Product pricing daily dataset

Product pricing daily dataset for the Markdown & Promotions module, containing daily selling, list, and cost prices per product and location.

The Product pricing daily dataset contains daily selling, list, and cost prices for each product at each location. It captures the pricing history that influences demand and margin in markdown and promotion decisions.

Purpose

The Markdown & Promotions module uses the Product pricing daily dataset to:

• Provide historical selling, list, and cost prices by product and location
• Support margin analysis for markdown and promotion decisions
• Capture how price has changed over time

Required fields

About the Constraint column: every field listed must be provided in your data. Non-null means its value cannot be null; Nullable means the value may be sent as null (or left blank in your source). Primary key, Unique, and Foreign key describe the field's role in the data model; a Primary key is always non-null.

Field	Description	Type	Constraint
location_id	Unique identifier for the store or location.	string	Primary key Foreign key to location
pricing_at	Timestamp the price applies from.	timestamp	Primary key
product_id	Unique identifier for the product SKU.	string	Primary key Foreign key to product
created_at	Timestamp when the record was created.	timestamp	Nullable
quote_stage	Stage of the pricing or quoting lifecycle the record represents.	string	Nullable
selling_price	Actual selling price per unit.	float	Non-null
unit_cost_price	Cost per unit.	float	Non-null
unit_list_price	Standard list price per unit.	float	Non-null
updated_at	Timestamp when the record was last updated.	timestamp	Nullable

Usage notes

• Product and location identifiers must align with the Products and Locations datasets.
• Prices should be expressed in the currency of the associated location.
• A daily record supports accurate historical margin analysis.

Why this dataset matters

Pricing history underpins margin and discount-depth analysis. Without it, the solution cannot assess the financial impact of markdowns and promotions.