DATA-DRIVEN APPROACH TO POWER THE PRICING STRATEGY
Data-driven pricing is the rapid adjustment of prices to customer context and demand conditions based on real-time internal and external data.
This approach enables banks to react to changes in the environment of a product or service while pursuing certain goals like profit or revenue maximization.
Asynchronous approaches, such as cost-plus and value-based approaches, can lead to inaccurate pricing due to dynamic internal and external environments. On the other hand, a data-driven approach facilitates synchronized dynamic pricing in banking to estimate demand functions and adjust the pricing strategy.
Typically, pricing algorithms encompass a specific approach that begins with collecting and processing historical and current data. Next, the demand function is derived, followed by generating optimal pricing to a predefined goal, such as revenue maximization, within the constraints imposed by the pricing policy. After the optimal pricing is applied for a certain time period, the realized demand is observed, and the cycle is repeated.
Another approach that is more relevant for banking segments such as commercial banking is leveraging a banking dynamic pricing index that considers real-time data points. The dynamic index is then applied to pricing determined through traditional pricing models to arrive at an updated data-driven price. The dynamic pricing index is generated using algorithms that consider internal factors, such as business transaction volumes, credit line utilization and relationship size, and external factors like corporate customer financial news, credit ratings, and quarterly results.
The price recommendations of the pricing algorithms can be integrated into banks’ operational processes according to different levels of automation and specific business segments.