1. SAP Glossary
  2. Price Optimization for Banking
  3. demand-driven pricing


What is demand-driven pricing in SAP CA-FS-PO - Price Optimization for Banking?


SAP Term: demand-driven pricing

  • Component: CA-FS-PO

  • Component Name: Price Optimization for Banking

  • Description: The price at which the customer is willing to buy. Demand models are used to predict how customers will react to a proposed rate, using several types of historical data. The optimal rate is then generated based on this customer history.


Smart SAP Assistant

  • Key Concepts: 
    Demand-driven pricing is a pricing strategy used by banks to optimize their pricing decisions. It is based on the idea that the price of a product or service should be determined by the demand for it. This strategy is implemented in SAP's CA-FS-PO Price Optimization for Banking component, which uses predictive analytics to analyze customer data and determine the optimal price for a product or service. 
    
    How to use it: 
    The CA-FS-PO Price Optimization for Banking component can be used to set prices based on customer demand. It uses predictive analytics to analyze customer data and determine the optimal price for a product or service. The component also allows users to set rules and parameters for pricing decisions, such as discounts, promotions, and other incentives. 
    
    Tips & Tricks: 
    When using the CA-FS-PO Price Optimization for Banking component, it is important to consider the customer's needs and preferences when setting prices. Additionally, it is important to consider the competitive landscape when setting prices, as this can help ensure that prices remain competitive. 
    
    Related Information: 
    For more information about demand-driven pricing and SAP's CA-FS-PO Price Optimization for Banking component, please visit SAP's website at https://www.sap.com/products/ca-fs-po-price-optimization.html. Additionally, there are many resources available online that provide more information about demand-driven pricing strategies and how they can be used to optimize pricing decisions.
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