Many companies are making the push toward using data and tools to make better decisions and improve performance. While that’s certainly better than relying on gut instinct or guesswork, better decisions are not the same as optimal decisions. After all, a manager’s job is to maximize value, not just slightly improve it, so it’s important to make optimal decisions, not just better, data-driven ones.
The term “optimization” is widely used in the pricing field to describe applications that set prices, but the definition of what optimal truly means has become nearly as murky as the term “Big Data.” Simply stated, optimization is a decision-making process that employs data, algorithms and software to make recommendations faster and more accurately than humans. By examining all possible choices, optimization predicts the outcome of each and selects the one that maximizes business results. When applied to pricing, it examines all possible price choices, predicts the revenue and profit outcome of each and selects the one that maximizes business objectives.
Consider the case of a regional sales manager for a major manufacturer who is striving to achieve revenue growth of 15 percent while simultaneously growing profits by 5 percent on his main product line of hydraulic actuators. Based on his experience alone, the manager believes that raising prices by 2 to 4 percent on small orders will drive his profit target. At the same time, he intends to lower prices on other order sizes by 1 to 3 percent in hopes of picking up additional “wins” in the market and hitting his revenue target.
While there is some logic to this manager’s approach, it only differentiates price based on one factor – order size. Additionally, there is no firm prediction of what the profit and revenue outcomes will actually be, so the manager is essentially guessing how much price change is needed to hit his targets.
As an alternative, price optimization can validate the presence of distinct customer types, statistically significant order sizes and product velocity effects that reflect lifecycle behavior on every SKU in the hydraulic actuator family, resulting in thousands of distinct price segments. Using this fine-grained segmentation, specific price adjustments can be recommended for each combination of customer type, order size and SKU velocity. For instance, one customer segment ordering $10,000 of a slow-moving SKU might receive a 1.4 percent price increase, while another customer segment ordering $130,000 of a fast-moving SKU might receive a 2.9 percent decrease. The price optimizer can predict the combined effects of all price adjustments after carefully considering the market response to these price changes in order to exceed the manager’s revenue and profit goals.
Price Elasticity Measurement is Central to Optimization
Optimization is not just about using automation tools to make faster decisions or using data to make better decisions, although both will happen when optimization is applied. The deeper purpose of optimization is to find the decisions which lead to the maximum output. In other words, find the prices that result in the best revenue or margin outcomes for each part of your business. The goal is not just to have different prices tomorrow than you had yesterday, it’s to hit specific revenue and margin targets using price as the lever.
In order to predict the revenue and margin outcome of any price change, you must know how different customers will react to price changes across various circumstances, which requires knowledge of price elasticity. Price elasticity is the single most-important factor, ahead of average selling prices, cost-plus margin targets or firm limits on price discounting authority, in setting profitable prices while keeping revenue risk to a minimum. These other well-meaning factors are blind to their own impacts, but elasticity sees what the outcomes will be before you make any price moves. If you don’t understand price elasticity for a given customer segment, you risk leaving money on the table or losing profitable sales.
Most B2B companies do not use price elasticity to set prices because they assume they can’t. Instead, these companies rely on backward-looking analytics or statistical distributions of prices. It’s been a long-held belief that price elasticity is impossible to calculate in a B2B selling environment. That’s simply not true. Advanced analytical techniques make it possible to measure how individual market segments respond to price changes in B2B markets and thus optimize outcomes.
The data needed to take this scientific approach to price optimization already exists. It’s readily available transaction data — the customer, product and order data that every company captures in the course of doing business. From that data, you can segment customers into small groups that have similar price responses and measure the price elasticity on an ongoing basis for each segment. Taking a surgical approach to pricing, or actually optimizing prices by measuring price elasticity, can have a dramatic impact on profitability while minimizing risk and improving responsiveness to market dynamics.
Of course, understanding buyers’ true price sensitivities at a line-item level is not easy, but the technology exists to handle all of the heavy-lifting, instantly and automatically generating optimized prices. Take an electrical manufacturing company for example. The company might have a robust project-based business, with strong customers that are 100 percent negotiated. By leveraging price optimization and taking a surgical approach to pricing, the company, which may have more than 500 field sales reps and several hundred distributors with volumes approaching 40,000 quotes per month, could see margin dollars increase into the millions. The result of price optimization in this market can significantly streamline business processes through pricing standardization. The process also can improve turnaround times on price quotes, measured in hours instead of days.
Why Hindsight Analytics Fail
B2B companies often choose the path of least resistance when embarking on a pricing project. That path usually begins with analytics — report-centric, hindsight analytics, to be more specific. While there may be some value in knowing where you’ve been, backward-looking analytics can’t provide value when it comes to making better pricing decisions in the future.
When it comes to pricing, reports and hindsight analytics often show where pricing mistakes were made, such as when a company discounted significantly from the list or matrix price. However, these reports don’t tell you how to set and dynamically update prices, negotiate pricing agreements going forward, and more importantly, how customers will respond to those prices. This lack of insight often causes companies to either leave money on the table or lose the sale as a result of being too aggressive.
Despite their appeal to B2B companies, the reality is that the hindsight-analytics approach to pricing crumbles for companies that face the massive complexity of modern B2B pricing. These companies inadvertently relegate millions of price decisions to their sales reps, forcing them to guess the best prices to hit company objectives. The predictive analytics approach based on the measurement of price elasticity can quantify the true factors that affect price outcomes, predict customer buying response to different prices and enable companies to predict and control how pricing strategies will impact their profit and loss statement.
Barrett Thomson is the general manager of Zilliant’s pricing excellence solutions. He has more than 20 years of experience working with businesses and their complex pricing environments.