Best practices: how do succesfull brands build their size chart strategy.
I. Size chart: From Visual Element to Decision System
For many online merchants, the size chart is treated as a compact visual element designed to display as much information as possible, whereas leading fashion businesses treat sizing as a foundational operational system.
After eight years building sizing systems alongside some of the most demanding fashion houses globally, one pattern is consistent: brands with disciplined measurement guides experience less sizing ambiguity, more stable conversion behavior, and tighter control over returns management.
Ambiguous or complex size charts create interpretative work for the shopper. Interpretative work creates hesitation. Hesitation reduces conversion and increases the probability of returns.
Sizing is therefore not a content block. It is a cognitive interface.
Wether you are a scaling brand—or established brands transitioning to Shopify— when it comes to sizing, the important questions is not how much information I can give to my shopper, but, will my size chart help my shopper choosing the right size!
II. Strategic Foundations: Body vs. Product Dimensions & Range vs. Exact Values
The most coherent sizing systems are built around a consistent fit model, where each size corresponds to a defined body profile. Size variations are then introduced through controlled ease—not by redefining the size itself. This structure makes body-based size charts predictable and easy for shoppers to understand.
However, many brands operate with inherited inconsistencies across designers, factories or seasons. The good news is that clarity can be restored.
Here are five principal that High-performing brands apply to their sizing logic. These are not stylistic preferences, they are efficiency decisions.
1. Anchor the System in Body Dimensions
For most categories, body measurements provide the clearest decision framework.
Body-based size charts answer the question shoppers naturally ask:
“Which size corresponds to my body?”
Chest, waist, hips, and height are human reference points. They do not require interpretation.
Garment measurements, by contrast, introduce an additional step:
- Is the measurement flat?
- Do I double it?
- Is ease included?
- How much oversize is intentional?
Every additional mental step increases cognitive load.
When the size chart reflects body targets—especially when the collection is designed around a consistent fit model—the reading becomes straightforward. Bias is reduced. The shopper compares themselves directly to the brand’s intended proportions.
This alignment supports efficient size recommendation logic and clearer decision-making.
2. Use Product Measurements Selectively
Product (garment) measurements are appropriate when structural deviation exists:
- Tailored or rigid outerwear
- Extreme oversize silhouettes
- SKUs with documented fit inconsistency
However, best practice is precise:
- Show only front-of-garment dimensions
- Measure flat on a table
- Provide exact values
This format is reproducible. A shopper can measure a similar item at home and compare.
But remember to keep the information digestible, publishing the full internal pattern measurements, curved seams, or multi-point garment specs does not increase clarity. It increases interpretative bias.
3. Use Ranges for Body Measurements
Exact single-point body values create exclusion.
Very few shoppers match a single measurement perfectly across chest, waist, and hips simultaneously. Non-overlapping ranges—e.g., 89–93 cm—allow for natural human variance without compromising grading logic.
Ranges reduce hesitation because the shopper sees themselves clearly within a bracket, rather than slightly outside a threshold.
Less friction. Fewer doubts.
4. Use Exact Values for Product Measurements
Garment measurements represent physical facts. They should be exact.
A chest width of 56 cm is 56 cm.
Precision supports comparability. Ranges would introduce ambiguity in this context.
5. Minimize Non-Decision Data
High-performing brands are disciplined about what they exclude.
For example:
- Armhole depth
- Shoulder slope
- Sleeve opening
- Knee circumference
- Internal construction details
These measurements are essential for production. They are rarely essential for purchase decisions.
Removing non-decision data reduces reading bias and speeds comprehension.
The outcome is not simplification for its own sake. It is cognitive efficiency.
III. What This Looks Like in Practice: Structured Data by Category
Below are baseline examples reflecting high-performing implementation standards.
Upper Body (T-Shirts, Shirts, Knitwear)
Body-Based Size Chart Example (cm)
| Size | EU | Chest (cm) | Waist (cm) |
|---|---|---|---|
| S | 46 | 88–92 | 74–78 |
| M | 48 | 92–96 | 78–82 |
| L | 50 | 96–100 | 82–86 |
| XL | 52 | 100–104 | 86–90 |
Necessary: Chest, Waist
Useful (for some SKUs): Hips, Height, Arm’s length
Avoid: Back length, Armhole depth, Upper Arm Circumference, Pocket opening,
Size equivalence should reflect the primary market (e.g., EU ↔ UK ↔ US), not every global system.
Lower Body (Trousers, Denim)
Body-Based Size Chart Example (cm)
| Size | EU | Waist (cm) | Hips (cm) | Inseam (cm) |
|---|---|---|---|---|
| 30 | 46 | 76–80 | 92–96 | 80 |
| 32 | 48 | 80–84 | 96–100 | 82 |
| 34 | 50 | 84–88 | 100–104 | 84 |
| 36 | 52 | 88–92 | 104–108 | 86 |
Necessary: Waist, Hips
Useful: Length (inseam),
Not Necessary (unless category-specific): Thigh width, knee width, Leg Opening
Footwear
Footwear Size Chart Example
| EU | US | Foot Length (cm) |
|---|---|---|
| 40 | 7 | 25.0 |
| 41 | 8 | 26.0 |
| 42 | 9 | 27.0 |
| 43 | 10 | 28.0 |
Necessary: Internal foot length
Strongly Recommended: Market size equivalence
Not Necessary: Width, unless explicitly produced as wide/narrow variants
Foot length remains the most universal and least biased measurement across markets.
IV. Impact on P&L: Why Structural Clarity Matters
Clearer size charts do not merely improve UX.
They influence the economics of the business.
When a measurement guide reduces cognitive load:
- Shoppers understand the product faster.
- Conversion increases because hesitation decreases.
- Size-related returns decline.
- Exchange volume stabilizes.
- Logistics strain reduces.
For scaling Shopify brands, this translates into measurable margin impact. Reverse logistics costs shrink. Customer support tickets decrease. Inventory planning becomes more predictable.
Over time, consistency compounds. Shoppers learn their size within the brand. Repeat purchases accelerate because sizing uncertainty disappears.
The measurement guide becomes part of customer retention.
Clear structure. Clear understanding. Clearer P&L outcomes.