Defining how price optimization software works, how price elasticity is calculated in B2B, data science techniques, and why price optimization solutions are superior to a rules-based approach in delivering meaningful P&L impacts for complex B2B businesses.
What is Price Optimization?
The best pricing optimization software utilizes artificial intelligence to measure price elasticity and predict the outcomes of various pricing strategies to generate revenue- or profit-maximizing prices.
Modern business moves at lightning speed through many sales channels. The corresponding data that gets captured grows at an exponential pace, creating ever-increasing complexity as new market triggers force companies to adapt. These dynamic factors – including order history, customer behavior, competitive concerns, available inventory, demand swings and geographical market specifics, to name a few – each uniquely influences price from one selling scenario to the next.
Without the right price at hand for each unique circumstance, an unintended consequence of trying to manage pricing complexity manually with spreadsheets or homegrown tools, companies are exposed to serious margin leakage.
Analysts commonly use spreadsheets and other manual tools to set and manage prices. If your business only has a handful of products and customers, this tends to work well. What about companies with tens of thousands of SKUs and thousands of customers? A massively complex business environment is the new reality for most B2B companies.
Price optimization software that uses artificial intelligence and machine learning techniques can capitalize on this inherent complexity that companies are operating within to achieve P&L objectives. AI price optimization has been developed for both B2B and B2C use cases; IDC draws a distinction between the two groups as follows:
- B2B-focused price optimization applications, which are typically focused on pricing products that are sold by a salesperson but are increasingly being sold via B2B eCommerce and direct to consumer via B2C and B2B2C.
- Retail-focused B2C price optimization applications, which are customized for pricing retail merchandise across channels and life-cycle pricing capabilities to price merchandise as it moves through the various stages of retail life: regular, introduction, promotion, markdown, and clearance pricing.
As the leader in B2B price optimization software, we will focus on the former in this explainer.
How Does Price Optimization Software Work in B2B?
Setting prices that make sense for each unique selling circumstance within a B2B company can be an amazingly challenging endeavor.
The many ways a company prices — list, matrices or tiers, customer-specific agreements, spot negotiations and overrides, all of which are interconnected — drive complexity. Distributed pricing decisions, large customer and product counts, and complex product configurations further complicate the pricing process. It can become unmanageable to account for all of the factors that influence price, including cost changes, competitive dynamics, product velocity, customer relationships and types, geographies, and order circumstances.
It’s no wonder the go-to method of complicated spreadsheets or generic manual tools no longer suffice in setting the nuanced pricing that’s critical to meet P&L targets.
Rather, a more sophisticated approach is necessary. Price optimization simultaneously accounts for all the factors that drive price, rationally aligns price/customer/order/product relationships simultaneously, and statistically measures what drives price response in the market, all while enforcing necessary guardrails and producing price guidance for all the different ways price is expressed in a B2B business.
The data needed to take a scientific approach to price optimization already exists in most businesses. 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 response 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 and setting goal-seeking pricing strategies to maximize revenue or profit, can have a dramatic impact on profitability while minimizing risk and improving responsiveness to market dynamics.
How to Calculate Optimal Price Point
Price optimization explores all the factors that influence price to create a statistically and strategically relevant price segmentation structure. The resulting micro-segments are typically a function of product, order and customer attributes. The most effective segmentation structures balance model sophistication with explanatory power. In other words, if you can get to 90 percent explanatory power with eight attributes, but to get to 92 percent explanatory power you would need to five additional attributes, it may not be worth the trouble or added complexity. 90 percent will suffice.
Segmentation attributes, such as customer size, geography, order size, product velocity, product category, etc., are typically arranged in a tree structure. As is common in B2B, there will likely be some nodes in the tree with little to no transaction data available. Advanced statistical techniques can be used to ensure your optimization model can derive clues from nearby nodes in the tree and come to a determination on the market price. Importantly, the underlying data science is exposed to the user, empowering them to explore different variations of the segmentation model, add new attributes, or create an entirely new structure as needed.
Once similar transactions are grouped in the proper segments, price optimization solutions can pinpoint the market price for each segment, and begin to use that as the foundation for how to set prices going forward, including factoring in a key, and often overlooked step: measuring price elasticity to understand how a change in price will impact a change in win rate or volume, segment by segment.
Can You Measure Price Elasticity in B2B?
