After years of deflation, raw-material prices are beginning to increase again. When their factor costs first began to balloon, many companies responded by cutting budgets and trying to improve procurement. Recently, however, many industrial companies have begun to reexamine pricing—the response to rising costs that could potentially deliver the most value.
Although businesses in many sectors regularly tinker with pricing, industrial companies have typically implemented modest increases at year-end. Now they’re rethinking both the frequency and extent of these adjustments. And for some industrial companies, the increase in raw-material costs is prompting greater questions about their traditional reliance on “cost-plus” pricing. This trusted approach does guarantee profits, but they may not be optimal. In its place, industrial companies are considering a move to value-based pricing, based on a customer’s willingness to pay.
This shift in industrial-pricing architecture may sound straightforward, but it’s hard to implement at companies that offer hundreds or thousands of products across multiple brands, each of which has a different value. But a new approach that relies on advanced analytics can help industrial companies overcome many obstacles. It allows them to determine value in all major product categories—standard units, configured products, engineered products, spare parts, and kits—using different analytical methods. With these insights, industrial companies can accurately determine the extent of potential price increases. The underlying analytics are much more accurate than they would have been even a few years ago, thanks to improved algorithms and greater availability of data.
In addition to challenging the assumptions underlying price calculations, companies should take this opportunity to reexamine other aspects of their industrial-pricing strategy—both category-specific problems and issues that affect the entire portfolio, such as methods for determining discounts. When these practical insights are combined with analytics, industrial companies may be able to increase margins to an extent that previously seemed impossible.
Catalyzing change in industrial pricing
The recent rise in raw-material costs is unprecedented. Since 2017, for example, the Stainless Steel Index has risen 25 percent, and costs for nickel and ferrochrome have soared (Exhibit 1). Plastics have also seen a sharp rise, with the Global Low-Density Polyethylene Index up more than 7 percent since 2015. And these cost increases are only part of the problem for industrial companies—in the past year alone, major governments worldwide have imposed tariffs and duties that are further increasing raw-material costs.
Many companies are making steep, midyear price changes in response to their escalating internal costs. For instance, many heating, ventilation, and air-conditioning (HVAC) companies raised prices in June 2018, rather than waiting for the traditional months of November or December. The magnitude of the price increases has also been higher in 2018, with one leading HVAC player increasing prices by 5 to 8 percent for products across its portfolio, compared with the historical 3 to 6 percent.
Some industrial companies, especially in sectors that have experienced eroding margins, are also taking this opportunity to reset overall industry profitability. Consider the lighting-fixture subsector, which has experienced falling prices over the last four years as its products became increasingly commoditized. After a few companies in this subsector increased their prices by 6 percent in 2018, they were quickly followed by others. The extent of the increase should provide margins far above those obtained before the spike in raw-material prices.
Although higher or more frequent price adjustments provide some relief, most industrial companies aren’t using advanced analytics to determine the new numbers. They’re just adding a few percentage points on top of their usual markup and moving up the schedule. While industrial companies are obtaining higher revenues, the new prices may be well below the amount that customers are willing to pay (or in some cases, so far above it that sales could dip). Before they make any further adjustments, it’s time for a more structured approach to pricing that focuses on value.
Determining baseline pricing performance
The first step to better industrial pricing involves examining current performance—an exercise that could uncover problems that have long gone under the radar. At one industrial company, managers had historically raised list prices by 2 to 3 percent annually, believing this increase allowed them to maintain profitability even as inflationary pressures intensified. But the pricing review revealed that price increases varied greatly by brand. In addition, even when the company raised list prices by 3 percent annually, the average sales price (ASP) did not keep up (Exhibit 2). For some products, the gap was widening between list price and ASP because the company often increased distributor discounts to keep its customers happy.
A combination of factors had pushed the company into this position. First, it lacked an integrated business-intelligence system that tracked detailed pricing information for each product. Each brand also relied on different pricing tools and independent sales forces, which made it difficult to see how industrial pricing and profitability varied across brands and regions. Further, the company incorrectly scoped the costs for many of its products—sometimes because it relied on outdated information—and that led to large gaps between the booked margin and realized margin.
