- 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
List price dataset containing baseline product list pricing for manufacturing pricing scenarios, required for application deployment and pricing insights.
The List Price dataset contains product list pricing information used in manufacturing pricing scenarios. It defines baseline selling prices that apply at specific stages in the quoting or pricing process.
This dataset is required for application deployment and pricing insights analysis.
→ For the canonical technical schema (data types, validation rules, request/response examples), see List Price in the API Guide.
Purpose
The commercial pricing solution uses the List Price dataset to:
- Establish baseline prices for products
- Compare quoted prices against list prices
- Evaluate pricing adjustments and guardrails
- Analyze pricing performance relative to standard pricing policies
List price data provides the reference point for pricing recommendations and margin analysis.
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 |
price_list_id | Price list identifier (Primary 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 list price to the date of each quote. | timestamp_tz | Yes |
list_price | List price for the product. Used to calculate the discount 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 an APPROVAL_STATUS column to this dataset:
curl -X POST \
'https://ingestion.peak.ai/api/v2/schema/QP_LIST_PRICE_OOTB/add-attribute' \
-H 'Authorization: YOUR_API_KEY' \
-H 'Content-Type: application/json' \
-d '{
"solutionName": "QP_OOTB",
"columnName": "APPROVAL_STATUS",
"dataType": "string",
"validations": [
{ "type": "optional" }
]
}'
curl -X POST \
'https://ingestion.peak.ai/api/v2/schema/QP_LIST_PRICE_OOTB/add-attribute' \
-H 'Authorization: YOUR_API_KEY' \
-H 'Content-Type: application/json' \
-d '{
"solutionName": "QP_OOTB",
"columnName": "APPROVAL_STATUS",
"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_LIST_PRICE_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 APPROVAL_STATUS becomes C_APPROVAL_STATUS 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
- List prices should reflect the official baseline prices used in the quoting process.
- All referenced identifiers (
product_id,region_id,price_list_id) must exist in their corresponding datasets. - Use the
price_list_idto distinguish list prices that apply in different pricing contexts (such as regional or customer-tier price lists). - Successive versions of a list price are appended with new
updated_atvalues, so historical pricing analysis stays accurate over time. - Historical list prices improve pricing analysis and performance tracking.
Why this dataset matters
The List Price dataset establishes the baseline pricing structure for manufacturing pricing. Without it, the solution cannot compare quoted prices against standard pricing policies or evaluate deviations from list price.