Margin contraction, growing competition, and the ongoing low-interest environment force corporate banks to optimize their pricing strategy. Pricing discretion of relationship managers versus company pricing policies causes massive fluctuation in revenue, inconsistent client satisfaction, and suboptimal pricing strategies. Reactive pricing (replying to RFPs) and revenue maximization (hitting product sales targets) dominate.
Goals and Objectives
Maximize revenue, as studies have shown that an optimized pricing strategy can outperform the market by 20–30%.
Drive customer loyalty, and increase the share of primary client relationships.
Reduce deviations from the standard pricing schedule.
Drive discipline, and optimize cross-product subsidies.
Actively manage and forecast the return on investment of discretional discounts and promotions.
Big Data and analytics to optimize pricing and analyze external data
Machine learning to forecast and optimize campaign profitability and personalize the offering to individual clients
Real-time competitive intelligence monitoring
Use Case Summary
Corporate banks seek to leverage Big Data/digital solutions to build intelligent pricing tools and strengthen sales process discipline. Algorithms help identify price sensitivities based on multiple available data sources (internal benchmarks, market benchmarks, and client behavior). Custom tools embedded in the sales application drive productivity and allow intuitive simulation of pricing opportunities.