Do these returns justify a size change?
You are interviewing for an apparel merchandising role at a fictional UK online retailer.
The question
The two shipment cohorts are from the same week and have completed the same returns window. Every shipment and return count is a physical unit.
| Tapered | Wide leg | |
|---|---|---|
| Units shipped | 200 | 120 |
| Units returned | 50 | 24 |
The team wants to know whether it should change the size guidance.
“Explain what the return figures support and what you would change after checking the product.”
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A strong answer, step by step
Open each step in turn. This is one strong answer, not the only one.
Step 1: Which style has a higher return rate?
What would you do here? Open the move you’d make.
Compare 25% with 20% and look for the reason
The strong move. Both cohorts had the same time to return. The rate accounts for different shipment counts.
Say tapered returns are more than double because 50 exceeds 24
Tempting, but it misses something. The shipment counts differ. A raw return total overstates the difference in rates.
Change all tapered sizes immediately
Tempting, but it misses something. The overall return count does not establish a size defect.
See how a strong candidate says it
“Tapered returns are 50 out of 200, or 25%. Wide leg is 24 out of 120, or 20%. The gap is five percentage points. I would check reasons and the distribution by size before changing the guide.”
Why it works: The shipment denominators allow a fair comparison. Overall returns cannot identify a fit cause.
Step 2: What does the first fit report suggest?
A report labels 30 tapered and six wide-leg entries as fit-return units. The team has not verified that every entry represents a distinct returned unit.
What would you do here? Open the move you’d make.
Report 60% versus 25% as the share of all shipments
Tempting, but it misses something. Those percentages use returned units as the denominator. They answer how returns are labelled, a different question.
Compare the provisional shipment rates and verify the records
The strong move. That measures the fit burden on shipments and checks the count before the team acts.
Assume every entry means the garment ran small
Tempting, but it misses something. Fit codes can cover different issues and the product still needs a check.
See how a strong candidate says it
“If each entry counts a separate unit, tapered fit returns are 30 out of 200, or 15%. Wide leg is six out of 120, or 5%. I would verify distinct units and compare sizes, washes and lots.”
Why it works: The figures give a provisional fit-return rate over all shipped units.
Step 3: What changes after deduplication?
Twelve of the tapered entries are repeated tags on units already counted. The six wide-leg entries are verified distinct units.
What would you do here? Open the move you’d make.
Deduplicate and compare 9% with 5%
The strong move. This uses verified fit-return units and keeps both shipment cohorts the same.
Subtract the 12 tags from the 200 shipments as well
Tempting, but it misses something. A repeated reason tag does not remove a shipment from the cohort.
Treat 9% as proof that every tapered lot runs small
Tempting, but it misses something. The revised figure identifies a higher rate. It does not locate the issue or establish the cause.
See how a strong candidate says it
“Tapered has 18 distinct fit-return units, or 9% of shipments. Wide leg stays at 5%. The difference is four percentage points. I would investigate which sizes and lots account for it.”
Why it works: Removing duplicate tags corrects the numerator. It leaves the same shipment denominator.
Step 4: What should the team check before changing the guide?
Quality measures ten size L pairs from one tapered wash and lot. Eight waistbands measure 80 cm and two measure 82 cm. The spec is 82 cm with a tolerance of 1 cm either way, using the same measurement method.
What would you do here? Open the move you’d make.
Adjust the size guide for all jeans
Tempting, but it misses something. The inspection covers one tapered wash, lot and size. It cannot establish the measurements of other garments.
Hold the affected lot and verify the scope
The strong move. Eight samples measure below the 81 cm lower limit. Quality can check the affected lot and adjacent stock.
Keep selling the affected stock and rely on size recommendations
Tempting, but it misses something. A recommendation cannot resolve the confirmed difference between the samples and the garment specification.
See how a strong candidate says it
“Eight sampled pairs are below tolerance. I would hold size L from that tapered wash and lot for quality review, confirm the measurement method and check other lots before changing guidance more widely.”
Why it works: The garment check supports action on the sampled lot. The broader range needs its own evidence.
The answer, in short
- Decision
- Use verified return counts and measurements to decide what to change.
- Because
- Customer fit feedback can point to a problem. The product check helps determine its scope.
- What would change it
- Checks on other sizes or lots could expand or narrow the action.
Common traps
- Count each returned unit once.
- Use a physical measurement before changing the scope of a quality action.
Check your answer
- I used a consistent shipment denominator.
- I checked how returns were counted.
- I updated the fit-return rates.
- I kept the response within the evidence.
Last reviewed Nov 11, 2026