|The price is right? (September 2007)|
If you haven’t heard of price optimization software, you’re already behind the curve. See how this tech-driven capability supports product differentiation.
By Lauren Bielski, senior editor
Price optimization software tailors product pricing with greater precision. Yet it must be handled with care
When it comes to recalibrating loan or account features—especially price—banks, historically, have limited their revisions to matching the competition.
Or, they’ve kept their analysis to peeks at cash flow, projected interest rates, and movements of capital markets, before setting terms. Changes often are made the old-fashioned way, with a spreadsheet and on a department-by-department basis. But more demanding customers have made alternatives to plain vanilla pricing necessary. At the same time, bank managements that want to create pricing in a centralized, predictable, and repeatable way have made price optimization technology the new hot thing to have.
PO—otherwise known as profit-based pricing systems—can add precision, notes Kathleen Khirallah, managing director and practice leader retail banking with TowerGroup, Needham, Mass. The best of these also compensate for tendencies such as adverse selection, or terms that could sink the bank with less desirable customers. PO systems are said to replace the many forms of manual workaround that gets information from the streets into a bank’s back office without the need to mess with the core processor.
To date, says Khirallah in a report she co-wrote called Pricing Optimization: A Practical Guide to a Retail Bank Implementation, the most common implementations of price optimization are typically in automotive and home equity lending.
Rather than help with segmentation or any form of customer analysis or record keeping, PO instead lasers in on the product, tailoring it to the customer by incorporating rules engines and intuitive interfaces that let bankers easily enact corporate strategy, on the one hand, and respond to situational or segmentation variables on the other, in building pricing models.
“It’s a complicated, emerging area that nearly every big bank is interested in or already pursuing, but they are trying to do so under the radar,” notes Richard De Lotto, principal analyst, banking and securities practice, Gartner, Stamford, Conn.
Over 55% of banks polled in a Gartner telephone survey of 34 retail banks in January have already adopted some form of price optimization and more than 75% plan to use these products in some way by 2012.
Price-tailoring capability is complicated to master, De Lotto says, because it requires math and pricing-science skills to interpret software results. Many banks are considering hiring consultants to get emerging programs on track. Meanwhile, De Lotto says, price setting is perhaps one of the more personal and political functions at a financial institution. “It may mean the end of the sweetheart deal,” he asserts. Despite all the intrigue and complication, De Lotto thinks that at the end of the day most banks will use the software and make it work for them.
Washington Mutual, one acknowledged early adopter, is using a price optimization solution from Nomis Solutions, San Bruno, Calif., as part of a strategy to hone its home equity business. Halifax Bank of Scotland, which was Nomis Solutions’ first bank client, is using price optimization to refine its personal loans program.
Gaining wider use
Of course, not every customer shops on price alone, but among those that are counting their pennies and basis points, more refined offerings can yield big successes. In the U.S., three of the top ten banks are incorporating price optimization technology in various line-of-business product offerings, notes Robert Phillips, Nomis Solutions’ co-founder, chief science officer, and vice-president. Phillips was a price optimization pioneer, having developed applications for the airline business before moving into the banking space.
Four or five other vendors that attack the problem in various ways provide solutions designed to let a bank set pricing policy without interacting with its core processing system. Again, says De Lotto, the technology would allow for easy changes, but bank senior management probably intends for ad hoc decisions to be greatly reduced.
Vendors in the space, according to Gartner, include Acorn Systems, Earnix, PROS, Rapt, SunTec Business Solutions, and Zafin Labs as well as the offerings of several business intelligence vendors that plan to enter the market.
In one of several research notes on the subject, De Lotto talked about PO as an underpinning of offset accounts, which, as the name suggests, offset loan interest owed against any funds held in customers’ savings accounts without freezing the savings as collateral. The technology can also help to create loyalty programs across product lines or build programs that rely on dynamic relationship pricing where consolidated statements and aggregate views of the customer and account value factor into pricing decisions.
De Lotto wrote that the software would have a bigger presence in retail banking, but warned: “PO will inexorably spread from its maturing base in mass-market retailing and travel to retail banking through 2012, leaving a trail of unrealized promises, broken hearts, shattered careers, and a few clear, profitable winners.” Some of the reasons for this are noted below.
IT potential, but no guarantees
To experts that have looked at the systems over the last 18 months or so, PO technology has the potential to improve forecasting, reduce manual processing errors, create a data trail around exception processing, and simplify workflow around new price posting.
Nevertheless, several factors could reduce its effectiveness, according to various sources. For one thing, the gathering of competitive rate and fee information requires inquiries among multiple sources and is done by far-flung office workers in inconsistent ways, which leads to errors, not the least of which are transpositions and coding mistakes, says TowerGroup’s Khirallah.
Also, cash flow projections by market are often inaccurate, others say, because institutions fail to factor demand elasticity into their projections and pricing recommendations are based on intuition. (Traditionally, too, fee waivers have not been tracked, or have only been tracked on a limited basis, resulting in lost revenue.)
One mixed note is that while the regulators, particularly the Office of the Comptroller of the Currency, are interested in price optimization as a means to guard against fair lending violations, some banks, for that very reason, have an aversion to being known as using the technology.
Overall, De Lotto is a cautious optimist when it comes to the category. He emphasizes that PO systems don’t “automate away” the need for a general pricing strategy and the need to know customers and cost structures intimately. And, as with other business intelligence tools, will not work without clean, accurate data on historical customer data.
Said a bit differently, tailoring the product will only work when you know, pretty exactly, for whom you are tailoring the product. BJ
The electronic version of this article available at: http://lb.ec2.nxtbook.com/nxtbooks/sb/ababj0907/index.php?startid=52
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