- 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
Data requirements
The UiPath solution for commercial pricing requires a defined set of customer data in order to generate pricing insights and recommendations. For details on how to connect and ingest data into the Peak platform, see Data ingestion.
The required datasets represent business information that already exists in the customer’s enterprise systems, such as product metadata, customer information, historical quotes, sales transactions, and pricing data.
These datasets are integrated into the Peak platform during onboarding. They are not created or maintained manually in the solution user interface.
How data is used by the solution
The UiPath solution for commercial pricing uses customer-provided data to evaluate pricing decisions and generate pricing recommendations.
In practice:
- Product and customer data define the entities being priced. These datasets establish what products are being sold and who they are being sold to.
- Quote and sales data represent historical pricing decisions and their outcomes. This data enables the solution to analyze win/loss behavior, demand response, and pricing effectiveness.
- Cost and list price data provide financial context. These datasets allow the solution to evaluate trade-offs between margin, revenue, and competitiveness.
- Region and merchant data provide segmentation context. These datasets help differentiate pricing strategies across geographies or business units.
During onboarding, these datasets are mapped and ingested into the Peak platform. Business users do not manually create or edit this data within the pricing solution. Instead, they review pricing context and recommendations generated from it.
The quality, consistency, and historical depth of the data directly influence the stability and accuracy of pricing recommendations.
Required datasets
The following datasets are required to deploy the the commercial pricing solution.
| Dataset | Description | Dataset(s) in this guide |
|---|---|---|
| Products dataset | Stores detailed information about products, including identifiers, names, categories, bespoke status, and update timestamps. | Products dataset |
| Customers dataset | Stores key information about customers, including identifiers, names, and categorization. | Customers dataset |
| Merchants dataset | Stores merchant metadata, including identifiers and categorization. | Merchants dataset |
| Projects dataset | Stores project-level metadata used in pricing contexts. | Projects dataset |
| List price dataset | Stores product list prices for specific quote stages. | List price dataset |
| Product cost dataset | Stores cost price information for products. | Product cost dataset |
| Quote line dataset | Stores quote-level line item data for pricing analysis. | Quote line dataset |
| Sales dataset | Stores completed sales transaction data for demand and performance analysis. | Sales dataset |
| Region dataset | Stores geographical region definitions used for pricing differentiation. | Region dataset |