- 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
The Products dataset contains detailed metadata for products used in the commercial pricing solution. It defines the items being priced and provides attributes required for pricing analysis, segmentation, and guardrail enforcement.
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 in the API Guide.
Purpose
The commercial pricing solution uses the Products dataset to:
- Identify products included in pricing analysis
- Enable pricing evaluation at product and category levels
- Support segmentation strategies (for example, bespoke vs standard products)
- Ensure consistent product identifiers across pricing datasets
This dataset is foundational for both List Pricing and Quote Pricing use cases.
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. | string | No |
updated_at | Timestamp when the record was updated. Used to identify the most up-to-date data in the UI and to match product metadata to the date of each quote in model training. | timestamp_tz | Yes |
bespoke_product | Whether the product is bespoke (custom-made) or standard. Used in model training as a feature to determine the optimal quote price. | boolean | Yes |
product_name | Product name. Used in the UI to identify products. | string | No |
product_category | Product category. Used in model training as a feature to determine the optimal quote price. | string | Yes |
product_subcategory | Product sub-category. Used in model training as a feature to determine the optimal quote price. | string | Yes |
Usage notes
- All products referenced in the List Price, Product Cost, Quote Line, and Sales datasets must exist in the Products dataset.
- The
product_idmust be consistent across all related datasets. - The
bespoke_productfield supports differentiation between custom and standard products in pricing strategies. - Accurate categorization improves segmentation-based pricing recommendations.
Why this dataset matters
The Products dataset defines the scope of pricing analysis. Without it, the commercial pricing solution cannot reliably join cost, quote, sales, and list price data or apply product-level pricing strategies.