Somewhere right now, a seller is staring at a 28% comeback rate and panic-Googling “return rate benchmark India” — and another seller in a different category is celebrating 18% without realising it is terrible for what they sell. Benchmarks without context don't reassure; they mislead. This post gives you the honest version: bands instead of a magic number, the COD effect that bends every comparison, and the measurement method that makes your own rate worth comparing at all.
One disclaimer up front, because honesty is the whole point of this post: marketplaces do not publish category-wise return rates. Every band below is an illustrative range, consistent with what sellers we've sat with at 5–25 orders/day on AJIO and Meesho report. Treat them as orientation, not gospel — and treat anyone quoting a precise industry-wide figure with suspicion.
Why do bands beat single numbers? Because a return rate is not one phenomenon — it is the sum of two different ones. RTO (the parcel never delivered) reflects your buyer geography, COD share, and dispatch speed. Customer returns (delivered, then sent back) reflect your listings, sizing, and product truth. Two stores can share a 25% total with completely different compositions and therefore completely different problems. Any benchmark that doesn't acknowledge the split — and almost none you'll find online do — is averaging two unrelated diseases into one symptom.
The bands: a return rate benchmark by category
Fashion is the highest-return category in e-commerce everywhere, and India adds its own multipliers: COD, first-generation online buyers, and a price segment where ordering two sizes to keep one is rational behaviour. Within fashion, the gradient follows fit sensitivity: the more precisely a garment must fit a specific body, the more often it comes back.
- Sarees and unstitched fabric — roughly 10–20% total comebacks. No fit problem to have. Comebacks skew toward colour-versus-photo disappointment and RTO.
- Men's casual wear — roughly 15–28%. Loose fits forgive sizing errors; buyers are less likely to order multiples.
- Kurtis and women's ethnic — roughly 18–32%. The big Meesho/AJIO middle. Semi-fitted, photo-sensitive, heavily COD.
- Western women's wear — roughly 22–38%. Fit expectations are tighter and trend cycles are shorter.
- Fitted garments and footwear — roughly 25–45%. Jeans, blazers, shapewear, shoes. Order-two-keep-one behaviour lives here, and a rate that would be alarming for sarees is simply the cost of the category.
Price point moves you inside each band, and not in the direction intuition suggests. Very cheap items (under ₹300) often come back less via customer returns — the reverse trip isn't worth the buyer's effort — but bounce more as RTO, because refusing a casual ₹250 punt costs nothing. Mid-range items collect the most deliberate returns: the buyer cares enough to send back what disappoints. Premium items in the marketplace context tend to return less again, because the buyer who spent ₹1,500 usually researched first. None of this is destiny, but it explains why two sellers in the same category band can sit at opposite ends of it with identical operational discipline.
The COD effect: why your mix bends every band
Two stores sell the same kurtis at the same price. Store A is 85% COD; store B pushed prepaid hard and sits at 50% COD. Store A's blended comeback rate can run ten points higher than store B's with identical products, identical photos, identical sizing — because COD changes the buyer's exit cost. A prepaid buyer who half-regrets an order has money committed and usually accepts delivery, deciding afterwards. A COD buyer's cheapest exit is the doorstep refusal, which lands on you as an RTO with shipping burned both ways.
The crucial detail: COD inflates the comeback number mostly through RTO, not customer returns. Post-delivery return behaviour differs far less between payment types, because by then the buyer has engaged with the product either way. Which means a COD-heavy store comparing its blended rate against any benchmark — or against a prepaid-heavy competitor — is comparing apples to small trucks.
Before comparing against anyone, then, normalise for your own mix. Compute your two rates separately for COD and prepaid orders — four numbers instead of one. If your COD RTO rate sits inside a sane band but your blended number looks scary, the scary part is your payment mix, which is mostly your category talking, not your operations failing. If your prepaid RTO rate is also high, that points somewhere more specific: addresses, dispatch delays, or courier coverage in your buyer geography, because prepaid buyers rarely refuse what they have already paid for.
