You received 600 units of a linen shirt for an eight-week summer drop. Six weeks in, 440 have sold. Is that good? The merchandiser says yes, the founder looking at the stock room says no, and neither wins the argument because they are not dividing by the same number. The sell-through rate formula is genuinely simple — units sold ÷ units received — but the two definition choices you make before running it swing the answer by more than twenty points. This guide pins down the definitions, works a full eight-week apparel drop, and gets to what people actually want: what a good rate looks like at each stage, and what to do when yours isn't.
Key takeaways
- Sell-Through Rate = Units Sold ÷ Units Received × 100. The number is meaningless without the window and the denominator stated next to it.
- One real drop, four defensible answers: 4.3%, 10.4%, 69.0% and 82.8%. Nothing changed but the definition.
- Healthy for a fashion drop: 45-60% cumulative by week 4, 85-95% by end of season. Evergreen basics run far lower per period — by design.
- Above ~90% inside the first four weeks is not a win. It means you underbought and spent the rest of the window out of stock.
- Sell-through judges a single buy. Inventory turnover judges the whole business. Different questions, different tools.
The formula, and the two forks hidden inside it
Start with the version everyone agrees on:
Received 600, sold 497, sell-through is 82.8%. Fine. The trouble starts the moment two people compute it from the same warehouse and get different answers, which happens constantly, because the formula quietly asks you two questions it doesn't tell you it's asking.
Fork one: period or cumulative?
Period sell-through measures one slice of time — a week, a month — on its own. It is a velocity reading. It tells you how the product is moving right now and it should decline over the life of a drop, because the people who wanted it most bought it first.
Cumulative sell-through — often called season-to-date — measures everything sold since the goods landed. It is a progress bar. It only ever goes up, and it is the number you compare against a target like "80% by end of season."
Both are correct. A weekly period number will always look small beside a season-to-date number for the same product, and reporting one while your buyer assumes the other is how a dying drop gets defended for three extra weeks.
Fork two: units received, or units received plus opening stock?
The strict definition uses units received — the quantity that arrived on this purchase order or production run. That isolates the buy: did this specific decision sell?
The retail-floor definition uses total goods available — units received plus whatever opening stock you already had. That reflects reality: the customer doesn't know which unit came in on which PO, and if you're trying to clear a style, the carryover counts.
Neither is wrong. Mixing them is. The received-only denominator flatters you whenever there is carryover, because it hides units you still have to sell. Pick one convention per report, label it in the column header, and never benchmark a number computed one way against one computed the other.
One drop, four answers
Here is the arithmetic, on a real-shaped example. A womenswear brand drops a linen shirt for an eight-week summer window:
- Units received: 600 (one production run, landed in week 0)
- Opening stock: 120 units of the same style carried over from last summer
- Total goods available: 720 units
Eight weeks of sales, with both readings computed against the received quantity:
| Week | Units sold | Cumulative sold | On hand (end) | Weekly ST | Cumulative ST |
|---|---|---|---|---|---|
| 1 | 132 | 132 | 588 | 22.0% | 22.0% |
| 2 | 96 | 228 | 492 | 16.0% | 38.0% |
| 3 | 71 | 299 | 421 | 11.8% | 49.8% |
| 4 | 58 | 357 | 363 | 9.7% | 59.5% |
| 5 | 44 | 401 | 319 | 7.3% | 66.8% |
| 6 | 39 | 440 | 280 | 6.5% | 73.3% |
| 7 | 31 | 471 | 249 | 5.2% | 78.5% |
| 8 | 26 | 497 | 223 | 4.3% | 82.8% |
Now take the single moment "end of week 8" and run all four definitions against it:
Four numbers, one drop, zero disagreement about the underlying facts. 82.8% reads like a well-judged buy. 69.0% reads like 223 units still sitting in a bin, which is also true and is the number your cash cares about. The 4.3% is not a failure — it is the normal tail of a decaying curve, and reading it as a crisis is how brands panic-discount a drop that was fine.
Practical rule: use cumulative against total goods available when deciding what to do with the remaining stock, and cumulative against units received when grading the buy so the next order is sized better. Read period sell-through weekly, and only ever against the same week of a previous drop.
What a good sell-through rate actually looks like
This is the part everyone wants and almost nobody qualifies properly. A "good" sell-through rate is a function of two things: what kind of product it is, and how far into its selling window you are. A 25% cumulative rate is alarming in week 6 of an eight-week drop and completely healthy for a core basic you replenish every month.
| Product type | First 4 weeks (cumulative) | Mid-window | End of window |
|---|---|---|---|
| Seasonal fashion drop (8-16 weeks) | 45-60% | 70-80% | 85-95% including markdown |
| Evergreen basics (continuous replenishment) | 20-30% | 45-55% | 25-35% per month at steady state |
| Consumables (skincare, food, refills) | 30-45% | 65-75% | 90%+ well before the expiry window |
| High-ticket considered purchase (jewellery, furniture) | 10-20% | 30-40% | 60-75% over 6-12 months |
Treat those as planning heuristics, not benchmarks with a citation behind them. The honest version of this table is the one you build from your own last four to six drops: pull cumulative sell-through at weeks 2, 4 and 8 for every style you have run, and you have a house curve that beats any published figure.
