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Supply Chain & Retail Solutions user guide
- Overview
- Platform setup and administration
- Platform setup and administration
- Platform architecture
- Data Bridge onboarding overview
- Connecting a Peak-managed data lake
- Connecting a customer-managed data lake
- Creating an AWS IAM role for Data Bridge
- Connecting a Snowflake data warehouse
- Connecting a Redshift data warehouse (public connectivity)
- Connecting a Redshift data warehouse (private connectivity)
- Reauthorizing a Snowflake OAuth connection
- Using Snowflake with Peak
- SQL Explorer overview
- Roles and permissions
- User management
- Inventory management solution
- Commercial pricing solution
- Merchandising solution
Sales dataset for the Markdown & Promotions module, containing historical sales and return transactions per product and location.
The Sales dataset contains historical sales and return transactions for each product at each location. It is the primary demand signal used by the Markdown & Promotions module to understand product performance.
Purpose
The Markdown & Promotions module uses the Sales dataset to:
- Provide historical demand and sales performance by product and location
- Capture returns alongside gross sales for net performance analysis
- Support evaluation of markdown and promotion impact over time
Required fields
| Field | Description | Type | Use | Notes |
|---|---|---|---|---|
location_id | Unique identifier for the store or location. | string | Application / Presales | Required. Primary key. Foreign key to location. |
product_id | Unique identifier for the product SKU. | string | Application / Presales | Required. Primary key. Foreign key to product. |
sold_at | Timestamp when the sale occurred. | timestamp | Application / Presales | Required. Primary key. |
created_at | Timestamp when the record was created. | timestamp | Application | Optional. |
customer_id | Identifier for the customer associated with the transaction. | string | Application / Presales | Optional. |
demand_units | Number of units demanded or ordered. | float | Application / Presales | Optional. |
demand_value | Total value of the demanded units. | float | Application / Presales | Optional. |
return_units | Number of units returned. | integer | Application / Presales | Required. |
return_value | Total value of the returned units. | float | Application / Presales | Required. |
returned_at | Timestamp when items were returned. | timestamp | Application / Presales | Optional. |
sale_id | Source-system identifier for the sale. | string | Application / Presales | Optional. |
sales_units | Net units sold, after returns. | integer | Application / Presales | Required. |
sales_value | Net sales value, after returns. | float | Application / Presales | Required. |
shipped_at | Timestamp when items were shipped. | timestamp | Application / Presales | Optional. |
updated_at | Timestamp when the record was last updated. | timestamp | Application | Optional. |
Usage notes
- Product and location identifiers must align with the Products and Locations datasets.
- Units should use the same unit of measure as related datasets (for example, Stock).
- A complete history improves the accuracy of demand analysis.
Why this dataset matters
Sales history is the core demand signal. Without it, the solution cannot assess product performance or the impact of markdowns and promotions.