Sell-through rate: the formula, the denominator trap, and how to read your own number

In October, 77.5% of the candles you had on the shelf sold. That single number — units sold divided by units available, times 100 — is sell-through rate, and it's the most direct answer to the question "did I buy the right amount?" that inventory math produces. Buy too much and sell-through sags; buy too little and it spikes while you lose the sales the extra stock would have made.

This article covers the formula (including a denominator trap that silently changes what you're measuring), a worked monthly example with the arithmetic shown, how sell-through differs from turnover and days of cover, why the benchmarks floating around the internet deserve suspicion, how to use the number for buying and markdown decisions, and a copy-paste spreadsheet version that works as a calculator.

The formula — and the trap hiding in the denominator

Sell-through rate = (units sold ÷ units available) × 100

Simple, except for one word: available. There are two defensible denominators, and picking the wrong one for your situation produces numbers that look fine and mean nothing.

Denominator A: beginning inventory for the period. What you had on the shelf when the period opened. This is the right choice for a steady, replenished SKU — you measure how much of the stock you carried actually moved during the month.

Denominator B: units received in the period. What arrived. This is the right choice for stock you received as a batch — a new product launch, a seasonal buy, a single PO you're tracking to completion. A shipment of 250 hoodies that sold 180 has a sell-through of 180 ÷ 250 = 72% against the buy.

The trap: use "received" on a SKU you replenish weekly and the denominator churns so fast the number is noise. Use "beginning inventory" on a one-time seasonal buy and leftover receipts from the previous period pollute the reading. Decide per SKU based on how you buy it, and stay consistent — that's what makes month-over-month comparisons honest.

Worked monthly example

Your candle SKU CNDL-12, October:

Sell-through = 310 ÷ 400 × 100 = 77.5%

Same store, second SKU — a new hoodie drop that arrived Oct 1:

Sell-through = 145 ÷ 250 × 100 = 58%

The candle's 77.5% against beginning inventory tells you the replenishment cadence is roughly right, maybe conservative. The hoodie's 58% against the buy is the launch tracking number — at one month into a seasonal buy, that's a number to watch, not yet a verdict. Two numbers, two denominators, two different questions answered.

One quick data-hygiene note: sell-through above 100% means you sold more units than you had available — which points to a data problem (miscounted beginning stock), a counting lag, or genuine overselling. The overselling case has its own causes and fixes, covered in why Shopify stores oversell.

Sell-through vs. inventory turnover vs. days of cover

Three metrics, three different questions. Merchants mix them up constantly because they're all "how fast is stock moving" — but they answer at different levels.

Sell-through is a ratio for a period — it needs the period to be over (or nearly over) to mean anything. Days of cover is a snapshot — readable any morning, which is why it's the reorder trigger while sell-through is the buying verdict. Turnover is the finance-lens cousin of sell-through, computed in dollars across the catalog rather than units per SKU. The full metric set, with formulas and what each one hides, is in the inventory KPIs guide; days of cover has its own worked example in the days of cover guide.

Why you should ignore most published benchmarks

Search "sell through rate benchmark" and you'll find confident numbers — including from sources that disagree with themselves. Shopify's own guide on sell-through rate (shopify.com/blog/sell-through-rate, accessed Oct 2026) states "the benchmark STR is at or above 80%" in one section, then "a healthy STR is in the 70%–80% range" in another, then illustrates category benchmarks like fragrance at ~23% after eight weeks and home improvement at ~55% in the same window. That's not sloppy editing — it's an accidental confession that no single number survives contact with different categories, price bands, and cadences.

Category and price band. Fresh or trend-driven goods are bought to sell through almost completely — a grocery-adjacent SKU at 70% in a month might be undersupplied. Durable goods with long shelf lives are bought to sit. A $24 candle and a $240 furniture piece cannot share a benchmark; their buys are structured differently by design.

Replenishment cadence — the sneaky one. A SKU you replenish weekly will show high sell-through against beginning inventory partly because you deliberately hold only a few days of stock. A SKU you buy quarterly will show lower sell-through partly because you deliberately bought a quarter's worth. Same operational health, opposite numbers. This is why a store that "improves" its sell-through by ordering more often hasn't necessarily improved anything.

The honest alternative: set your own baseline. Your last 2–3 months are the only benchmark group that shares your category, price band, and cadence:

1. Compute monthly sell-through per SKU for the last two or three months (method below).
2. For each SKU, take the median of those months. That median is the baseline.
3. Define a normal band of roughly ±10 points around it. Above the band: you're likely under-stocked or under-buying — check stockout days before celebrating. Below the band for two consecutive months: the SKU is slowing, and the intervention ladder in the slow-moving inventory guide applies.

