Margin contraction, growing competition, and the ongoing low-interest environment force corporate banks to optimize their pricing strategy to maximize revenue and drive customer loyalty.
Pricing discretion of relationship managers versus company pricing policies cause massive fluctuation in revenue, inconsistent client satisfaction, and suboptimal pricing strategies.
There is a lack of understanding of client price sensitivity.
Reactive pricing (reply to RFPs) and revenue maximization (hit product sales targets) dominate instead of a proactive strategy with the customer’s benefit at heart.
Goals and Objectives
- Maximize revenue – 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 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/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). This will help optimize and standardize pricing discipline.
Custom tools embedded in the sales application drive productivity and allow intuitive simulation of pricing opportunities.