Merchants dataset

Merchants dataset containing merchant entity information used in the Commercial Pricing solution for pricing analysis at merchant and segment levels.

The Merchants dataset contains key information about merchants used in the commercial pricing solution. It defines merchant entities associated with pricing activities and supports pricing analysis at merchant and segment levels.

This dataset is required for application deployment and pricing insights analysis.

→ For the canonical technical schema (data types, validation rules, request/response examples), see Merchant in the API Guide.

Purpose

The solution uses the Merchants dataset to:

• Identify merchants associated with quotes and sales transactions
• Support merchant-level pricing segmentation and analysis
• Enable pricing differentiation across merchant categories and subcategories

This dataset is especially important when pricing strategies vary across merchant types.

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
merchant_id	Unique identifier for each merchant.	string	Primary key
merchant_name	Merchant name.	string	Non-null
merchant_category	Merchant category.	string	Nullable
merchant_subcategory	Merchant sub-category.	string	Nullable
updated_at	Timestamp when the record was updated.	timestamp_tz	Primary key

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 MERCHANT_CHANNEL column to this dataset:

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

• Every merchant referenced in the Quote Line or Sales datasets must exist in the Merchants dataset.
• Merchant identifiers must be consistent across all related datasets.
• Accurate categorization improves segmentation-based pricing strategies.
• The updated_at field ensures synchronization integrity during incremental data feeds.

Why this dataset matters

The Merchants dataset enables the pricing solution to segment and analyze pricing behavior by merchant. Without this dataset, the application cannot reliably apply merchant-level pricing strategies or generate accurate merchant-specific insights.