This takes one spreadsheet afternoon and produces numbers that actually mean something in your store. One caveat that matters: a sell-through reading taken during a period when the SKU was out of stock for part of the month is supply-limited — it says "you sold everything you had," not "demand was that size." If you don't track out-of-stock days, some of your baseline is poisoned. (This is exactly why InventoryX keeps a per-SKU out-of-stock ledger next to velocity — knowing which months were supply-constrained is the difference between a real baseline and a flattering one.)

Using sell-through for buying and markdown decisions

The number earns its keep at the edges of its range, when you pair it with cover:

• High sell-through + low days of cover → reorder. The SKU proved demand and you're about to prove a stockout. The reorder quantity math belongs to the reorder point formula — sell-through told you the buy was right; the reorder point tells you the buy was too small.
• High sell-through + healthy cover → buy more next cycle. Demand is real and you can afford to size up the next PO deliberately rather than chasing it.
• Low sell-through, first month → investigate, don't act. Check for supply limits, a thin promo month, or new-competition noise.
• Low sell-through, two to three consecutive months → markdown or stop reordering. This is the early-warning trigger. The decision framework for which intervention fits lives in the slow-mover guide linked above; sell-through is what promoted the SKU onto that list.

Timing rule of thumb for markdowns on batch buys: when a seasonal buy's cumulative sell-through is well behind your own baseline at the point where roughly half the season is gone, the remaining inventory needs an exit plan before the season ends — not after.

Where to get the numbers in Shopify

Be precise about what's native, because this is where most implementations quietly stall:

• Units sold: yes. Shopify's sales reports give you units sold by product and variant for any period, exportable to CSV (Shopify Help Center, accessed Oct 2026).
• A native sell-through report: partially. Shopify ships a Product sell-through rate report (Shopify's blog documents it — shopify.com/blog/sell-through-rate, accessed Oct 2026), which is handy for a quick read. But it computes the ratio on Shopify's chosen denominator and period — it won't let you run the two-denominator discipline from earlier in this article, track a batch buy across multiple months, or hold your own median baseline.
• Units received per period: no native report. As of October 2026, Shopify doesn't ship a received-units-per-period report; you tally receipts from your purchase orders and receiving records. For batch buys this is easy — the PO is the receipt. For continuously replenished SKUs, keep a receipts column in the snapshot sheet.

The upshot: sell-through on Shopify is a 15-minute spreadsheet habit, not something you'll fully automate natively — and even if a report hands you the ratio, the baseline and denominator decisions stay yours.

Copy-paste spreadsheet walkthrough

One row per SKU per month. Set up columns like this:

Formulas for row 2:

• E2: =B2+C2-D2 (ending units, if you're not entering it manually)
• F2: =D2/B2 — format as percentage. This is your standard monthly reading.
• G2: =IF(C2=0,"",D2/C2) — the batch-buy reading; blank when nothing was received, so it doesn't fill with #DIV/0!.

For a launch SKU tracked across multiple months, add a cumulative column: H2: =SUM($D$2:D2)/SUM($C$2:C2) — copied down, this gives sell-through-to-date against the total buy, which is the number you watch across a season.

Then the baseline: one row per SKU with =MEDIAN(F2:F4) across your last three months, and your band is that median ± 0.10. Flag anything outside it in Monday's ten-minute scan.

Common questions

What is a good sell-through rate? No single benchmark survives contact with different categories and cadences — even Shopify's own guide gives both "80%+" and "70–80%" as healthy ranges (accessed October 2026). The honest baseline is your own: each SKU's median sell-through over the last two or three months, with a normal band of roughly ±10 points around it.

How do you calculate sell-through rate? Units sold ÷ units available × 100, over a defined period. The denominator choice matters: beginning inventory for steadily replenished SKUs, units received for batch buys like launches or seasonal orders. A candle starting October with 400 units and selling 310 has a sell-through of 77.5%.

What's the difference between sell-through rate and inventory turnover? Sell-through is a per-SKU ratio for a specific period — did this buy move? Turnover is a store-level finance metric in dollars (COGS ÷ average inventory value) that says how fast your whole inventory investment cycles. Sell-through drives buying and markdown decisions; turnover feeds lender and finance conversations.

Can sell-through rate be over 100%? Yes, and it's a flag, not a win. Selling more units than you had available points to a miscounted beginning inventory, a counting lag, or genuine overselling — orders taken you couldn't fill. Check the data before celebrating; the overselling case has its own causes and fixes.

How do you increase sell-through rate? Match the buy to real demand first — most low sell-through is an oversized purchase, not a selling problem. Then read the number next to days of cover: high sell-through with low cover means under-buying, while a SKU two months below its own baseline needs the slow-mover intervention ladder, not more shelf time.

Next step: compute sell-through for your ten highest-inventory-value SKUs for the last three months and set their baselines. Anything sitting more than 10 points below its own median two months running goes on this month's buying-decision list — and if you want the surrounding metric set, the inventory KPIs guide picks up from here. Sell-through is one input to the bigger question of what demand is coming next; the methods ladder in our Shopify inventory forecasting guide shows how it feeds a forecast.