Sales dataset

Sales dataset containing completed transaction data per product-customer combination, used by Commercial Pricing to analyze how quoted prices translate into sales performance.

The Sales dataset contains completed transaction data for products sold to customers. It represents actual pricing outcomes and is used to evaluate how quoted and list prices translate into realized sales performance.

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

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

Purpose

The commercial pricing solution uses the Sales dataset to:

• Analyze actual transaction outcomes following quotes
• Evaluate demand response to pricing decisions
• Measure realized revenue and margin performance
• Support win/loss and conversion analysis

Sales data complements the Quote Line dataset by providing actual transaction results rather than proposed prices.

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
sale_id	Unique identifier for each sale.	string	Primary key
product_id	Unique identifier for each product.	string	Primary key
region_id	Unique identifier of the region.	string	Primary key
customer_id	Unique identifier for each customer.	string	Nullable
merchant_id	Unique identifier for each merchant.	string	Nullable
quote_id	Unique identifier for each quote.	string	Non-null
price_list_id	Price list used for this sale.	string	Nullable
sold_at	Timestamp of sale.	timestamp_tz	Primary key
quantity	Quantity of the product sold.	float	Nullable
selling_price	Selling price for the product.	float	Nullable

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 SALES_CHANNEL column to this dataset:

curl -X POST \
  'https://ingestion.peak.ai/api/v2/schema/QP_SALE_OOTB/add-attribute' \
  -H 'Authorization: YOUR_API_KEY' \
  -H 'Content-Type: application/json' \
  -d '{
    "solutionName": "QP_OOTB",
    "columnName": "SALES_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_SALE_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 SALES_CHANNEL becomes C_SALES_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

• All referenced identifiers (product_id, customer_id, merchant_id, quote_id, region_id, price_list_id) must exist in their corresponding datasets.
• Sales data should reflect finalized transactions, not quotes or provisional pricing.
• Units of measure must be consistent with related datasets (Products, Quote Line).
• Historical sales depth improves model stability and recommendation accuracy.
• Consistency between Quote Line and Sales datasets supports reliable win/loss and conversion analysis.

Why this dataset matters

The Sales dataset provides the outcome data necessary to evaluate the effectiveness of pricing decisions. Without actual transaction data, the solution cannot measure conversion performance, margin realization, or the true impact of pricing strategies.