If companies want to avoid these issues and obtain real pricing transparency, they’ll need to build a comprehensive transaction database that has accurate price, cost, volume, and margin information for all their products, including engineered-to-order and configured, made-to-order offerings. The database should include detailed records about pricing and discounts to enable a pocket-margin view at both the SKU and customer level.
Taking a new approach to product segmentation and list pricing
After determining baseline performance, industrial companies must consider some difficult pricing issues. What makes a customer easily accept a $5 increase in a product’s price but balk at a $7 increase? Why does a 3 percent discount have no effect while a 4 percent reduction doubles business? At most companies, pricing experts wouldn’t be able to answer these questions or similar ones.
Although they know that prices are largely based on a customer’s willingness to pay, they lack the analytics to determine what “willingness” truly entails. It’s much easier to default to the more simplistic cost-plus approach to industrial pricing, especially at companies with numerous products. One industrial company, for instance, had more than 100,000 spare-part SKUs for a single brand, and it’s not uncommon to find other businesses with the same amount or more.
Despite this inherent complexity, companies can accurately estimate customers’ willingness to pay, using advanced analytics across all product categories, even if multiple SKUs are involved. These insights, combined with knowledge of category-specific issues, will help industrial companies set optimal list prices.
Standard made-to-stock units
For standard units, companies should default to attribute-based pricing. They’ll first need to segment the portfolio into groups of similar products and identify the most important features in each one—for instance, size, material, or performance—based on their experience in the field. After compiling these data, companies can use analytical models that assess all attributes and determine what each one contributes to product value. The models consider various factors when assessing value, including historical pricing data about products with similar features. Of course, business leaders will have to consider the model output in combination with their own market knowledge. They can’t go with a model’s recommendation to price a product at $1,000 if the leading competitor only charges $900.
Unlike configured or engineered products, which are tailored to individual customers, industrial companies may sell large quantities of standard, made-to-order units, so it’s also important to consider price in relation to volume. The rule is simple: high-volume customers get better prices.
One industrial company analyzed individual products to determine the value of their attributes and then looked at the volume sold to each customer. With these insights, it was able to create a new price curve that took both factors into account. The recommended prices reflect the highest reasonable starting point in negotiations, based on both analytics and knowledge of the market. Companies that conduct similar analyses when resetting prices can expect to increase their return on sales for standard units by about 3 to 5 percent.
Configured, made-to-order units
It can be especially difficult to price products that are uniquely configured to meet customer requirements, especially if the customer base and feature set is large. For instance, some companies may have several hundred products with thousands of customizable features, resulting in more than a million potential offerings. In such cases, companies should first disaggregate pricing—in other words, determine the value of the base model and each individual attribute, similar to the process for pricing standard units. But with configured products, industrial companies must also examine each customer segment when developing attribute prices. Buyers of both low-end and high-end cars can opt to have leather seats, for example, but analytical models might show that they differ in what they’re willing to pay for this feature.
Customers order engineered units to be built from scratch according to their specifications, so industrial companies typically don’t have any similar products that they can use to establish baseline pricing. Often, deals for these units are linked to a specific project, and the exact unit specifications may be uncertain at first. To price engineered units accurately, industrial companies must determine all the features that they are likely to include, as well as the customer’s willingness to pay for each one.
During projects for engineered units, customers frequently alter the specifications described in their original order. To maintain their margins, industrial companies must again follow a standard value-based methodology for pricing these changes.
In addition to pricing, industrial companies can take some other steps to improve margins on engineered-to-order units. For instance, these projects typically have long lead times, and procurement costs for raw materials may be much higher than those estimated at kickoff. Companies must factor the potential for such increases into their initial estimates—something few now attempt—or else they risk underpricing their units.
