Pricing and Profitability Management in BankingDOWNLOAD
A large mortgage lender knew that it could boost profits by tailoring its prices more precisely to customer behavior in different marketplace segments. But how could it reliably anticipate how customers would react to price changes?
This U.S.-based financial institution, one of the largest mortgage lenders in the country, had historically taken a competitor-driven “ranked” approach to pricing: In any given marketplace segment, it offered customers the second-best rates relative to those available from its four biggest competitors. While this approach had worked well in the past, the pricing executive believed that the business could do even better with a more nuanced approach to pricing that looked beyond competitor pricing comparisons and incorporated a thorough understanding of the way pricing affected customers’ buying behavior (price elasticity).
The organization had an existing price optimization tool that, given the right data, could calculate profitable prices for each of its marketplace segments. But to understand each segment’s price elasticity, the company needed reliable sources of data as well as people with the skills needed to calculate the elasticities. Lacking appropriately trained in-house staff at the time, the pricing head turned to Deloitte for help.
With Deloitte’s assistance, the company analyzed historical customer data along with data from publicly available sources to understand which data types and sources could support analyses yielding the most reliable elasticity figures for each segment. The company used this data to calculate elasticities at a very detailed level to feed into the price optimization model. The result was a set of prices that could be expected to maximize profitability for the desired business volume for particular customer populations segmented by product type, channel, rate, and geography. The analysis not only put the company’s pricing on a customer-focused, data-driven footing, but also enabled refinements to the company’s geographic segmentation: The results revealed patterns that allowed the company to divide several U.S. states into a number of distinct pricing zones, a level of geographic detail that the company had previously not considered in setting prices.
The company wanted to maintain its price optimization efforts on an ongoing basis, so it engaged Deloitte to help train key in-house personnel to manage the models and calculate elasticities. Deloitte’s assistance also included helping the company develop processes and tools to obtain, cleanse, and analyze appropriate data for the elasticity calculations and transfer the results into the price optimization tool.
By implementing the elasticity-influenced prices with the optimization tool, the company increased its pre-tax bottom-line revenue by almost $100 million annually, an average 8 bps price improvement (keeping volume and risk profile constant). This represented a pre-tax profit improvement of 12 percent over the previous year.
More generally, the ability to reliably calculate elasticities has transformed the company’s pricing strategy from a “follow the competition” approach to a more proactive “understand the customer” method. Pricing executives now have the data, and a level of insight based on that data, to understand when and how a seemingly counterintuitive pricing decision would be likely to influence customer behavior in desirable ways. As a result, price has become a much more powerful and effective strategic tool. In one instance, for example, the company used elasticity estimates to set prices in such a way as to increase market share in more-profitable customer segments while decreasing market share in less-profitable segments. In another situation, after a merger created significant changes in the business’ volume, the company was able to adjust prices and influence volume to be consistent with its operational capacity and maintain profitability at a desired level.