Return Rate and Reverse Logistics
Date: 2026-08-16
Returns are a margin line, not a customer service line. A returned order costs more than the revenue it reverses, and most analytics never deducts them — so reported revenue only ever goes up.
What it is
Return rate is the proportion of orders or units sent back. Reverse logistics is the cost of handling them.
The cost of one returned £50 order:
revenue reversed −£50.00
outbound delivery (unrecoverable) −£3.00
return postage (if free) −£3.00
inspection and restocking −£2.00
payment processing (often not refunded) −£0.30
────────
−£58.30
plus: goods returned to stock at reduced
value, or written off entirely
A return costs more than the sale was worth. The order contributed £15; reversing it costs £15 plus £8.30 of unrecoverable handling.
What it does to unit economics
Return rate applies as a haircut to every downstream number:
5% returns 30% returns
orders 15,000 15,000
returns 750 4,500
net orders 14,250 10,500
contribution (net × £15) £213,750 £157,500
− return handling −£6,225 −£37,350
───────── ─────────
net contribution £207,525 £120,150
Same traffic, same conversion, 42% less contribution. Two categories with identical gross margin are entirely different businesses at those rates.
Which means: contribution margin must be computed net of expected returns, per category. A blended figure hides that the high-return category is barely profitable — Contribution Margin, Basket Composition.
Where CRO makes it worse
The connection that gets missed. Conversion tactics can raise returns, and the test that measured conversion won’t see it:
- Optimistic imagery or sizing that oversells
- Reduced friction at checkout removing hesitation that was doing useful filtering
- Free returns promoted prominently — raises conversion, raises returns, and the second lands weeks later
- Discounting, which brings lower-intent buyers with higher return propensity
- Bracketing — customers deliberately ordering multiple sizes to return most of them
Return rate belongs as a guardrail on any test touching product pages, sizing or checkout — and it needs a longer window than the test, because returns arrive after the return period — Guardrail Metrics, Leading and Lagging Indicators.
That lag is the structural problem: a test finishes, ships as a winner, and the return consequence arrives a month later attributed to nothing.
Reducing it
Ordered by effect:
- Better sizing information. Fit guides, per-product measurements, review-based fit signals. The largest lever in apparel by a distance
- More and better imagery, including scale and detail. Most returns are “not as expected”
- Accurate descriptions, resisting the pull towards flattering copy
- Record the reason. A structured return reason at the point of return is the highest-value data you’re probably not collecting — it points directly at which products and which content are causing it
- Address the outliers. Return rate is heavily skewed by product; a handful of lines usually account for a disproportionate share, and fixing or delisting them is quick
Measuring it
- By product and category, never blended. The blended figure hides everything actionable
- By acquisition channel, since return propensity varies by how the customer arrived
- As a cohort metric, because returns lag orders — a rising return rate on this month’s orders won’t be visible for weeks
- Deducted from revenue in analytics. Most implementations never fire a refund event, so analytics revenue is overstated by the return rate permanently — Revenue Metrics, Ecommerce Event Schema
That last point is worth acting on: if you fire no refund event, every revenue figure you report is wrong by your return rate, and nobody will ever notice from inside the analytics.