95 percent on shelf, 85 percent in practice, and the distance between those two numbers is a counting method rather than a rounding error. As of November 9, 2026, Instacart is rolling out two in-store products that turn its shopper network into a continuous shelf audit: Inventory Intelligence for retailers and the Store Excellence Portal for consumer brands. The company says roughly 600,000 active shoppers visit participating stores more than 15 times per store per day, producing over 10 million unique data points daily across about 2,200 banners and nearly 100,000 stores. For a brand team, the offer is not a faster report but a different unit of measure, an empty facing priced at the store and at the UPC.
Two products, one measurement change
The retailer tool renders a walkthrough of an aisle and flags shelf gaps, high velocity items at risk of running out and planogram changes before they happen. The brand tool goes further: it estimates lost sales from out of stocks every day, at store level and at UPC level, and tracks whether a shopper who met an empty facing switched to a competitor SKU or simply took another pack size of the same product. Grocery CPG reported that both sit inside the Instacart Enterprise line, with a handful of grocers live and a wider rollout expected over the coming months.
The commercial detail left out of the announcement is the one buyers will ask about first: what a store pays for continuous visibility, and whether the brand or the retailer carries that cost. Instacart partners with more than 2,200 retail banners representing nearly 100,000 stores, which gives the platform a footprint that few shelf audit vendors can match on day one. That scale is the argument for the product, and it is also the reason a mid-sized brand should test the data against its own store list before signing.
Inventory Intelligence for store teams
For a store manager the output is a list rather than a dashboard: which facings are empty, which fast movers are about to run out, and where a single added facing would close a revenue gap. That is a different job from reading a weekly stockout rate, because the work is assigned by position in an aisle instead of by category total. Whether the list survives contact with the back room, where the missing carton usually sits, is the open question every pilot answers in its first month.
Six hundred thousand shoppers as a sampling frame
The measurement engine is not a new sensor network. Shoppers already walk every aisle to pick orders, and each visit produces scans and shelf imagery, augmented by Caper Cart cameras that use vision technology from Arpalus, a company Instacart acquired to build the capability. The pitch is that the data is a by-product of work that was already happening, which is why the coverage claim is stated in visits per store per day rather than in installed devices or in square metres of shelf.
A dense visit count is not an even one. High volume stores in dense metros generate far more passes than a suburban store with a thin order book, so a national availability figure can reflect the store mix of the sample as much as the state of the shelf. A brand reading that number should ask whether the sample matches its own distribution footprint before carrying it into a buyer meeting, because distribution coverage in doors and availability at the facing are two different claims.
Substitution is the number most reports skip
A stockout rate says how often a product was missing; it does not say where the demand went. If the shopper bought another pack size inside the same brand, the loss is smaller than the rate implies. If the shopper walked to a competitor facing, the loss compounds, because the new habit can outlast the refill. Linking the two takes order history, which is why 1.6 billion lifetime orders matter more here than the shelf images themselves.
What a shelf scan still cannot see
| What the system reports | Unit of measure | How often it refreshes | Known limit |
|---|---|---|---|
| Shelf gap on a facing | Position in an aisle | Daily, from shopper passes | Cannot separate back room stock from a delisting |
| Lost sales from out of stocks | Store and UPC | Daily estimate | Modelled from substitution history, not invoiced |
| Substitution path | Shopper basket | Daily where order data exists | Thin in stores with few online orders |
| Planogram drift | Aisle layout | On each scan cycle | Density varies by metro and banner |
The gap between an empty facing and a real out of stock is the first limit. A scan shows whether the position is occupied; it does not show whether stock sits in the back room, whether a replenishment task is already queued, or whether the item was quietly delisted. Those three cases look identical in a photograph and need three different responses, which is why availability data still takes a human check before it becomes a claim in a negotiation.
Why lost sales per store reorders field visits
Turning an empty shelf into a currency figure requires assumptions about what the shopper would have bought. The output is an estimate, not an invoice, and the difference matters once the number travels from a dashboard into a trade conversation about fixtures, fees or ranging. Advantage Solutions, an early user of the portal, said the data lets teams prioritise store visits by sales opportunity and send field resources to the shelves most likely to recover revenue.
That changes the ranking logic inside a sales force as well. A store with a modest stockout rate on a high margin line can outrank a store with a severe rate on a low value line, which is close to the opposite of how most availability reports are sorted today. Once the sort key changes, the visit plan changes with it, and so does the argument about who should pay for the extra facing.
Where coverage density thins out
The uneven part is geography. A brand concentrated in regional banners may find the sample thin exactly where its own volume sits, which makes a national number look better than the stores that decide the quarter. The practical response is to ask for the store list behind the average rather than a tighter interval around the average, and to compare the sample against a listing such as the Coles rollout of a two SKU range across 750 doors, where coverage in doors and availability on shelf diverge fast.
FAQ
Does continuous shelf data replace a periodic store audit?
It replaces the cadence, not the audit. A scan confirms whether a facing is occupied; it cannot tell whether the stock is in the back room or the item was delisted, so a manual check still sits between the data and the claim.
How should a brand read a national availability number?
As a sample average, not a score. Ask which stores are in the sample, whether the mix matches your distribution, and how many passes each store gets per day before comparing it with your own numbers.
Why does substitution behaviour matter more than the stockout rate?
Because it sizes the loss. A shopper who takes another pack size of the same product leaves less value behind than one who walks to a competitor facing, and the second case often persists after the shelf is refilled.
Is the lost sales estimate usable in a trade negotiation?
As a modelled estimate, yes, provided it is labelled that way. It ranks stores and facings well; it does not settle a claim about invoiced revenue without a second source.
