Logistics operators play a vital role in the global economy. They connect production and consumption in complex—and rapidly changing—physical networks. Their performance and resilience are crucial for maintaining modern markets. Yet the logistics industry often struggles to appropriately price the critical work it does.
Our research suggests that pricing is both more controllable and more important for logistics operators than we often hear in industry conversations. We studied 21 logistics companies over a six-year period from 2017 through 2022. The top performers grew prices 20 percent faster annually than the median. This relationship often held even across subsectors: Whether in road, rail, ocean, or air, it was generally the case that a few companies grew prices faster than the rest.
Even more important, the majority of companies in the panel that delivered top-quartile EBIT growth also delivered top-quartile price growth. This correlation held even when separating prepandemic and postpandemic periods. Industry leaders are not relying solely on cost discipline and sales growth—they are using price to excel.
This article outlines four pricing strategies that we see driving outperformance in the industry today:
- pinpointing willingness to pay
- digitizing deal reviews
- using price to shape the network
- automating price execution
While each strategy is conceptually timeless, maturing AI technology is dramatically lowering the barriers to implementation. We are seeing logistics companies reimagining their pricing with more urgency and pace in the last 18 months than we ever have before.
Pinpointing willingness to pay
When we talk about pricing with companies, the most common challenge we hear is commoditization. Customers see little difference among providers and as a result they cross-shop aggressively. It’s true that logistics is more commoditized than some other sectors—but that does not mean differentiation is nonexistent. Advanced analytical engines can find what we call the “millimeter gaps”: nuanced sources of differentiation in the marketplace.
For example, one road freight player’s machine learning engine identifies hundreds of distinct characteristics relating to lanes, schedules, and end uses, all of which can drive differences in how much shippers are willing to pay. Importantly, the engine is not simply trying to raise prices. It estimates price–volume elasticity at the deal level and then maximizes margin.
While these advanced analytical approaches existed five years ago, recent advances in gen AI and agentic AI have dramatically expanded the pool of usable data. Three years ago, for example, customer and cargo types were often defined in broad categories. Now, an agent can read free text from multiple unstructured sources (such as a bill of lading, invoice, or company registration) and create a much more granular categorization of these input variables. That increased granularity translates into a more powerful pricing engine.
Of the four strategies we outline in this article, analytically pinpointed willingness to pay is the most widely adopted across all subsectors. Logistics companies that are not using an analytics engine for spot pricing are likely falling behind.
Digitizing deal reviews
If perceived commoditization is the most commonly cited logistics pricing challenge, negotiating contracts is the second most common. Large shippers run aggressive, structured procurement processes designed to place pressure on providers. To a commercial team tasked with tender negotiation, it can feel like the shipper has all the information and advantages.
Leading companies challenge this mindset. They see instead a world in which they contract their service thousands of times per year, while the typical customer only contracts it once. That means the logistics provider has access to a broader picture. Annual contracting season can be a precious resource—the key is to employ technology to codify pattern recognition and make it usable.
In one example, a company digitized its deal review process, creating an accurate record of every current, proposed, and past deal at the lane level. The result yielded a vast information advantage regarding patterns in both customer and sales team behavior that outstripped all but the largest shippers. Deal review conversations could then shift from general perceptions about the shipper to specific observations about which services are most competitive, where pricing drives decisions, and how a proposed deal compares with both previous deals over time and to deals with similar shippers.
A better approach is possible for any company that can view its historical contracting activities as a precious resource to be mined.
Pricing to support the network
Logistics operators know that no two shipments are alike. Where, when, and how freight moves through a network creates substantial variation in cost and complexity.
The challenge is quantifying those complex relationships and translating that insight into practical pricing decisions. In a tender, the problem is magnified, as the shipper contracts for thousands of pieces across the network—many of which will not materialize as promised. Worse yet, logistics providers are often left in disadvantageous situations when shipper volumes end up being more costly or complex than expected.
The most sophisticated logistics companies are rethinking this time-worn problem using data analytics. Digital twins can quantify the incremental value of each piece of volume in a network. One provider recently built an engine that allows a commercial team to quickly see how beneficial volumes are to the network’s long-run economics. Armed with these numbers, the commercial team can confidently negotiate pricing and volume trade-offs at the lane level.
Logistics companies can also borrow a page from insurers’ playbooks by quantifying variation and risk. How consistently does a given shipper deliver their contracted volumes? Do they typically induct during operational peaks or valleys? Are their volumes stable or cyclical? How often do they induct irregularities (such as improperly packed freight or missing paperwork) into the network? These are answerable risk questions that can be calculated and priced.
Companies that use this approach to reimagine network pricing remove much of the guesswork. Their commercial teams don’t wonder how “strategic” a given deal is. They instead use insights to confidently seek and price the volume their networks need.
Automating price execution
This article has so far focused on approaches to setting a price. In our experience, however, actually getting the price is a frequently overlooked pool of value. In logistics, product complexity and legacy technology architectures collide in byzantine pricing processes that typically involve multiple system handoffs and myriad manual steps. Plenty of opportunities for revenue leakage result, to go along with the administration costs.
Price execution can be an ideal problem statement for agentic AI technology. Agentifying workflows such as request-for-quote (RFQ) responses, order entry, or rate table updates can improve accuracy and speed. In one instance, we saw a company shorten its response time for nonstandard RFQs from four hours to two minutes using an agentic solution that interacts directly with the customer through email. AI technology is also adept at validation tasks that span across systems and data formats, comparing rate tables to quotes to invoices to payments, all in search of leakage.
Logistics companies searching for a lighthouse AI use case might find it in price execution. Price execution is the rare automation project that can delight customers, recover margin leakage, and reduce administrative cost.
This article lays out four ways that logistics companies can reimagine how they are paid for the valuable work they do. While these strategies are bold, they are possible. Willingness-to-pay analytics, digitized deal reviews, network pricing, and agentic price execution are already creating results in the industry.
For logistics companies, cost discipline and sales growth alone are not enough. Companies that can move quickly to reimagine their pricing are likely to find advantages in the AI era.
