The national average hides the decision. Perimeter drills down to the store and SKU level to show what to restock, what to move between stores and what to clear out, with weeks to spare before price decides for you.
Perimeter compares each SKU’s rate of sale in each store against its own history and against comparable stores, not against the chain average.
select store, sku, sum(units)/8 as wk_rate
from store_sales
where week >= 28
group by 1,2 having wk_rate < 2Inventory that hasn’t moved in weeks is projected against the coming season. You know how much is left over and how much time remains before clearance is the only option.
Every launch is compared against its equivalent season, adjusting for open stores and assortment changes, so the comparison is fair.
Each one is answered in the conversation, with its chart and the SQL behind it. When the number matters, anyone can audit where it came from.
From the moment excess appears until the team has a concrete proposal to approve.
Rate of sale by SKU and store, compared against similar stores.
Estimates how much will be left at season end if the trend holds.
Finds the store where that SKU does sell and calculates how many units to move.
The proposal arrives with details for both stores and waits for a human sign-off.
No exporting spreadsheets or pasting data by hand. Every connection is read-only and can be revoked in one click.
SAP
Bsale
Falabella
Cencosud
Mercado Libre
Shopify
Google Sheets
Snowflake
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