Prepaid share is slowly rising across Indian e-commerce as UPI habits deepen, which will drag blended comeback rates down over the coming years without any seller doing anything. That is worth remembering when you compare your current number against a figure someone quoted from 2023, or when next year's rate looks better and you feel tempted to credit your own genius. Benchmarks age; the measurement method below doesn't.
How to measure your return rate so it means something
Before any comparison, your own number has to be built correctly. Three rules do all the work.
Rule 1 — split the rate. RTO and customer returns are different events with different causes (we've covered the full split elsewhere). A 28% blended rate that is 22% RTO is an address-and-COD problem; the same 28% at 6% RTO is a listing-truth problem. The blend hides the diagnosis.
Rule 2 — use the right denominators. RTO rate is RTOs ÷ shipped orders. Customer-return rate is customer returns ÷ delivered orders. Dividing customer returns by shipped orders understates the problem, because the orders that never arrived could never be returned. Small distortion at 5% RTO; serious at 25%.
Rule 3 — assign comebacks to the shipping cohort. A return that lands today belongs to the week its order shipped, not to this week. Returns trail shipment by one to three weeks, so a growing store's “this month's returns ÷ this month's orders” always flatters itself — today's big denominator absorbs comebacks from last month's smaller one. Cohort accounting is the difference between a rate and an illusion.
Building a baseline you can defend
Benchmarks orient you; baselines run your business. Here is the honest build sequence. Weeks one to four: measure the two rates per weekly shipping cohort and resist judging anything — the cohorts aren't mature and the numbers will wobble. Weeks five to twelve: cohorts from the first month are now 30+ days old and fully baked; start recording them as your baseline series. From week twelve onward you have eight-plus mature cohorts, which is enough to distinguish a real shift from noise: a single bad week means nothing, three consecutive cohorts moving the same direction means something.
Watch out for the seasonal distortion that wrecks naive baselines. Sale events change both the numerator and the denominator: order volume spikes with first-time impulse buyers (higher refusal rates, higher size-gamble returns), and the returns wave from a sale lands two to three weeks after the revenue did. A store that looks brilliant during sale week and terrible three weeks later hasn't changed — it is watching one cohort's delayed exhaust. Tag sale cohorts separately and compare like with like: festival cohorts against festival cohorts, steady-state against steady-state.
When to worry — and when not to
With clean measurement and the bands above, the diagnosis gets simple. You are fine if you sit in the lower half of your category band with a COD-heavy mix — spend your energy growing. You should investigate if you are in the top quarter of the band: pull the comebacks by SKU and pin code, because concentration is the usual story — two SKUs with a fit problem, three pin codes with a refusal habit. You have a fire if you are above the band on the customer-return side specifically, because that is buyers telling you the listing is writing cheques the product doesn't cash.
Whatever the diagnosis, attach rupees before reacting. A 30% comeback rate on a ₹250-margin SKU and the same rate on a ₹80-margin SKU are different emergencies — the second one may already be selling at a loss once the returns tax is counted. The benchmark question (“am I normal?”) is really a profit question wearing a percentage costume, and the profit answer sometimes contradicts the percentage one: a high-return SKU with fat margins can be worth keeping, while a “well-behaved” SKU at 12% returns and razor margins quietly isn't.
Where Robnu fits
Everything in this post is measurement discipline, and measurement discipline is what software is for. Robnu — the agentic OMS for AJIO and Meesho sellers — classifies every comeback as RTO or customer return automatically, assigns it to its shipping cohort, and keeps both rates on the operations dashboard next to the rupee cost each one carries. The SKU and pin-code concentration views are one click deep, so “investigate” takes minutes instead of a spreadsheet weekend.
Robnu is free for everyone right now — every feature, every order, no card, no trial timer. When paid pricing eventually launches, sellers under 25 orders/day stay free forever. Start by measuring this week's cohort properly; in a month you'll have a baseline worth trusting, which is more than most of your competitors will ever have.