Learn what price optimization is, how price elasticity is calculated, how dynamic pricing works, and why price optimization is critical for maximizing profit.
What is Price Optimization?
Pricing optimization utilizes artificial intelligence and machine learning to measure price elasticity and predict the outcomes of various pricing strategies. It then generates pricing that wins the business without sacrificing the necessary margin.
Modern business moves at lightning speed through many sales channels. The corresponding data grows at an exponential pace and creates increasing complexity as new market triggers force companies to adjust pricing. These dynamic factors include order history, customer behavior, competitive concerns, available inventory, demand swings and market specifics, and more. Each factor uniquely influences price from one selling scenario to the next.
Attempting to manage pricing complexity manually with spreadsheets or homegrown tools exposes companies 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 can capitalize on this inherent complexity that companies are operating within by using artificial intelligence and machine learning techniques to achieve P&L objectives. Price optimization has been developed for both B2B and B2C use cases. IDC draws a distinction between the two groups as follows:
- B2B price optimization applications, which typically focus on pricing products that are sold by a salesperson, are increasingly being sold via B2B eCommerce and direct to consumers via B2C and B2B2C.
- Retail B2C price optimization applications 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. These stages include regular, introduction, promotion, markdown, and clearance pricing.
As the leader in B2B price optimization software, we will focus on the former in this explainer.
What is the Role of Price Optimization in B2B?
Price optimization allows B2B companies to set prices that make sense for each unique selling circumstance. This is a significant challenge without leveraging a practical approach to data science.
The many ways a company prices — list, matrices or tiers, customer 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. These factors include cost changes, competitive dynamics, product velocity, customer relationships and types, geographies, and order circumstances.
It’s no wonder the go-to method of spreadsheets or generic manual tools no longer works for setting pricing that’s critical to meeting financial targets. A more sophisticated approach is necessary.
Read More: How Price Optimization Helps B2B Sales
Why is Price Optimization Important?
Price optimization simultaneously accounts for all the factors that drive price, rationally aligns price/customer/order/product relationships simultaneously, and measures what drives price response in the market. It also simultaneously enforces necessary guardrails and produces price guidance for all the different ways price is expressed in a B2B business.
Read More: Pricing Data Science Blog - FAQs with Lee Rehwinkel
How do you Calculate an 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.
Watch: The Unique Pricing Data Science Behind Zilliant Price IQ
How do 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.
Understanding how different customers will react to price changes – and predicting the revenue and margin outcome - across various circumstances requires price elasticity. Price elasticity is the single most-important factor in setting profitable prices while keeping revenue risk to a minimum. Without 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.
It is possible to measure how market segments respond to price changes and thus optimize outcomes. The data needed to take a 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. Then, measure the price elasticity on an ongoing basis for each segment. Take a surgical approach to pricing and measure price elasticity and set goal-seeking pricing strategies to maximize revenue or profit. This can have a dramatic impact on profits while minimizing risk and improving responsiveness to market dynamics. This approach is what differentiates price optimization.
Read More: Price Elasticity in B2B: The Real Meaning of Price Optimization
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