return reasons train far better fit models than those who only see "returned: yes/no."
Fit prediction reduces returns per order. Allocation reduces a different waste: stock in the wrong place.
Classic buying orders each style in a fixed size curve (say 10% XS, 20% S, 30% M, 25% L, 15% XL) applied uniformly. The problem: a beach store in one region sells a different size mix than a city store in another. Uniform curves guarantee you sell out of the popular middle sizes while XS and XXL sit unsold, get marked down, and eventually become waste.
Demand-driven allocation uses local sales history to forecast size demand per store or per fulfillment node, then allocates stock to match. The payoff:
For deeper reading on how forecasting and allocation connect, McKinsey's State of Fashion reports track how leading players are operationalizing this.
🎬 [VIDEO: "How Zalando Uses Machine Learning" — youtube.com — Zalando engineers explain size recommendation and personalization at scale]
Technology is the easy half. Here is where deployments succeed or fail.
Coverage matters more than accuracy. A brilliant recommendation shown on 5% of products barely moves the return rate. Prioritize getting a decent recommendation on the widest possible catalog. A "runs small, size up" nudge on every product often beats a precise scan on a handful.
Placement in the journey. The recommendation must appear at the moment of size selection, not buried in a tab. A single visible line ("We recommend size M based on your profile") drives adoption.
Handle the cold start. New customers and new products have no history. Use self-reported measurements and category-level fit priors until enough data accumulates. New articles can inherit fit bias from similar past styles (same brand, same fabric stretch, same block).
Close the loop with return reasons. Make the return flow capture *why*. Structured reasons ("too tight in shoulders," "length too long") feed the model and, crucially, feed back to design and buying so recurring fit flaws get fixed at the source.
Watch bracketing behavior. Some retailers charge for returns or flag serial bracketers. This is commercially and reputationally delicate. Better fit prediction is the positive-sum move: help people order right the first time rather than penalizing them.
Knowledge check
1. Why are returns considered a fashion-specific crisis rather than just a routine cost of doing business online?
2. A retailer notices that a customer routinely orders the same item in multiple sizes and returns all but one. Which root cause does this behavior illustrate?
3. Why does the lesson frame cutting returns as both a margin play and a waste-reduction play?
4. Select ALL correct answers. Which costs are described as making a single return expensive?
Select all the correct answers.
5. Select ALL correct answers. Which statements accurately reflect how fit prediction reduces returns?
Select all the correct answers.
Do not claim a win from a vanity metric. Track:
Run it as a proper experiment. Show the recommendation to a random test group, withhold it from a control group, and compare. Correlation between "users of the fit tool" and "lower returns" is misleading, because engaged shoppers behave differently anyway. Only a controlled test tells you the true lift.
Every avoided return is roughly one avoided round trip of shipping plus the emissions and packaging that go with it. Every better-allocated size curve means fewer garments marked down to clearance or liquidated. For a mid-size online retailer, shaving even a few points off the size-related return rate can translate into meaningful cost savings and a genuine sustainability story you can report, provided you measure it rigorously rather than estimate it generously.