fewer size-related returns
higher conversion rate
of returns are size-related
Why Returns Are a Modeling Problem
Wrong Size Recommendation = Return
If the system recommends "Size M" but M is too tight at the waist, the customer returns it. Statistical systems only recognize the pattern after thousands of returns.
Lack of Transparency = Uncertainty
Customers order multiple sizes because they don't trust the recommendation. This doubles logistics costs and returns.
Generic Data = Generic Errors
Size tables vary between brands and collections. Tools based on them inherit these errors.
How maketribe Reduces Returns
Fit Analysis Instead of Size Recommendation
Customers don't just see "Size M" — they see how M and L fit at the shoulders, chest, waist, and hips. This eliminates uncertainty.
0.59cm Measurement Accuracy
Highest precision on the market means: the right recommendation on the first purchase, not the third.
Real Garment Analysis
Cut, material, and elasticity flow into the calculation. Recommendations are product-specific, not generic.
No Cold Start Problem
Works from day one without historical purchase data. New products and new brands receive precise recommendations immediately.
Returns in fashion e-commerce are not an inevitable evil — they are the result of inaccurate size recommendations. maketribe solves the problem at its root: with physics-based fit calculation, 0.59cm measurement accuracy, and per-body-area analysis that customers understand and trust.