Customers dataset

Customers dataset containing customer information for manufacturing pricing scenarios, enabling pricing analysis at customer and segment levels.

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 Constraint column: every field listed must be provided in your data. Non-null means its value cannot be null; Nullable means the value may be sent as null (or left blank in your source). Primary key, Unique, and Foreign key describe the field's role in the data model; a Primary key is always non-null.

Field	Description	Type	Constraint
customer_id	Unique identifier for each customer.	string	Primary key
updated_at	Timestamp when the record was updated.	timestamp_tz	Primary key
customer_name	Customer name.	string	Non-null
customer_category	Customer category.	string	Non-null
customer_subcategory	Customer sub-category.	string	Non-null
customer_price_list_id	Reference to the price list assigned to this customer.	string	Nullable
source	Source of creation of the customer entry. Enum values: peak (created by the application) or customer (customer-provided data).	string	Non-null

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 LOYALTY_TIER column to this dataset:

curl -X POST \
  'https://ingestion.peak.ai/api/v2/schema/QP_CUSTOMER_OOTB/add-attribute' \
  -H 'Authorization: YOUR_API_KEY' \
  -H 'Content-Type: application/json' \
  -d '{
    "solutionName": "QP_OOTB",
    "columnName": "LOYALTY_TIER",
    "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_CUSTOMER_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 LOYALTY_TIER becomes C_LOYALTY_TIER 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

• All customers referenced in the Quote Line and Sales datasets must exist in the Customers dataset.
• The customer_id must be consistent across all related datasets.
• The source field 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.