- 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 customers dataset contains key information about customers used in manufacturing pricing scenarios. It defines who products are being quoted and sold to, and enables pricing analysis at customer and segment levels.
This dataset is required for both application deployment and pricing insights analysis.
→ For the canonical technical schema (data types, validation rules, request/response examples), see Customer in the API Guide.
Purpose
The commercial pricing solution uses the customers dataset to:
- Identify customers associated with quotes and sales transactions
- Enable customer-level pricing analysis
- Support segmentation and categorization for pricing strategies
- Differentiate pricing behavior across customer categories and subcategories
This dataset is essential for 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 |
|---|---|---|---|
customer_id | Unique identifier for each customer. Used to join customer 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 customer metadata to the date of each quote in model training. | timestamp_tz | Yes |
customer_name | Customer name. Used in the UI to identify customers. | string | No |
customer_category | Customer category. Used in model training as a feature to determine the optimal quote price. | string | Yes |
customer_subcategory | Customer sub-category. Used in model training as a feature to determine the optimal quote price. | string | Yes |
customer_price_list_id | Reference to the price list assigned to this customer. | string | Yes |
source | Source of creation of the customer entry. Enum values: peak (created by Peak application) or customer (customer-provided data). | string | Yes |
Usage notes
- All customers referenced in the Quote Line and Sales datasets must exist in the Customers dataset.
- The
customer_idmust be consistent across all related datasets. - The
sourcefield distinguishes between records created by the Peak application and customer-provided records - Accurate customer categorization improves segmentation-based pricing strategies.
Why this dataset matters
Without the Customers dataset, the solution cannot evaluate quote pricing behavior, analyze customer-level demand response, or generate customer-specific pricing recommendations.