industry-department-solutions
latest
false
- 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
Last updated Apr 16, 2026
Sales dataset
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.
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
| Field | Description | Type | Priority |
|---|---|---|---|
sale_id | Unique identifier for each sale transaction. | string | Required |
product_id | Unique identifier for the product SKU. | string | Required |
customer_id | Unique identifier for the customer. | string | Required |
merchant_id | Unique identifier for the merchant. | string | Required |
project_id | Unique identifier for the project. | string | Required |
sold_units | Number of units sold. | float | Required |
sold_price | Final price per unit at which the product was sold. | float | Required |
sale_date | Date the sale occurred. | timestamp_tz | Required |
updated_at | Timestamp when the record was last updated. | timestamp_tz | Required |
Usage notes
- All referenced identifiers (
product_id,customer_id,merchant_id,project_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.