Deposit Price Optimization - Old Ways Die Hard!

I read just recently that setting the “right” price is an “age-old” challenge in every industry. In the context of deposit pricing in the financial services industry, this statement could read: set the price too low and you struggle to sell your deposits, set the price too high and you sell all the deposits you want, but leave money on the table.

In this series of articles I’ve espoused deposit price optimization as the solution to the age-old challenge. But in developing my position with as much supporting evidence as I can glean from research, I’m confronted with a vexing question, “why has the adoption of deposit price optimization been slow to catch on in financial services, especially in mid-size to smaller institutions?” One of the explanations comes from numerous conversations within the industry that the use of the “old ways” are more comfortable; while at the same time acknowledging that the old ways do not work with any degree of accuracy or do not provide important information to make deposit price decisions. That even in this age of advanced science and technology and the concomitant ability to process and assess huge quantities of data, “Old Ways Die Hard.”

Price Optimization, defined again for new readers to this series, according to Aeronomics, is the application of disciplined tactics that predict consumer behavior at the micro market level and optimize product availability and price to maximize revenue growth. [Price Optimization] is about maximizing revenue from existing business. It’s a hard management science that employs rocket-science mathematical concepts and high-powered computers to crunch gigabytes of marketing information to: 1) accurately assess future consumer behavior under dynamically changing market conditions, 2) determine the most effective way to price and allocate inventory to reach every future consumer, each and every day, making real-time adjustments as market conditions change, with the consumer in real-time, 3) communicate this information instantaneously to distribution and sale outlets which deal with the consumer in real-time, and 4) serve as a decision-support resource for marketing and operations functions, including but not limited to: pricing, scheduling, product development, advertising, sales, distribution, human resource utilization and capacity planning.

I accept the criticism that Aeronomics references price optimization in a retail industry business environment, and that this series of articles is supposed to talk about the application to the financial services industry. So one might justifiably ask where the similarity is. Therefore, this article will highlight a few of the “old ways” the financial services industry currently sets price by revealing how the retail industry used to set price using these methodologies but has since embraced price optimization successfully. In other words, the retail industry was harnessed by the same systemic pricing methodologies before pricing optimization took hold.

To start, it must be stated that there is no difference between other industry use of the technology and the financial services industry. As a major vendor of price optimization software to the financial services industry states on its website, savings products are “virtual commodities” which require a need for differentiation amongst suppliers, that financial service industry customers engage in rate comparison shopping, and that the financial services industry has available highly valuable customer data in which to realize valuable data to increase revenue and margins.

So, have other industries confronted resistance to price optimization…you bet! Again from P.J. Jakovljevic in “Know Thy Market Segment’s Price Response,” May 18, 2007, “Since commoditization, price transparency, price wars, and price erosion are all seemingly here to stay, there is thus an increasing urge to transform the crude, self-destructive, reactive, and other “dark art” pricing strategies of yesteryears that are still largely practiced today. Such archaic methods have companies relying on anecdotes from the field, applying a “cost plus” pricing approach, watching and matching competitors prices to form their pricing strategies.” He goes on to state that “executives who are devoted to using data and analytics in all kinds of other functional areas still think it is entirely acceptable to set prices based on ‘history,’ ‘experience,’ or ‘instinct.’”

Therefore, I submit that the financial services industry equivalents of “history,” experience,” and “instinct,” are comparable to “finger in the wind,” “tiered pricing,” and most significantly, “competitor pricing” methodologies, still used today by large numbers of financial institutions, the “old ways.”

(The following snippets are from an article in Forbes, Pricing Software Could Reshape Retail,” Brian Bergstein, April 24, 2007)

Finger in the Wind. For example, the retailing industry has confronted their own version of the “finger in the wind” methodology “In fairness, retailers long have been hip to this. Hence the common concept of “loss leader” – a routine item like soda is sold at cost or a slight loss, to entice people into a store and establish a bargain reputation. The store hopes to more than make up the difference on other products. But much of that has been trial and error. Enter price-optimization software, and computers’ ability to calculate inhuman degrees of variables. Packed with years of data from stores and competitors, the software predicts how much of something will sell at given prices. And it hunts for items that correlate with each other. So a store can ask many questions at once: If we lower the price of Coke, how much more Coke will we sell? How many fewer store-brand sodas will we sell? And what do soda buyers also tend to purchase that we could bump up by a few cents? Chips? Beer? Shoe polish?”

Tiered Pricing. “A large retail chain had a problem. It sold three similar power drills: one for about $90, a purportedly better one at $120 and a top-tier one at $130….But while drill know-it-alls flocked to the $130 model and price-fretters grabbed its $90 cousin; shoppers often ignored the middle one. So the store sought advice from a new breed of “price-optimization” software…. What followed offers us a clue about important shifts that technology is bringing to retail shopping. After analyzing an array of variables, including sales history and competitors’ prices, the software suggested cutting the middle drill to $110. That might have made the top drill seem more expensive. But drill aficionados still were fine shelling out $130. Sales of that drill didn’t change. However, now that the $90 version seemed less of a bargain, the store sold 4% fewer low-end drills- and 11% more of the mid-range model. Profits rose. Because of insights like this, price-optimization software is often credited with boosting retail profits by a few percentage points – a huge leap in an industry that lives on margins slimmer than a 25-cent pack of gum”

Competitor’s Pricing. “The truth is that for all the sophistication of the retail industry, prices often have been set with a simple formula: the cost to the retailer plus a set markup to ensure a profit. Sometimes there’s even less math. Retailers often match a competitor’s price or replicate what they charged last year. The problem with marking all items up by roughly similar percentages is that some products are more ‘price sensitive’ than others. For many everyday items, like milk, stores can’t get away with a high markup. On specialty products, however, the stores might be leaving money on the table by charging only their set markup. They probably could demand more.”

In articles to follow on Deposit Price Optimization, I will address important questions, such as: How to determine the best rate, or “price,” for every customer segment and channel to maximize financial results while hitting volume targets?, What rate should I set to meet volume and profitability targets for the year, while maximizing institutional economic value? and, What other factors affect deposit price sensitivity other than rate?