Some industrial companies may also try to increase margins by trying to shift customers from engineered products to modular products with similar features, such as a wiring system that has all essential components. Although modular products aren’t customized, they still frequently meet all customer needs. They also have lower production costs, which can increase an industrial company’s profitability. The move to modular products doesn’t technically qualify as a change to pricing architecture, but it has the same objective: better returns.
In our experience, spare parts and consumables typically account for about 20 to 40 percent of revenue but make a much larger contribution to a company’s profitability. As in other categories with numerous SKUs, companies may be reluctant to move from cost-plus pricing. But a value-based approach can work within spare parts, provided that companies make a few category-specific modifications.
When determining the value of spare parts, industrial companies should first look at attributes like provenance (whether they are proprietary or nonproprietary) and transaction features, such as the frequency of purchase. They should then estimate a customer’s willingness to pay by examining proxies such as resale price (what distributors charge customers) or a spare part’s list price as a percentage of the base-unit or kit price. If they use the latter method, they should strive for consistency across the portfolio. For instance, an industrial company might charge $10 for a spare part used in a $100 base unit (Exhibit 3). For a second base unit that costs $200, it might raise the part price to $15. That small increase doesn’t truly reflect the customers’ willingness to pay, since the company was able to price the part at 10 percent of the base unit’s price with the first product. To get that same ratio on the second product, the industrial company would need to raise the cost of the spare part from $15 to $20.
Companies often set kit prices by adding up the cost of all the items that they contain. While this approach does allow for consistency, unit pricing might be a more appropriate benchmark. For instance, industrial companies might offer a kit that contains dozens of components that typically must be ordered from different suppliers. The unit price would factor in the value of convenience to customers, many of whom want to avoid tracking down and ordering multiple parts. Customers might also appreciate that kits eliminate uncertainty about whether different parts will work well together.
Setting discounts and commissions based on performance and customer segment
Industrial companies don’t typically follow a standard approach when setting discounts. Some companies determine them based on a product’s sales volume or performance tier. Others apply standard discounts, regardless of volume, or else reward distributors based only on their willingness to comply with certain requests (such as agreeing to engage in extensive customer outreach in certain segments). Many companies default to this approach because they’ve grown through acquisitions. The company and its target may have used different methods for calculating discounts for their brands, and the new entity simply retains the old methods.
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For best results, industrial companies should follow a consistent discounting approach across products that considers factors such as sales volume, market demand, and desired distributor behaviors. The specific considerations factored into the calculation, as well as their relative importance, may vary by company. For instance, some industrial companies may provide the highest discounts to distributors that carry a large inventory of long-tail SKUs because they want end customers to have immediate access to essential spare parts. But others might prioritize distributors that have highly trained support staff who can help end customers understand complex products.
Creating systems and processes to track risk, compliance, and value capture
To build confidence in their new initiatives, industrial companies should develop performance-management systems, supported by tools, that emphasize the importance of pricing. Some industry leaders have created tools that allow sales representatives to enter key details for all deals, such as the size and frequency of transactions. The tools then analyze the data and derive insights that assist with industrial-pricing decisions. In parallel, the company created a central pricing team that investigated whether price increases appeared to be affecting sales volume and value capture. It also monitored all business units to ensure that they were following pricing and discount guidelines. For any deviations, the team attempted to determine the cause.
The increase in raw-material costs could be an unexpected blessing for industrial companies. Faced with lower margins, they’re taking a hard look at pricing for the first time in many years and questioning whether their cost-plus approach is truly best for their business. Shifting from cost-plus pricing to value-based pricing may allow companies to improve return on sales by an average of 5 to 10 percent. And that means pricing could be the lever that delivers the greatest and most immediate impact in a market where companies face increased competition and soaring costs.
In the course of working with each of our clients, we identify a range of possible alternative options to improve its position and profitability. These options may relate to your conduct in the marketplace and may, therefore, be governed by competition or antitrust laws. Our recommendations and analyses assume action consistent with, and in no way are meant to imply that steps be taken contrary to, any applicable laws. McKinsey does not render legal advice; if you have any legal questions relating to these options or our advice, we recommend that you seek legal advice prior to taking action.
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