Traditionally, pricing in the business-to-business (B2B) world has been static and relatively straightforward. In most cases, prices were set annually or biannually, by contract, and often by a cost-plus-margin equation.

Then the world changed. After several years of dramatic swings and imbalances in supply and demand, periods of high inflation, and now an array of newly instituted tariffs, the old ways are now untenable. Today, if a business cannot adjust prices quickly to reflect rapid and unexpected changes, it could lose customers (who might seek a lower price elsewhere), revenue (if the business absorbs the increased costs), or both.

Meanwhile, organizations are seeing increased opportunities to adapt to this new reality by using digital technologies such as cloud platforms, powerful analytics, and artificial intelligence to build more flexible pricing models. These models help companies change prices to reflect real-time market conditions such as a sudden and dramatic rise in materials costs.

“The goal is to bring both timeliness and precision to pricing,” says Lionnel Bourgouin, managing director and partner at consulting company BCG. “With the use of additional data sources, now there is the ability to update the price in a timely way. You can even move to a particular price point that is specific to your customer.”

The C-suite has noticed, says Bourgouin. “Inflation made pricing an important topic for executives. Suddenly they were a lot more willing to invest in pricing capabilities.”

But B2B companies are still trying to build the muscle that enables flexible pricing. “Despite the huge potential, a lot of companies struggle with how to do it,” he says.

In the consumer market, flexible pricing, sometimes called dynamic pricing, is common practice. Anyone who’s bought an airline ticket knows pricing will be higher on certain days of the week or when the ticket is purchased closer to travel dates. That’s flexible pricing based on inventory—a given flight has a limited number of seats. Uber’s surge pricing raises the cost of a ride after the bar closes, which reflects a pricing model based on times of highest demand.

In B2B, however, flexing a price is more complex. Most B2B prices are governed by contracts, which restrict a seller’s ability to adjust prices. Although contracts increasingly include clauses allowing for price increases—often based on a formula—companies are not accustomed to the real-time dynamic pricing used in consumer markets, where prices can change every few minutes. B2B contract pricing is also opaque. Because prices are negotiated between companies, based on relationships, Company A doesn’t know what Company B pays for the same component from Supplier C.

Consumer pricing is also usually transactional: Take it or leave it. If you don’t like the surge price offered by Uber, you might opt for the local taxi service. But you may still use Uber tomorrow. "One transaction in the consumer market doesn't usually have a tremendous bearing on the next transaction," says Mitch Lee, profit evangelist and vice president of product marketing at Vendavo, which sells pricing software for various platforms, including SAP's.

It all adds up to the fact that B2B companies have work to do to create pricing models that can keep up with today’s fast-changing environment. “Dynamic pricing is hard because you need lots of information, you need it in near real time, you need computational power to turn all that data into prices, and then you need to push those prices to salespeople that are making selling decisions,” says Ben Blaney, sales and pricing practice director at PwC U.S.

This article will discuss the pricing models developing in B2B markets, the challenges of implementing them, and advice on moving to more dynamic pricing.

An array of flexible B2B pricing models

Pricing models in the B2B world roughly fit into four categories: cost-plus, market-based, value-based, and spot pricing. Which model a company uses can depend on the type of product, the application in which it is used, whether it’s covered by a negotiated contract or is an unplanned expenditure, and even the channel through which the product is sold.

Cost-plus pricing: uses readily accessible data.

Cost-plus pricing is exactly what it says—costs plus a percentage margin. The size of the margin varies widely depending on the market and product, usually very low for commodities and higher for more finished goods. The model is the most common because it is simple and relies on data the company already has.

Historically, that data didn’t change much. Today, it does. Recent events have led to more frequent contract modifications, the inclusion of escalation clauses, and even total renegotiations. This requires new data on costs, with frequent updates.

During the inflation spike of the past few years, for example, some manufacturers changed prices nine times, instead of the traditional once-per-year setting of prices pre-2020, says Doug Fuehne, senior vice president at Pricefx, which sells pricing software that works with SAP systems. He adds that tariffs could affect prices even more dramatically. Unlike inflation, which prompted gradual, incremental price increases over years, tariffs are large increases, happening all at once, Fuehne points out.

“Confusion reigns around these items, and many companies are struggling to get their arms around what to do,” he says. “And in today’s tariff-heavy world, if manufacturers do nothing, they could face a significant reduction in gross margin as their suppliers increase costs that are not passed on."

Market-based pricing: relies on timely information.

The market-based pricing model uses timely information on the state of the market and the competition to adjust prices. If there are materials shortages that have competitors scrambling, while your supply is reliable and stable, for example, a price increase may be a good idea.

While this model relies on recent information and analysis, the cadence might be monthly or weekly, but not real time as in consumer industries like airlines and hotels.

Value-based pricing: requires in-depth customer insights.

Value-based pricing is linked to the value the seller’s product adds to the customer’s business or, in manufacturing, what the manufacturer is building. It requires a deep understanding of the customer relationship and knowledge of how the customer uses your product, or incorporates it into its final product.

Vendavo’s Lee uses an example of a business that sells dynamite to a gravel company. If the seller knows that the gravel company produces gravel of various sizes for different customer uses, it might offer advice on how to apply the explosives to crush rock "to spec."

