Excerpts from Issue 3 – TCS BaNCS Research Journal
Most financial institutions tend to provide exclusive offers to their key and valued customers. These services do not follow a pre-determined yardstick as they depend on the negotiation skills of both customers and financial advisors. Therein comes the concept of dynamic pricing of financial products, which is fast gaining higher priority in business planning.
Customer Lifetime Value (CLV) is the primary driver for Relationship-based Pricing (RBP). Naturally, a financial institution should maximize the CLV through a flexible bouquet of products with specific pricing attached to each product. But how does one arrive at a specific figure for CLV for each customer and maximize the total CLV for the entire customer base for an institution?
The following steps outline an optimized methodology for CLV calculations:
- Segment the customer base
- Derive a CLV for each segment
- Identify the product bundles
- Determine price elasticity
- Optimize the product bundle allocation
- Define a value-based targeting strategy
- Deploy the rule engine
This approach is a radical shift in the way financial institutions currently approach the customer with their basket of offerings. As such, there is a significant impact on the processes and technologies employed in the operational activities involved. However, the benefits far outweigh the efforts to be invested to enable the institution to treat each customer as an individual and provide products and services in a personalized manner. This also enables the institution to arrive at an appropriate relationship definition and strategy.
Here, we explore a statistical approach to the relationship-based pricing (RBP) of financial products.
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