> ## Documentation Index > Fetch the complete documentation index at: https://docs.retailgrid.io/llms.txt > Use this file to discover all available pages before exploring further. # Product store data > The Product Store-Level dataset allows you to provide product information at store, market, or sales-channel level. The **Product Store-Level** dataset allows you to provide product information at store, market, or sales-channel level. This dataset is required if: * You operate multiple physical stores * You sell across different markets (countries) * You manage multiple sales channels (Online / Marketplace / Wholesale) * Prices, costs, tax rates, or stock differ per store Each row represents: > **One product in one specific store (or market / channel)** *** ## When to use store-level data Use this dataset if: * Prices vary by store * Unit cost differs by warehouse or region * Stock is managed per store * Tax rates vary by country * Some products are active in certain stores only If all stores share identical product data, you may use only the Products dataset. *** ## Data schema | Field Name | Required | Type | Format | Description | Alternative Names | Example | | :--------------------- | :------- | :------- | :------------------------ | :------------------------------------------------------------------------- | :---------------------------------------------------------- | :---------- | | `item_id` | ✅ Yes | string | Text | Unique SKU-level identifier (must match `item_id` in the Products dataset) | itemId, sku\_id | SKU-1001 | | `store_id` | ✅ Yes | string | Text | Store / market / sales channel identifier | storeId, store\_code, market\_id, channel\_id, location\_id | FI-HELSINKI | | `price` | ✅ Yes | float | Dot separator | Current selling price | selling\_price, current\_price | 119.99 | | `unit_cost` | ⬜ No | float | Dot separator | Cost per unit | cost, purchase\_cost, landed\_cost | 75 | | `status` | ⬜ No | string | Text | Product status in this store (active / discontinued / out\_of\_stock) | availability\_status, lifecycle\_status | active | | `stock` | ⬜ No | float | Numeric | Available inventory units | inventory, stock\_qty, quantity\_available | 42 | | `regular_price` | ⬜ No | float | Dot separator | Non-discounted base price | list\_price, base\_price | 129.99 | | `tax_rate` | ⬜ No | float | Percentage (0–1 or 0–100) | VAT or sales tax rate | vat, vat\_rate, tax | 24 | | `currency` | ⬜ No | string | ISO 4217 | Currency code | currency\_code | EUR | | `price_effective_date` | ⬜ No | datetime | YYYY-MM-DD | Date when price becomes effective | effective\_from, valid\_from | 2026-02-01 | You may include additional attributes as separate columns. *** ## Matching logic * `item_id` must match the Products dataset * `store_id` must match a store defined in [Stores management](/stores/manage-stores) Matching can be configured using: * Exact match * Composite keys (item\_id + store\_id) * Custom identifiers *** ## Load by CSV or by API Store-level data can be loaded two ways: * **CSV / XLSX upload** - via the Datasets page, following the format requirements below. * **Public API** - push product-store records programmatically, one record per product-store combination. The upsert key is `(item_id, store_id)`; payloads that omit `store_id` remain product-level and behave as before. See the [API changelog](/api-reference/changelog) for details. *** ## Price interpretation logic Retailgrid applies the following logic: * If `regular_price` exists and `price` \< `regular_price` → product is considered on promotion * Margin is calculated as: ``` Margin = price - unit_cost Margin % = (price - unit_cost) / price ``` * If `tax_rate` is provided, gross/net calculations are supported *** ## Store / market / channel flexibility `store_id` can represent: * Physical store * Country * Warehouse * Online shop * Marketplace (Amazon, Zalando, etc.) * B2B vs B2C channel Retailgrid treats this as a **location dimension**. *** ## Currency handling If multiple stores use different currencies: * Currency must be provided * FX conversion rules must be configured Recommended:\ Keep price and cost in the same currency per row. *** ## Time handling This dataset is typically a **current snapshot**, not time-series. If `price_effective_date` is provided: * Future price simulations become possible * Scheduled price changes can be modeled *** ## Data format requirements Supported formats: * CSV (recommended) * XLSX Encoding: * UTF-8 * Dot (.) as decimal separator Rules: * One row = one product × one store * Do not duplicate identical product-store combinations * Use consistent store\_id formatting *** ## Validation rules Upload will fail if: * `item_id` is missing * `store_id` is missing * `price` is missing or non-numeric * Matching with Products dataset fails *** ## Example row | item\_id | store\_id | price | unit\_cost | status | stock | regular\_price | tax\_rate | currency | price\_effective\_date | | :------- | :---------- | :----- | :--------- | :----- | :---- | :------------- | :-------- | :------- | :--------------------- | | SKU-1001 | FI-HELSINKI | 119.99 | 75 | active | 42 | 129.99 | 24 | EUR | 2026-02-01 | *** ## How Retailgrid uses store-level data This dataset powers: ### Margin analysis per store * Store-level profitability * Margin leakage detection ### Local price optimization * Dynamic pricing by store * Market-based pricing ### Assortment strategy * Store-specific availability * Stock-based pricing decisions ### Promotion strategy * Local promo activation * Regional discount benchmarking *** ## Advanced recommendations For best results: * Always provide `unit_cost` for margin optimization * Keep store identifiers consistent * Provide stock for AI-driven replenishment signals * Include tax\_rate for cross-country operations *** ## Related * [Manage stores](/stores/manage-stores) * [Product and SKU-Store grid views](/grids/sku-store-view) * [Datasets](/data-requirements/datasets)