Some software businesses use a similar approach. In software-as-a-service business models, vendors start customers with subscriptions at low price points. As the software vendor learns more about how the customer uses the software, it can add features, which raises the subscription price, says BCG’s Bourgouin. "It's important to know what value-add a specific customer needs, and also what the risk is that they will churn and end the subscription when you reprice at renewal time,” he says.

An AI model could be designed to do just that, providing salespeople with information on what to sell to which customer and at what price. “That’s an area where dynamic pricing is coming into play,” he says. “Some companies are starting to do that.”

Spot pricing: uses data to make arbitrary decisions more strategic.

Spot pricing is ad hoc, happening outside of the contract. It occurs when a tractor manufacturer, for example, experiences a surge in demand and suddenly needs more bolts than is specified in its contract with its bolt supplier. Historically, a spot price might be based on arbitrary factors, including those unrelated to the profit of the business. For example: “Depending on how close the salesperson was to reaching their quota, they might have given a more generous discount,” says PwC’s Blaney.

With the right data and analysis, however, it might not be so arbitrary, or so generous. More companies are crunching data to provide salespeople with acceptable ranges for spot quoting, he says. “That’s a commercial excellence lever that companies are pulling more frequently.”

Such analysis could be especially important in an age of trade wars. Blaney explains how a manufacturer could quickly adjust to a steel supplier raising prices because of atariff increase. If the manufacturer had detailed data on its bill of materials, including how much steel goes into every single product, it could make carefully calculated decisions on pricing each product. For example, say a product contains 20% steel. “Now if the price for 14 ounces of steel has gone up 25%—what does that do to the total cost of that product?” asks Blaney.

Challenges to flexible pricing and how to overcome them

A company might use any one or a mix of these pricing approaches, depending on its level of pricing maturity, data quality, and technology capabilities. However, many B2B companies aren’t effectively using flexible pricing because their capabilities in these areas are undeveloped, says BCG’s Bourgouin.

Here are the main challenges B2B businesses face with implementing more flexible pricing practices and how to overcome them.

Challenge: Antiquated pricing processes. Companies may be following the same pricing approach they’ve used for decades. Worse, they may be using many different methods of determining price. “Companies sometimes have convoluted, inefficient, inconsistent processes,” Blaney says. “They might have 17 different ways of doing the same thing, which precludes any type of automation.”

Possible solution: Methodically inventory and review your processes. Before using any technology to automate pricing, take a careful look at how you go about figuring and setting prices.

Challenge: Low-quality data. Companies might not have the right data. It might be too siloed or disorganized, or it might be too old. For instance, Blaney’s example of adjusting prices based on a new tariff can’t work if the manufacturer doesn’t have the required data. Although manufacturers know the general costs of their bills of materials, “industrial companies don’t always have—down to the ounce or gram—how much steel, rubber or petroleum-based material goes into each of their products,” Blaney notes.

Possible solution: Depending on the industry you’re in and how much your business is affected by such volatility, it may be worth the effort and expense required to gather and maintain data for each product. In any case, given the direction of technology, analytics, and AI, it’s worth cleaning up your data and experimenting in general with flexible pricing.

But don’t let perfect be the enemy of the good. You don’t need to wait until data is fully cleansed and prepared. Instead, use agile software development techniques. “Start small, augment the data, improve the pricing models, see how it works, see what other data you need, then continue iterating,” Bourgouin advises. “We’ve seen this be more effective in getting results faster.”

Challenge: Lack of data science skills. Companies selling to consumers usually have strong pricing teams, including staff who can expertly build algorithms to calculate prices based on changing factors. In B2B, not so much. “They may not have the type of data scientists you need to build pricing logic,” says Bourgouin. “They need to build up that talent.”

Pricing software needs to be accompanied by people with this expertise. “Executives think if they invest in a tool like pricing software, that’s it,” says Bourgouin. But that’s usually not it. “Pricing is extremely complex. If you want to create competitive advantage, then the pricing logic, the actual code that recommends a price point to the sales organization, should be customized,” he explains. “You don’t have to build the tool, but the core intelligence of the pricing engine has to be yours.”

Possible solution: Recruit and hire talent needed to bulk up your data science capability and strengthen your pricing team, or find a partner that has those skills to help.

Challenge: Resistance from the sales organization: Because B2B sales are based on relationships, salespeople play a key role. They may be suspicious of technology that they fear could replace those relationships, and therefore be reluctant to use pricing tools.

Unless they are properly educated about the technology, they may doubt the prices that the tools recommend. “You need to build their trust, so they understand why this new AI model told them this, and so they have the confidence to bring this to their clients,” says Bourgouin.

Possible solution: Educate and measure. Make sure the sales organization understands why the company wants to use flexible pricing, how the technology works, and how it will benefit the business. Then set up metrics and the capability to measure the results of pricing changes, says Bourgouin. “You have to be able to see how it’s working in the market.” If it’s not working, adjust. If it is working, share those results internally with the salespeople, so they develop trust in the solution, he notes.

Flexible pricing for an uncertain future

B2B businesses will continue to wrestle with a world of rapid change and frequent disruption. This will make it increasingly important to develop the ability to quickly adjust pricing. Flexible pricing will become an important tool in their strategic toolbox and a vital key to their ongoing health.