A cost treated as weather
In several categories returns run at a level that would be treated as a crisis anywhere else in the business, and they are accepted as a cost of trading. The processing, shipping, inspection and markdown on returned goods are all real.
Some of it is genuinely inherent - customers buying two sizes intending to return one. A meaningful share is not, and is caused by something fixable.
What predicts a return
- The product itself - some lines return far more than their category average
- Size and fit, where the product runs differently from the customer's usual
- Customer history - past return rate is a strong signal
- Basket composition, particularly multiple sizes of one item
- Discount depth, where deep discounts attract more speculative buying
- Delivery time, where a long wait increases second thoughts
Product-level patterns are the most actionable. A line returning at several times its category average usually has a specific cause - the colour differs from the photograph, the sizing is off, the description is misleading - and that cause is fixable once identified.
Use it to fix causes, not to restrict customers
| Use | Assessment |
|---|---|
| Flag products with unusual return rates for review | Best return on effort |
| Improve sizing guidance on problem lines | Directly reduces the cause |
| Add imagery or detail where descriptions mislead | Directly reduces the cause |
| Plan reverse logistics capacity | Operationally useful |
| Forecast net sales for stock planning | Useful and uncontroversial |
| Restrict or charge high-return customers | High risk to relationships and reputation |
That last row deserves care. Customers with high return rates are frequently high-value customers who buy a lot, and restricting them can cost more than the returns. It also generates the kind of publicity nobody wants.
Returns and demand are different numbers
For planning, what matters is net sales. A line selling a thousand with three hundred coming back needs stock planned on seven hundred, and the returned units re-entering stock have their own timing.
Businesses that forecast gross sales and treat returns as a separate write-off consistently mis-plan. Modelling the return rate per line and the return timing makes the stock position considerably more accurate.
The reason codes are worth fixing
Return reason data is usually poor - a dropdown where 'no longer wanted' absorbs everything because it is the quickest option. That destroys the most useful signal available.
Improving reason capture, even slightly, pays for itself. Fewer options, better wording, and asking at the right moment produce data that identifies fixable causes. Without it you know products come back but not why, which is the question that matters.
A product returning at three times the category average is telling you something specific. The reason codes are how you find out what.