- 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
Product cost dataset containing unit cost information per product over time, providing the financial context for margin and profitability analysis in Commercial Pricing.
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.
→ For the canonical technical schema (data types, validation rules, request/response examples), see Product Cost in the API Guide.
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
About the Nullable column: every field below must appear in your data. Nullable: Yes means the field can be sent as null (or left blank in your source) when no value is available; Nullable: No means a non-null value is required for every row.
| Field | Description | Type | Nullable |
|---|---|---|---|
product_id | Unique identifier for each product. Used to join product metadata to quotations and sales for model training and guardrails. | string | No |
region_id | Region identifier (Primary Key, Foreign Key) | string | No |
updated_at | Timestamp when the record was updated. Used to identify the most up-to-date data in the UI and when optimising prices. For model training, used to match the cost price to the date of each quote. | timestamp_tz | Yes |
product_cost | Cost price of the product. Used to calculate the margin/profit applied to all quotations and sales orders for model training and optimising prices. | float | Yes |
Custom fields
The fields listed previously are the standard schema for this dataset. A field that your business requires but the standard schema does not include can be added as a custom attribute after the schema is rolled out. Custom attributes are additive, so they extend the dataset without affecting existing fields, and they are validated in the same way as standard fields.
The following request adds a COST_SOURCE column to this dataset:
curl -X POST \
'https://ingestion.peak.ai/api/v2/schema/QP_PRODUCT_COST_OOTB/add-attribute' \
-H 'Authorization: YOUR_API_KEY' \
-H 'Content-Type: application/json' \
-d '{
"solutionName": "QP_OOTB",
"columnName": "COST_SOURCE",
"dataType": "string",
"validations": [
{ "type": "optional" }
]
}'
curl -X POST \
'https://ingestion.peak.ai/api/v2/schema/QP_PRODUCT_COST_OOTB/add-attribute' \
-H 'Authorization: YOUR_API_KEY' \
-H 'Content-Type: application/json' \
-d '{
"solutionName": "QP_OOTB",
"columnName": "COST_SOURCE",
"dataType": "string",
"validations": [
{ "type": "optional" }
]
}'
The table name in the request URL is this dataset's fully qualified warehouse name, which includes the prefix and suffix configured when your solution was rolled out — QP_PRODUCT_COST_OOTB in this example. Retrieve the name for your own solution with Describe a solution's schema.
On standard tables the API prefixes the new column name in the warehouse with C_ (Snowflake) or c_ (Redshift), so COST_SOURCE becomes C_COST_SOURCE and warehouse queries must use the prefixed name.
→ For the full parameter reference, supported data types, and validation rules, see Customizations in the API Guide.
Usage notes
- Cost data should reflect the actual cost basis used for margin calculations.
- All referenced identifiers (
product_id,region_id) must exist in their corresponding datasets. - Successive versions of a product cost are appended with new
updated_atvalues, preserving the cost history needed for accurate profitability analysis over time. - 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.