- 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 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 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 |
|---|---|---|---|
sale_id | Unique identifier for each sale. Used to identify each unique sale. | string | No |
product_id | Unique identifier for each product. Used to join product metadata to the table and to understand which products were sold. | string | No |
region_id | Unique identifier of the region. Used as a feature in model training as optimal prices vary between regions. | string | No |
customer_id | Unique identifier for each customer. Used to join customer metadata to the table. | string | Yes |
merchant_id | Unique identifier for each merchant. Used to join merchant metadata to the table. | string | Yes |
quote_id | Unique identifier for each quote. Used to identify which quotations have converted into sales. | string | Yes |
price_list_id | Price list used for this sale. | string | Yes |
sold_at | Timestamp of sale. Used in model training to understand seasonal effects on the optimal quote price. | timestamp_tz | Yes |
quantity | Quantity of the product sold. Used to understand how much of a product was sold for KPI calculations and model training. | float | Yes |
selling_price | Selling price for the product. Used to determine what price the product was sold for and to calculate the discount applied. | float | Yes |
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.