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- 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
Supply Chain & Retail Solutions user guide
Last updated Apr 16, 2026
Product cost dataset
The Product Cost dataset contains cost information for products used in commercial pricing scenarios. It defines the unit cost of products over time and provides the financial context necessary to evaluate margin and profitability.
This dataset is required for application deployment and pricing insights analysis.
Purpose
The commercial pricing solution uses the Product Cost dataset to:
- Calculate product-level margins
- Evaluate profitability of quoted or recommended prices
- Support trade-off analysis between revenue and margin objectives
- Ensure pricing guardrails reflect cost constraints
Cost data is essential for generating pricing recommendations that balance competitiveness and profitability.
Required fields
| Field | Description | Type | Priority |
|---|---|---|---|
product_id | Unique identifier for the product SKU. | string | Required |
unit_cost_price | Cost of a single unit of the product. | float | Required |
valid_from | Date and time when the cost becomes valid. | timestamp_tz | Required |
valid_to | Date and time when the cost is no longer valid. | timestamp_tz | Required |
updated_at | Timestamp when the record was last updated. | timestamp_tz | Required |
Usage notes
- Cost data should reflect the actual cost basis used for margin calculations.
- Validity periods must be defined to ensure historical pricing analysis remains accurate.
- The
product_idmust align with the Products dataset. - Historical cost changes improve the accuracy of profitability analysis and recommendation stability.
- Units of measure must be consistent with related datasets (for example, Quote Line and Sales).
Why this dataset matters
The Product Cost dataset enables the solution to evaluate pricing decisions against profitability objectives. Without cost data, pricing recommendations cannot reliably assess margin impact or enforce pricing guardrails.