The purpose of optimization is to find the set of inputs that lead to the maximum output. In other words, find the prices that result in the desired revenue or margin outcomes for each part of your business. The goal is not just to have different prices, it’s to hit certain revenue and margin targets, using price.
In order to predict the revenue and margin outcome, you have to 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 in setting profitable prices while keeping revenue risk to a minimum. If you don’t understand price elasticity for a given price 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.
The most effective price optimization solutions use price elasticity to calculate the revenue-maximizing and the profit-maximizing price for each price segment, thus allowing the user to optimize prices for different objectives and detect when segments are priced too low or priced too high for the market.
How Does Optimization Correct for Previous Pricing Mistakes or Irrational Pricing?
When pricing decisions are largely decentralized, it’s not uncommon for historical prices to be out of alignment with respect to rational price relationship expectations. For example, small customers get better pricing than large customers, all else being equal, or premium products sell for less than mid-tier products, all else being equal. Pricing along customer relationships, order size and product value dimensions can quickly become irrational, putting sales rep confidence in prices and customer satisfaction in jeopardy.
Constraint-based price optimization solves this challenge. If a pricing model built with constraint-based optimization could speak, it would say, “Tell me all the relationships that I need to respect and I will simultaneously compute all those prices rather than writing a thousand if/then statements.”
By simultaneously accounting for business rules and price relationship requirements through constraint-based price optimization, you avoid the hazards of conflicting rules.
Using Price Optimization in Practice
True price optimization is not a black box, but rather a crystal box. Once a pricing model is built, a pricing analyst or category manager needs a mechanism to interact with it. Given that optimization is a goal-seeking activity, a pricing analyst can set specific revenue and profit goals for different pricing segments at varying levels of granularity and subject those goals to a set of business constraints, and view the predicted revenue, margin and volume impacts of those price changes before putting prices into market.
For example, I’d like to maximize profitability on product category A in the Southwest region for small customers, but don’t raise my prices by more than five percent. In a different segment, large customers in the Northeast on product category B, I’d like to take more share and be revenue aggressive, but keep a minimum margin of 30 percent and don’t lower prices by more than seven percent. And, for all other segments in my business, take a more balanced approach to revenue and profitability.
Once the optimizer runs the various pricing strategies, the pricing analyst can drill into the recommended price changes across products and customers and view the predicted impact to ensure the strategies will produce the desired P&L results. The “what-if” scenario capability coupled with the predictive nature of price elasticity, is key to using price as a strategic lever to improve profitability.
Where Do Optimized Prices Go?
The difference between winning and losing an important deal often comes down to how quickly the right price is put into the hands of sales or in front of customers. Price guidance from a true cloud-native price optimization solution is seamlessly delivered into any ERP, CRM, CPQ or eCommerce application in real-time and dynamically adjusted as deal conditions change. Whenever, wherever — that’s the power of price optimization and it’s table-stakes for a modern B2B business.
Dynamic Pricing Example Using Price Optimization in B2B
The building products manufacturing industry is extremely competitive, fragmented and complex. As countless suppliers battle over the same pool of projects, prices tend to follow a downward trajectory. Often, sales reps face the choice of either losing the bid or slashing the price to a point that decimates profit margin. This is a big problem as material costs fluctuate.
For discrete products manufacturers, who may sell standard products through distributors and have a direct sales force, it’s important to find the right mix of system/matrix and customer-specific pricing while minimizing deviations.
Each of these industry undercurrents ring true for TERREAL, a €376 million French manufacturer of clay building materials, which employs nearly 2,500 people around the globe. Known for its specialty in terra cotta, TERREAL provides solutions for the entire building envelope, most notably roofing, structure and façade/decoration.
More than 150,000 homes are built or renovated with TERREAL tiles each year, and another 20,000 are built with TERREAL bricks. The company’s rapid growth and expanded global footprint introduced more layers of go-to-market complexity, as customers could buy from regional depots, distributors, eCommerce sites and retail stores. Aligning prices across sales channels and enforcing rational price guidance among sales reps became a heavy burden.
TERREAL revamped its pricing strategy with price optimization to price strategically. Prices that had decreased over time below the floor price have been moved back up to a fair and profitable level. Using price elasticity as a guide, TERREAL has been able to run campaigns that surgically change prices in the business to achieve varying P&L objectives. TERREAL is now getting fair value for its products, without losing volume or facing customer pushback.
Terreal has been above market performance in terms of volume and price!