Two structural adjustments before you judge yourself against any of it. First, size and colour curves: a style at 60% overall can be sold out of M and L while XS and XXL sit untouched, and the aggregate number hides it. Read sell-through per variant, not per style. Second, exclude days you were out of stock from any velocity you derive — otherwise you conclude demand was weak when what was weak was availability. Honey Shelf's velocity engine ignores stockout days for exactly this reason.
Reading a low number: three causes, three different fixes
Say you are at 32% cumulative at the end of week 4 against a 50% plan. The metric tells you there is a problem but not which one, and the three usual causes need opposite responses.
You over-ordered
Units are selling at a perfectly normal rate; there are simply too many of them. Check this first, because it is the most common and the least dramatic: compare weekly unit sales against the same week of a comparable past style. If units-per-week look normal and only the percentage looks bad, demand was fine and the buy was too big. The fix is upstream — smaller, more frequent runs next time, not a markdown now. Our guide to balancing stockout risk against overstock covers that ordering discipline.
You mispriced
Traffic to the product page is healthy, add-to-carts are not. That gap is a price signal, or a value-communication signal, long before it is an inventory signal. Test the price on a small window before you commit to a markdown you can't reverse.
You under-marketed
Nobody saw it. Product page sessions are a fraction of what comparable launches got. Check this before you discount: a markdown applied to a traffic problem burns margin without fixing anything.
If none of the three apply and the number stays flat for another month, you are no longer looking at a slow style — you are looking at the start of dead stock, and the calculus shifts from selling it to clearing it before its carrying cost exceeds its recoverable value.
Reading a high number: the expensive kind of good news
A consistently high early sell-through feels like a win and is often the costliest thing on the report. If a style crosses roughly 90% cumulative inside the first four weeks of a window meant to last eight or twelve, you did not sell brilliantly — you ran out.
Take the second colourway of the same linen shirt. You received 200 units and sold 190 by the end of week 3: 95% sell-through, and it looks like the best line in the range. Now price the miss. The primary colourway did 299 of its eventual 497 units — about 60% — in its first three weeks. Apply the same shape to the sold-out colourway and its full-window potential was roughly 190 ÷ 0.60 ≈ 317 units. You sold 190. The 127 units you did not have, at a contribution margin of ₹1,500 each, are about ₹1.9 lakh of margin that walked.
And the margin is the cheap part. Weeks 4 through 8 of a stockout also burn paid traffic sent to an unbuyable page, organic ranking on a product that stops converting, and customers who bought the substitute elsewhere and stayed there. When you review a range, put the styles above 90% early on the same page as the ones below 40% — both are buying errors, and only one is obvious.
Sell-through vs inventory turnover: different questions
These get used interchangeably and they should not be. Sell-through is a batch metric: one receipt, one window, expressed as a percentage of what came in. Inventory turnover is a flow metric: how many times a whole category or business cycles its stock in a period, expressed as a frequency. You need both, for different meetings.
| Sell-through rate | Inventory turnover | |
|---|---|---|
| Question it answers | Did this buy sell? | How hard is my cash working? |
| Formula | Units sold ÷ units received × 100 | COGS ÷ average inventory value |
| Unit | Percentage | Times per year |
| Scope | One style or variant, one receipt, one window | A category or the whole business, over a period |
| Best used for | Reorder calls, markdown timing, sizing the next buy | Cash planning, category comparison, board reporting |
| Blind spot | Says nothing about margin or carrying cost | Averages hide the dead SKU inside a healthy category |
The practical division: sell-through is what you read during a season to decide what to do next week; inventory turnover is what you read after a season to decide how much capital the category deserves next time. Strong sell-through with weak turnover usually means you hold stock too long between buys. Strong turnover with weak style-level sell-through means you are churning cash through products that need markdowns to move.
The week-by-week curve as an early-warning system
The real value of sell-through is not the post-mortem. It is that the curve declares itself early — usually by week 2 — and gives you time to act while acting is still cheap.
Build a planned cumulative curve when you place the order. Front-load it: for an eight-week drop, something like 22% / 38% / 50% / 60% / 67% / 73% / 79% / 83% — the shape of the worked example above. Plot actuals against it weekly. You are not looking for precision, only for divergence.
The reorder cut-off
This is the rule most brands discover a season too late. Your last useful reorder decision happens at window length minus supplier lead time. An eight-week window with a six-week production lead time means the decision must be made by the end of week 2 — anything later arrives when the demand curve has already collapsed and simply converts next season's cash into this season's markdown pile.
Which means week 2 is the meeting that matters. Tracking ahead of the planned curve by 10 points or more at that point is a reorder signal — and the quantity should be sized from current weekly velocity and remaining weeks, the same logic behind reorder point calculations, not from optimism.
The stop-reorder signal
Running behind the planned curve two weeks in a row, by 15 points or more, is a stop signal: cancel the open reorder, hold the second run, and switch the conversation from replenishment to clearance timing. Markdowns taken in week 5 recover far more than the same markdown taken in week 11, when everyone else in the category is also discounting.
None of this needs software to be true — a spreadsheet with a planned curve and eight rows does the job. What software changes is whether anyone updates it in week 2, when the drop is new and nobody is worried yet. Honey Shelf keeps sell-through and days-remaining per variant current from live Shopify sales, so the divergence surfaces on its own.
Where to start
Pick one convention — cumulative sell-through against units received, per variant — and apply it to every style you launched in the past year. Note the week-4 number, sort the list, and two groups appear that you had no language for before: the styles you consistently overbuy, and the ones that sell out early and quietly cost you the most. That sorted list is worth more than any benchmark table, including the one above.