Product store data

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)

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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.

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Data schema

You may include additional attributes as separate columns.

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Matching logic

• item_id must match the Products dataset
• store_id must match a store defined in Stores management

Matching can be configured using:

• Exact match
• Composite keys (item_id + store_id)
• Custom identifiers

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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 for details.

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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

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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.

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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.

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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

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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

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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

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Example row

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How Retailgrid uses store-level data

This dataset powers:

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Margin analysis per store

• Store-level profitability
• Margin leakage detection

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Local price optimization

• Dynamic pricing by store
• Market-based pricing

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Assortment strategy

• Store-specific availability
• Stock-based pricing decisions

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Promotion strategy

• Local promo activation
• Regional discount benchmarking

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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

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Related

• Manage stores
• Product and SKU-Store grid views
• Datasets