Average Sell-Through Rate by Vertical: 2026 Benchmarks

Sell-through rate is the cleanest single number for telling whether a buy was sized right. It is the share of the inventory you received that actually sold inside a defined window, before promotions or clearance distort the picture. The catch is that good looks completely different depending on what you sell. A 65% seasonal sell-through is a warning sign for a beauty brand sitting on near-expiry SKUs, and a perfectly healthy result for a home goods brand with higher price points and a longer consideration cycle. This piece benchmarks sell-through by vertical so you can calibrate your buys against the right peer set instead of a single industry-wide average that hides more than it reveals.

What sell-through rate actually measures (and what it doesn't)

Sell-through rate is units sold divided by units received over a set period, times 100. If you took in 1,000 units and sold 700 in the season, that is a 70% sell-through. It is a measure of how well a specific buy matched real demand inside a specific window.

It is not inventory turnover, and conflating the two is one of the most common mistakes we see. Turnover is annual COGS divided by average inventory, and it grades the whole business over a year. Sell-through grades one buy over one window. A brand can have healthy annual turns and still be drowning in a single bad seasonal buy, because the good SKUs mask the dead ones in the aggregate number. One formula note: received units versus opening stock can give you different readings for the same SKU, so agree on the denominator before comparing periods. This article focuses on benchmarks; for how inventory days and sell-through velocity relate, see our inventory-days benchmarking guide.

One thing worth flagging early: in practice, most operators do not actually say "sell-through" out loud. When I talk to founders running fast-moving brands, what they reach for is days of cover or rate of sale: inventory on hand divided by how fast it is selling right now. As one put it, "your sales are changing so fast that we have to arrive at something, and then we start measuring and talking about it." Same underlying idea, different vocabulary. The label matters less than whether you are reading the velocity weekly and acting on it.

Benchmark by vertical: the numbers and why they differ

Here is the core reference. These bands are the per-window full-price targets that inventory-planning vendors and fashion analysts converge on, and the spread between the top and bottom of the table is more than 50 points.

The structural reasons matter more than the numbers. Beauty sits a tier high because repeat-purchase loyalty pulls product through and expiry dates punish anything that lingers, so the category tolerates a high target. Home goods sits low because high ticket prices and long consideration cycles slow velocity, and that is fine: a 65% full-price result on a sofa program is a win. Premium footwear looks alarming at 35-50% until you realize the early selling cycle for a $300 boot is long and online-heavy, so a low early-weeks reading is structural, not a failure. Benchmark premium footwear against athletic footwear's 70-85% and you will systematically over-order every season.

Luxury is the odd one out. Brands there deliberately undershoot at 50-65% because scarcity is the point; selling out fast at full price would mean they under-priced or under-positioned. The exception is resale, where letting the market clear the price flips the logic entirely.

Seasonal vs. replenishable: why the same percentage means different things

The single most useful move before you apply any benchmark is to classify each program as seasonal or replenishable, because they read the same number in opposite directions.

A seasonal program should exit close to zero inventory. You bought it for a window, the window closes, and anything left is a markdown liability. That is why 80%+ in the season makes sense as a target. A replenishable program is the reverse: you hold safety stock on purpose so you never stock out of a staple, which means a 65-70% sell-through in any given period is healthy by design, not a miss. Judge a replenishable basics line by the seasonal standard and you will panic-discount product that was doing exactly what it was supposed to do.

This is also where the reorder logic lives. High sell-through SKUs get replenishment orders at or near their reorder point; low sell-through SKUs get frozen and moved to a markdown track. The operators who run this well tier their catalog first. The cleanest version I have heard: "Look at your best sellers in volume and margin, rank them A, B and C. The C's you might keep drop-shipping. The B's you hold maybe eight weeks of cover. The A's you hold twelve." That is sell-through tiering applied at the buy stage, which is the whole game. The brands that get into trouble are the ones managing the symptom instead. One operator described running a clearance event every single month "to get through aged inventory," which is what it looks like when sell-through is never driving the buy decision in the first place.

How sell-through predicts markdowns, and what being wrong costs

Sell-through is a leading indicator. You can read it four to six weeks into a season and act, rather than waiting until you are sitting on six months of dead stock. The further a program lands below plan, the deeper the markdown it usually takes to clear.

The cost of waiting is concrete. Carrying unsold inventory runs 20-30% of its value per year in capital, storage, service and obsolescence costs. Hold a $200K overstock at a 25% carry rate and it costs you roughly $50K a year just to keep it on the shelf, which frequently exceeds the margin you would have given up by marking it down earlier and shallower. Coresight found that about 53% of unplanned markdown cost traces back to misjudged inventory, which is to say overbuying, not weak demand. The markdown is usually a buy-stage problem showing up two quarters late. For the full math on what trapped stock costs, see our guide to inventory write-downs.

