Robert Kugel's Analyst Perspectives

I recently attended the 2012 Global Pricing Forum hosted by Nomis Solutions, a provider of software and services to banking and finance companies. This annual event brings together thought leaders and practitioners in the area of pricing and revenue optimization (PRO). This technique uses analytics to sift through large data sets to tease out customer behavior characteristics, identify customer segments and quantify their price sensitivities. These complex calculations require software designed for the purpose, but most in the financial services industry rely on older methods that produce less-than-optimal results. Analytics can help organizations more carefully manage the process of defining offers to customers (especially the levels of discretion offered to account managers and sales people) and the terms and conditions.

Pricing optimization is based on a concept that is simple to describe but difficult to execute because even buyers with identical demographic characteristics (such as age, income or location) can have different degrees of price sensitivity. For example, those with low price sensitivity may place more value on other characteristics of a transaction with a financial services firm. These may include service levels or service bundles, features (specific terms and conditions such as prepayment flexibility), convenience or brand values (including security, reliability or efficiency). By appropriately structuring its offers, a financial service company can get closer to its optimal trade-off between volumes and risk-adjusted profitability.

For its part Nomis’s software provides clients with a Nomis Score that measures customers’ price sensitivity. (You can find its patent application here. This score is the foundation for structuring offers. By allowing financial services companies to understand how changes in pricing will affect demand from different segments of customers, it can provide an analytic basis for an effective customer value pricing strategy. Several of the presentations at the conference were case histories documenting how customers have achieved higher returns or increased volumes using the score as part of their optimization efforts.

I’ve been going to this event for several years. For me, the key take-away from this year’s forum could be summed up as “pricing is more than just pricing.” In other words, while the mechanics of executing a price optimization strategy are extremely important, it’s valuable to step back and recognize some of the key reasons why price optimization is a strategic imperative in financial services.

Nomis founder Robert Phillips (also the author of a book on the topic) pointed out that this industry has inherently fewer strategic methods for maximizing profit. Interest rates are a commodity. Unlike for luxury goods, few if any borrowers are willing to pay more for a mortgage because of the prestige of borrowing from a particular lender. As a further complication, those eager to pay a higher interest rate are also more likely to default (a condition referred to as “moral hazard”). Thus the need to segment existing and potential customers to determine, on the one hand, their willingness to pay for loans or services or, on the other, their requirements for the use of their own funds (such as certificates of deposit or checking accounts).

Even though it’s important to consider the bigger picture, the focus of the conference was on improving the execution of price and revenue optimization. This is still a cutting-edge discipline from several perspectives: using the underlying analytics technology, managing the systems and integrating PRO into the day-to-day management of a financial institution.

As well, before diving into the mechanics of PRO, it’s necessary to consider the bigger picture issues at work in applying it successfully. One of the most important elements contributing to the success of PRO is understanding why customers want to do business with a company – not the reasons people within the company think existing and prospective customers have but those revealed by factual market research. Company lore often is out of date and, especially in larger institutions, doesn’t reflect the breadth of motivations and concerns of clients. No doubt this is generally good advice for every business, but it is vital to managing pricing as an ongoing process.

A broader point to come out of the conference is that pricing and revenue optimization will be even more important to the financial services sector in a period of rising interest rates. Today, most of the developed world is living in an environment of “rate suppression.” This will almost certainly end within the next couple of years if there’s any improvement in the world economy or if central bankers decide it is no longer advisable. Yet few people now working at banks or other financial institutions have experienced conditions similar to the 1970s when rates rose sharply. Managing portfolios and sourcing funds in a generally rising interest rate environment poses different challenges than dealing with today’s low rates. If history is a guide, not managing this properly will have severe consequences for organizations unprepared for it.

The underlying analytics are vital because it’s necessary for companies to monitor markets continuously to determine the price elasticities of various customer segments as they set rates, terms and conditions to best reflect the trade-offs customers make. Most customers respond in a predictable fashion to pricing. In financial services, for example, those with good credit ratings are more demanding because they have more options; those borrowing larger amounts are more price-conscious because of the absolute cost; and new customers are price-conscious because paying less is a key motivation for changing banks. However, it’s almost certain that within these general behavior patterns, lenders will find subtle but important variations in how potential customers respond to specific structured offers. Understanding the key lender and customer decisions that occur in the sales process is fundamental for defining an effective modeling approach to predicting customer behavior. These variations usually make the difference in improving an institution’s returns on capital.

Pricing and revenue optimization first took hold in the 1980s in the travel and hospitality industries. It is part of everyday business practices for airlines and hotels worldwide because it works. Yet its use is still immature in most other industries. While some financial services companies run their own internal PRO efforts, most don’t have the resources, desire or ability to do this in-house. Evidence is mounting that PRO is an effective tool in the finance industry, and I believe the outlook for the financial markets means that its use will be even more important in coming years. I recommend that financial services firms take steps to incorporate more effective pricing as part of their strategy and to look into Nomis Solutions to provide the analytical and operational underpinnings of such an effort.

Regards,

Robert Kugel – SVP Research