At a glance
How fast your FBA inventory turns. Sell-through compares units sold to the average units held over the period, so it tells you whether stock is moving at a healthy clip or sitting in Amazon’s warehouses racking up storage fees. It is the metric behind Amazon’s own Inventory Performance Index (IPI): low sell-through means overstock, capital tied up, and long-term storage exposure; very high sell-through means you are flirting with stockouts. The card flags SKUs turning at less than half the category norm.
| What it measures | Units sold over the period divided by the average FBA units on hand over the same period, a turnover ratio. Higher means inventory is moving quickly; lower means it is sitting. |
| Why it matters | Slow sell-through is the root of overstock costs: storage fees, long-term storage surcharges, stranded capital, and a weak IPI that can trigger storage limits. Fast sell-through risks the opposite, stocking out and losing rank. |
| Link to IPI | Sell-through and excess inventory are core inputs to Amazon’s Inventory Performance Index. A persistently low sell-through drags IPI down, which can cap how much you are allowed to send into FBA. |
| FBA-specific | This is an FBA card because FBA is where the storage clock runs. FBM stock sits in your own warehouse on your own cost basis, so the Amazon-fee consequence does not apply the same way. |
| Benchmark, not absolute | ”Good” sell-through is category-dependent. The alert is relative: a SKU below half the category median is turning too slowly regardless of the absolute number. |
| Action when low | Reduce inbound, run a promotion or coupon, lower price, bundle, or create a removal order before long-term storage fees bite. |
| Time window | 90D (the selected period) |
| Alert trigger | <0.5x category median, turning at under half the category norm |
| Roles | owner, operations, finance |
Calculation
Calculated automatically from your Amazon Seller Central data. See the At a glance summary above for what the metric tracks and the worked example below for a typical reading.Worked example
A toys-and-games FBA seller reviewing 90D sell-through on 01 May 26. Assume the category median sell-through for the period is about 1.5x. Figures are illustrative.| SKU | Units sold (90D) | Avg units on hand | Sell-through | vs category median (1.5x) | Read |
|---|---|---|---|---|---|
| Hot seller A | 5,400 | 1,800 | 3.0x | 2.0x median | turning fast, watch for stockout |
| Steady B | 2,700 | 1,800 | 1.5x | at median | healthy |
| Slow C | 600 | 1,500 | 0.4x | 0.27x median | alert: overstocked |
| Dead D | 90 | 1,200 | 0.075x | 0.05x median | alert: severe overstock, plan removal |
- Low sell-through is money sitting still. Dead D turned only 90 units against 1,200 average on hand over a full quarter. That is frozen capital and a growing storage bill. The fix is markdown, bundle, or removal, not “wait and see”, because long-term storage surcharges escalate the longer it sits. See ASINs Approaching Long-Term Storage.
- The benchmark is relative, not absolute. A 0.4x sell-through might be fine in a slow-moving category and a disaster in a fast one. The alert fires on under half the category median, which is why Slow C is flagged at 0.4x against a 1.5x norm but would not be in a category that naturally turns at 0.5x.
- Very high sell-through is a different risk. Hot A’s 3.0x is great for cash and IPI, but it is the SKU most likely to stock out. Pair high sell-through with Days of Cover (avg) and Replenishment Recommendations so a fast turner does not run dry.
- This feeds your IPI and your storage limits. Persistent low sell-through across the catalogue drags Amazon’s Inventory Performance Index down, which can restrict how much you are allowed to send in. Sell-through is not just a margin metric, it gates your ability to operate FBA at scale.
Sibling cards merchants should reference together
Sell-through is the turnover read. These cover the cost of getting it wrong in either direction:| Card | Why pair it with Sell-Through Rate (FBA) |
|---|---|
| Days of Cover (avg) | The flip side. High sell-through plus low cover means stockout risk; low sell-through plus high cover means overstock. |
| Stranded Inventory Value | Where chronically low sell-through ends up, capital stuck in stock that will not move. |
| ASINs Approaching Long-Term Storage | The escalating fee consequence of slow sell-through; act before stock ages into the surcharge. |
| FBA Storage Fees | The running cost of holding slow-moving stock. Low sell-through inflates this. |
| Replenishment Recommendations | Uses velocity to right-size reorders so you do not recreate the overstock. |
| ASINs Stocking Out <7 Days | The high-sell-through hazard, fast turners running out. |
Reconciling against Amazon Seller Central
Where to look in Seller Central: The closest Amazon-native views are:Inventory → Inventory Planning → Inventory Performance Dashboard (IPI, sell-through, excess inventory), and Reports → Fulfilment → Inventory reports for units on hand and units sold per SKU.Amazon’s Inventory Performance Dashboard publishes a sell-through figure and the IPI it feeds. Expect this card’s per-SKU sell-through to track Amazon’s account-level figure in spirit, though the exact ratio depends on the averaging method and window each tool uses. Timing, settlement, and reporting-lag table:
| Topic | Detail |
|---|---|
| Average-inventory method | Sell-through depends on how average units on hand is computed (start/end average vs daily average over the window). Different methods give slightly different ratios; Amazon’s IPI uses its own method. |
| Window length | The ratio scales with the window. A 90D sell-through is not directly comparable to a 30D one. Compare like windows. |
| Available vs total | Whether reserved and unfulfillable units are included in “on hand” affects the denominator. Stranded units in the denominator depress the ratio. |
| Reporting lag | Inventory snapshots and sales settle on slightly different schedules, so the freshest ratio can move as the latest data finalises. |
| Reason | Direction | Why |
|---|---|---|
| Averaging method | Either direction | Start/end vs daily-average inventory produces different denominators and therefore different ratios. |
| Window mismatch | Scales with window | A 90D ratio differs from Amazon’s default IPI window; align periods before comparing. |
| Stranded units in denominator | Ours may read lower | Including unfulfillable stock in on-hand depresses sell-through; excluding it raises it. |
| Category-median basis | Judgement | The “category median” benchmark is an estimate of the norm, not an official Amazon figure. |
| Card | Expected relationship | What causes legitimate divergence |
|---|---|---|
amazon.days_of_cover_avg | Inverse-ish relationship. High sell-through tends to mean low days of cover and vice versa, both built from velocity and on-hand stock. | They diverge when inventory is lumpy (a big inbound shipment) because cover is forward-looking while sell-through is backward-looking. |
shopify inventory turnover | Same concept, different cost basis. DTC stock turnover matters for cash but has no Amazon storage-fee or IPI consequence. | If stock is pooled across channels, reconcile total on-hand; if separate, the two are independent. |