When I talk to founders carrying a chronic overstock problem, the honest version is rarely a clever tactic. One operator running a nine-figure brand put it plainly: "Holding inventory is one of the biggest reasons people get into issues, and I keep telling them, can we please bring it down." There is often a secondary clearance channel worth lining up before you need it. Another operator's instinct was the right one: "connect with some of those off-price retailers where you could just offload the stuff that is not turning quickly." Knowing your liquidation outlet before the season turns is part of running sell-through as a system rather than a fire drill.

Sell-through is a leading indicator you can act on four weeks into a season, not a postmortem you run six months too late. The number itself is cheap. The discipline of reading it weekly, tiering your SKUs, and freezing the laggards before they need a 50% markdown is where the money is.

Real-world velocity: what public 10-Ks actually show

No major public apparel, footwear or beauty retailer discloses sell-through rate in its filings. We checked across Nike, Lululemon, Gap, American Eagle, VF Corp, Ralph Lauren, PVH and Under Armour, and the same holds: they report gross margin, comparable store sales, inventory balances and inventory turnover, but never sell-through. It remains a private buy-side metric. The closest public proxy is inventory turns, which calibrates how long each category holds stock even if it is an annual aggregate rather than a single-buy reading.

Williams-Sonoma, a premium home goods retailer, turns inventory about 3.2x a year, or 114 days of inventory outstanding. REVOLVE, a fashion DTC business, turns roughly 2.4x, or 154 days. e.l.f. Beauty turned 2.0x in FY2025 (182 days) and improved to about 2.35x in FY2026 (155 days). Read carefully, the pattern lines up with the benchmark logic: even well-run businesses hold inventory for three to six months on average, and beauty's apparent slowness is partly an artifact of e.l.f. building stock proactively to fill thousands of new retail doors during a hypergrowth run where revenue roughly quadrupled (~4x) from FY2022 to FY2026. That is an intentional growth build, not a velocity problem. For how those holding periods translate into days of cover, see our inventory turnover benchmarks by vertical and the public DTC inventory-days trend.

Building a sell-through cadence for your brand

Benchmarks are only useful if they drive a weekly rhythm. The brands that get this right run three loops.

Weekly, they track sell-through against plan at the SKU-family level and flag two thresholds: a positive reorder signal when a hero SKU is tracking at or above its benchmark, and a markdown watch when a program drops below it. The read is fast and visual. One operator described pulling up the table and seeing a SKU family "drop off majorly, an average going the wrong way," which was the cue to hold the reorder rather than chase a dying line.

Monthly, they run a buy review: compare seasonal sell-through to plan, calculate the variance, and set markdown triggers off the table above before the miss compounds. End of season, they audit every SKU and classify it as carry-forward, markdown, or liquidate based on final sell-through versus the category benchmark, not gut feel. The whole point is to make the buy-stage decision better next time, because that is where sell-through is actually won or lost. If your assortment is mixed (seasonal hero styles plus evergreen basics), split it before you benchmark, or you will average two different businesses into one misleading number.

If you want help turning a sell-through read into a smarter buy plan and freeing the cash tied up in slow SKUs, that is core fractional CFO work.

Sources and methodology

Vertical benchmark bands are vendor-published practitioner guidelines, not survey data. The category targets in the first chart and table come from inventory-planning and fashion-analytics vendors (Toolio, WearView, AIMS360, Heuritech, StyleMatrix) whose published ranges converge tightly but do not disclose sample sizes. Treat them as the industry-used benchmarks they are. The most complete vertical table is Toolio's sell-through guide, with footwear and time-staged figures from StyleMatrix.

The full-price and markdown figures come from the Coresight Research US Retailer Survey. The ~60% average full-price sell-through across US nongrocery retail, the ~$300B markdown pool, and the finding that 53% of unplanned markdown cost traces to misjudged inventory are from Coresight's 2019 survey. It is the most methodologically rigorous source here, though pre-COVID; post-2020 inventory swings may have shifted the averages, but the direction holds.

Inventory-turn proxies are computed from primary SEC filings. Turns and days inventory outstanding for REVOLVE (CIK 1746618), Williams-Sonoma (CIK 719955) and e.l.f. Beauty (CIK 1600033) were derived as COGS divided by average inventory, and 365 divided by turns, from annual 10-K filings on SEC EDGAR. These are annual aggregates for continuously replenished businesses, used as category velocity context, not as direct sell-through benchmarks.

Carrying-cost range reflects cross-source consensus. The 20-30% of inventory value per year figure is consistent across multiple independent inventory-cost references and is used here for the overstock math, not sourced to a single proprietary study.

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