How the commercial pricing solution works

How the UiPath Commercial Pricing solution generates price recommendations using machine learning models for quote pricing, markdown optimization, and list price adjustment.

The UiPath solution for commercial pricing supports quote pricing decisions by analyzing historical data, evaluating business objectives, and generating price recommendations accordingly. Machine learning models calculate the range of prices a quote is likely to convert at, then compare these to factors such as business guardrails and previous quote successes. This forms the output recommendation.

How this fits into pricing workflows

Following the ingestion of required data, this solution will generate optimal quote price recommendations at a customer level. Sales teams can then use this to rapidly respond to RFQs, with the confidence that this quote is based on factors such as historical data, regional competition, business objectives, and factors specific to that customer. 

Integrations to systems of record and action can also form part of this solution. For more information, reach out to your team contact.

Required inputs

The commercial pricing solution relies on data that reflects how prices affect demand, revenue, and margin across products, customers, or locations.

For details on the data requirements for the Commercial Pricing solution, refer to the following resources:

• Commercial Pricing
• Data requirements

Outputs

Recommended optimal quote prices for every single quote for every single customer, which can be viewed within the application front end provided or in systems of action or record with the use of integrations.