How a business prices its products is a dynamic process that drives customer satisfaction and loyalty, as well as business success. In the digital age, pricing is becoming even more complex. For example, companies like Amazon may revise the price of a hot item several times per day.
Dynamic pricing models for consumer financial products can be especially difficult for at least four reasons:
- A complex regulatory environment.
- Fair lending concerns.
- The potential for adverse selection by risky consumers and fraudsters.
- The direct impact the affordability of a loan may have on both the consumer’s ability to pay it and the likelihood that it will be prepaid.
If a lender offered the same interest rate and terms to every customer for the same loan product, low-risk customers would secure better rates elsewhere, and high-risk customers would not. The end result? Only the higher-risk customers would select the product, which would increase losses and reduce profitability.
For this reason, the lending industry has established risk-based pricing. This pricing method addresses the above issue, since customers with different risk profiles are offered different rates. But it’s limited. More advanced lenders also understand the price elasticity of customer demand, because there are diverse reasons why customers decide to take up differently priced loans.
Customers have different needs and risk profiles, so they react to a loan offer in different ways. Many factors determine a customer’s propensity to take up an offer — for example, the competitive environment and availability of other lenders, how time-critical the decision is, and the loan terms offered. Understanding the customer’s price elasticity allows a business to offer the ideal price to each customer to maximize profitability.
Pricing optimization is the superior method assuming the lender has a scientific, data-driven approach to predicting how different customers will respond to different prices. Optimization allows an organization to determine the best offer for each customer to meet business objectives while adhering to financial and operational constraints such as volume, margin and credit risk. The business can access trade-offs between competing objectives, such as maximizing revenue and maximizing volume, and determine the optimal decision to be made for each individual customer to best meet both objectives. In the table below, you can see five benefits lenders realize when they improve their pricing segmentation with an optimization strategy.
Interested in learning more about pricing optimization? Click here to download our full white paper, Price optimization in retail consumer lending.
