How returns rate is measured
Divide the orders returned in a period by the orders placed, and you have the headline figure. The complication is which orders you count. A return arriving this month usually belongs to a sale from an earlier month, so a fast-growing shop that compares returns against current sales will always flatter itself. Matching returns back to the period the order was placed gives an honest number.
The unit matters too. Measured by order count, one returned pair of shoes weighs the same as one returned sofa. Measured by value, the picture changes completely, and it is the value version that tells you what the returns are doing to profit. Most shops need both, plus a breakdown by product and by reason code.
Why returns rate matters
It is the quickest way to find out whether your marketing is describing the product honestly. Advertising that oversells, images that flatter a colour, a size guide that is wrong, a listing that omits a dimension — all of these convert well and come back later as returns. A campaign judged on sales alone can look excellent while destroying margin behind the scenes.
It also changes how you should bid. If one product category is returned far more often than another, the true value of a sale in that category is lower, and any bidding strategy fed with order value rather than net value will overspend on exactly the wrong products. Sending net revenue to the ad platforms, where your setup allows it, corrects that.
Handling returns costs money that never appears in the ad account: return postage, inspection, restocking, and stock that cannot be resold at full price. In Nepal, where reverse logistics outside the main cities are slower and less predictable, that cost is heavier than the same figure would be elsewhere.
Where returns rate goes wrong
The first mistake is treating it as one number. A shop-wide rate hides the handful of products causing most of the problem, and averages across categories that behave nothing like each other. Clothing and electronics do not belong in the same measurement.
The second is treating every return as failure. Some categories require choice at home, and a generous policy is what makes the first purchase possible. Squeezing the rate by making returns difficult usually buys a short-term improvement and a long-term reputation problem, visible in reviews and in repeat purchase.
The third is not collecting reasons. Without a reason code at the point of return, you know that something is wrong but not what, so the fixes are guesses.
What to do about it
Start with the products at the top of the list, read their return reasons, and fix the page rather than the policy: better photographs, real measurements, a size guide built from actual garments, and honest wording about what the product is not. Then check that the ad creative makes the same promise as the page, because a mismatch between the two is one of the more expensive things a conversion rate optimisation review turns up.
Feed the corrected value back into measurement. Report net of returns alongside gross, and watch the refund rate next to it so a rising figure is caught while it is still a product problem rather than a profit one.