Price Optimization is bandied about frequently in the financial services industry, but the true root began with Nomis founder Robert Phillips who ported some concepts from industrial manufacturing and high tech and devised and proved out new concepts unique to financial services. Phillips is credited as one of the founders of the price optimization space serving as CEO of Talus, the company widely lauded as the price optimization pioneer.
While today there are consulting firms who claim to provide “price optimization,” their approach runs counter to what Phillips conceived of as a founding principle of the space. Namely, he believed that to truly optimize, any solution would require powerful software capable of continuously crunching large data sets on a continuous basis to live up to the promise of “optimizing” the results over time. Price Optimization today is widely regarded as the nexus of big data software requiring massively parallel systems to perform cutting-edge statistical modelling, and a continuous flow of bank, market, and competitor data evaluated on an on-going basis.
The value of price optimization for deposits has now been widely established across global banking markets. Across 15+ implementations with top 100 banks, Nomis clients have realized significant business results with measurable profitability increases of 10 – 20%+ through savings in net interest expense and increases in balances. Our customers have achieved these results by using Nomis Price Optimization to answer questions such as:
Precision – The Nomis approach to price optimization is based on performing a complete set of analyses that maximize bank returns. These analyses take into account corner cases where money is frequently left on the table by other approaches.
Nomis plots optimizations across an Efficient Frontier so banks have a variety of optimal choices depending on their strategy.
Similar to the efficient frontier developed in modern portfolio theory, the Efficient Frontier in pricing optimization specifies the best outcome (be it maximizing volume growth, or maximizing net interest income, or reducing interest expense) that can be achieved given the tradeoff you would be willing to accept (e.g. interest expense, volume, balance run-off, etc.).
The Efficient Frontier allows the bank to find the optimal tradeoff between deposit balances and net interest income or expense, reflecting all operational and regulatory constraints while optimizing deposit pricing. Lesser approaches give “one answer’’ in a black box fashion which boxes banks into a single choice which may no longer be valid after it is calculated.
Another important data check is Price Sensitivity Dispersion. To assess the validity of price optimization, it is vital to understand if price sensitivity is widely distributed in a portfolio or highly concentrated. A highly concentrated portfolio may see a single erroneous effect limiting the results. A scenario might be an MSA that includes a university where students are highly sensitive to a given product price, but the rest of the population is insensitive. Lumping this group together without further analysis would create invalid rates and limit profit potential.
Another concept central to the Nomis approach is the measurement of Balance Distribution Entropy. This basically measures how distributed or concentrated your balances are across your pricing segments. If all balances are concentrated in one pricing cell, “balance entropy” is 0. If balances are distributed evenly across all your pricing cells, it is 1. The general notion is that the more the “entropy”, the greater the opportunity to optimize.
In deposit price optimization, we estimate hazard rates for each pricing cell and each component of your flow of funds. Typically, this results in hundreds or thousands of hazard rate estimates for a typical deposit portfolio. For a deposit portfolio, a high hazard rate indicates a segment of the portfolio that’s at high risk of attrition in a rising rate environment while a low hazard rate segment indicates stable funding. Optimizing your interest rates, lead/lag strategies, and product strategy based on a sound understanding of hazard rates is core to deposits optimization.
Flow of Funds modeling is another concept central to the Nomis approach to deposit price optimization. Our methodology models four different flow of funds components and estimates the price sensitivity (hazard rate) of each one:
Saturation is a typical safety check that other approaches to “price optimization” do not take into account. Every cell of a resulting pricing table must be checked to insure that demand for a given product does not exceed the total market demand at that price. This ensures no erroneous data can be put in market.
Scope – The Nomis approach to “price optimization” is to make true optimization possible by putting powerful software in the hands of end users to make continuous use of rate optimization on an end-to-end basis. Nomis believes it is not enough to simply create and refresh great rates. Banks also need to quickly get them into market and learn from live experience.
Active Recalibration – Keeping models valid is a continuous exercise, not a one-time consulting engagement. The Nomis approach is to leverage Active Recalibration to tune data and models on a regular basis. As market conditions change, variables need to be reevaluated. Nomis’ customers can rest assured that what they are recommending to and deploying in the rest of the business is accurate and based on the latest information.
Ability to Operationalize – Many price optimization exercises developed by consulting firms fall short as back room, spreadsheet-based one-off intellectual pursuits. Nomis leverages software to provide and end-to-end approach, giving banks the ability to quickly operationalize newly optimized rates. The rates flow into a deal management application that gives the frontline in the branches and call centers clear guidance as to what they can offer to create win-win financial solutions for the customer and the bank. It also helps banks record the offer flow to optimize human performance as well and guard against policy and regulatory violations.
Closed-Loop Feedback – Nomis believes that better products emerge when banks listen to their customers. In our frontline execution systems, offer performance is continuously recorded so that the lag time between identifying when an offer isn’t working and the replacement of the offer with a new one, is greatly reduced. This data helps banks isolate whether the problem lies with the sales approach, the offer itself, or both by comparing the complete business at any level for example, from branch to branch or region to region.
Lead/Lag Strategies – Nomis Price Optimization for deposits allows you to determine the optimal timing of rate increases in a rising rate environment based on your chosen operating point on the Efficient Frontier.
The optimal timing of a rate change is driven by the measurement of underlying hazard rates (price sensitivity). Simplistically, you should adjust your rates faster for segments of the portfolio that have high price sensitivity and can lag a bit longer for segments that have lower price sensitivity.
To get it right (and save 2 – 4 bps for each 25bps Fed funds move), you can use our software and science to determine the optimal price movements and timing given your chosen goals and constraints on the efficient frontier.
Peer Competitiveness – In Nomis Price Optimization for deposits, peer competitiveness is measured through a combination of the Nomis Market Price Index and the Hazard Rate for each balance flow component.
The Nomis Market Price Index is an empirically derived weekly measure of the competitive and substitution offers for each segment of your portfolio. Importantly, there is no single “Market Price Index” in our methodology but a unique measure for each pricing cell. This ensures that you can accurately forecast the impact of a competitor’s price moves on each of your product pricing cells.
Forecast Horizon – The forecast horizon for price optimization depends on your portfolio goals. The Nomis Price Optimizer allows for a forecast horizon across multiple years, however the core focus for pricing science is to accurately predict and optimize short-term marginal balances changes as a function of pricing and competitive dynamics. Practitioners of Nomis Pricing Science monitor their portfolio with weekly data updates and reforecasts and then re-optimize their rates on a monthly basis.
At Nomis, what has emerged from more than a decade of building price optimization software specifically for banks is an approach that eliminates potential pitfalls and maximizes returns. Unlike backroom efforts, Nomis takes price optimization to the frontlines to blend optimal rates with optimal front line human performance to ensure new offers reach the customer effectively. Like our approach to continuously optimizing rates with powerful big data software, we crunch the data on actual results as well to truly put banks in a real-time driver seat to maximize their results.
Used by 10,000+ front-line bankers to enhance customer experience.
$2 trillion+ in managed assets and liabilities across 30+ instances.
300 million returned to banks every year.