With roughly six million trucks on European roads, in an industry that employs ten million people, road freight plays a vital role in Europe’s economies.1 Despite various European countries’ decarbonization initiatives to shift freight volume from road to rail, trucking still represents around 75 percent of modal share.2
Still, trucking remains a fragmented and analog industry. On average, there are only five employees in each trucking company, and 90 percent of trucking companies have less than ten trucks. As smaller companies tend to lag behind in digitization, procurement still occurs via manual exchange of emails or phone calls in many cases.
Digitization and systematic data collection could offer trucking companies of all sizes a range of new opportunities. For instance, decisions around contract versus spot pricing are often based on intuition and prior experience. In contrast, data-driven decisions can help to make the mix more efficient and increase revenue. Greater reliance on data can also help companies to balance long-term utilization with attractive short-term freight rates. In an industry with small margins (on average between 2 and 4 percent EBIT), getting these decisions wrong can be costly. Conversely, even a small improvement in efficiency might generate significant value.
Greater efficiency is good for customers, too. Data-based insight into capacity could help companies decide where to deploy their trucks, and how they could accommodate customers’ needs—giving them better service, even on the busiest lanes.
This article examines the carrier’s decision between allocating capacity to long-term contracts or to the spot market to illustrate the impact that data-driven decisions could have for trucking companies. The analyses draw on European road freight data, across trade lanes, to highlight key trends and show how trucking companies could generate between 15 and 30 percent improvement in EBIT by leveraging data for decision making—equivalent to between €5 million and €10 million for a mid-sized carrier. In particular, companies could use data to decide on their contract share by lane, to update the mix frequently, and to predict changes in rate difference between spot and contract per lane (see sidebar, “About the data”).
Recent fundamental shifts in the road freight market highlight the importance of optimizing this decision. An analysis of data gathered between 2019 and 2022 illustrates how spot and contract rates have changed across multiple European trade lanes in reaction to the pandemic, and the implications for pricing.
Fundamental shifts from a buyer’s to a seller’s market
The trucking industry has seen some fundamental shifts since the outbreak of the COVID-19 pandemic which have affected contracting and pricing. For example, the lane from Germany to France shows three distinct phases, each of which required adjustments to the contracting mix (Exhibit 1).
The first phase, the pre-COVID-19 market, illustrates the common principle that spot market exposure leads to slightly higher returns—and higher volatility. In turn, volatility leads to operational challenges, such as varying utilization, and carriers may need to operate trucks below cost or stop offering if the spot rate temporarily drops too low. In these market conditions, a base share of revenue secured by contracts is required to stabilize the business.
Second, during the initial wave of the COVID-19 outbreak, closely followed by border closures and the shutdown of economic activity, the spot market collapsed and rates dropped substantially below contracted rates. A buyer’s market emerged, and contracts became crucial for carriers from March to July 2020. In these conditions, support from long-term customers through contract rates is required to stabilize the value chain.
Third, after the initial shock of the pandemic subsided, demand recovered quickly and led to a seller’s market. The expansion of e-commerce, and a spike in demand for various daily-use consumer items, led to a recovery in spot rates in the summer of 2020. Since mid-2020, border closures, driving school closures, and a lack of drivers created a trucking supply shortage. In 2021 there was a shortage of 400,000 drivers in Europe.3 In the United Kingdom, this situation was exacerbated by Brexit, which hindered drivers’ free movement between the European Union and the United Kingdom. Furthermore, carriers’ costs continued to increase, particularly for labor. For example, it is estimated that UK truck drivers’ salaries increased by more than 10 percent in 2021.4
In the current landscape, spot rates remain significantly above contract rates, making the spot market even more attractive for carriers. In these conditions a careful increase of spot share can improve margins while carriers could still preserve a stable revenue base by maintaining contract exposure.
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Contract mix optimization—and how to capture its potential
The industry has seen massive change over the past three years, but the good news is that as more and more data becomes available, all stakeholders can use it for better-informed decisions to help them deal with uncertainty and risk. Shippers and carriers typically balance risk and flexibility by combining contract and spot volumes across lanes and countries. Often, an organization’s business model determines its risk exposure. For instance, brokers with a more flexible cost base could bear higher exposure to spot rates compared to asset-based carriers that have their own personnel.
A data-driven approach can help carriers identify the most profitable mix of contract and spot pricing and decide where to deploy trucks. In normal market circumstances, spot market exposure—in principle—leads to higher returns and higher volatility. But increasing spot share may not be efficient, as it increases risk, especially in volatile markets.
The following three-step approach could help companies diagnose the optimal spot-contract mix, and deal with volatile prices:
- Create a historical portfolio. Trucking companies could define a portfolio with a combination of capacity deployed on different trade lanes and how it is sold (for example, exposure to spot or contract). Then, they calculate the excess returns (freight rates above market average) that these portfolios yielded as well as the volatility of the freight rates as a measure of risk.
- Simulate many historical portfolios with corresponding returns and volatilities. This data-driven simulation shows potential combinations of risk and returns.
- Plot an efficient frontier. Using the simulation results, companies could plot an efficient frontier—the area where returns are maximized, at an acceptable risk tolerance. The difference between the historical decisions taken and the efficient frontier highlights the potential for optimization.
Examples from other industries show that this contract-mix diagnosis can be highly effective. For instance, a petrochemical company was looking to improve margins by finding the optimal spot-contract mix. Its existing processes for determining pricing and contracts were relationship based, and reliant on individual sales personnel’s skills. The company realized between $8 million and $12 million in value by using this data-driven model to find the optimal mix.
Exhibit 2 illustrates how this could be applied in the trucking industry. In this example, 5,000 contracting profiles are simulated, where each profile defines the spot-contract share across seven European trade lanes. In step four, the y-axis shows the excess returns of the chosen contracting profile over six months compared to 100 percent exposure to contracts. The x-axis indicates the volatility of these returns, or the risk. The higher the volatility, the more likely the actual return will have a substantial deviation from the mean.
The simulation provides an estimate of the potential value that a carrier could realize through this approach. When looking at a fixed point in time, the outcome of contracting strategies varies significantly, even at the same risk level. Switching from a suboptimal to an efficient contracting portfolio could yield up to €8/100km in additional revenue—at the same level of volatility. For a large carrier with around 1,000 trucks—that each conduct 250 trips between 250 and 500km—this adds up to a potential value of between €5 million and €10 million. Translated to EBIT, this could yield an increase of 15 to 30 percent.
When realizing this value by moving toward the optimum choice of contract and spot shares, carriers could bear the following three considerations in mind:
- Understand lane differences and make decisions lane by lane, rather than adopting a “one size fits all” approach. When taking a three-year view, average spot prices are above contract prices on most lanes. But there are various structural differences in freight rates that cannot be normalized by only looking at the difference between contracted rates and spot rates (Exhibit 3). Some trade lanes yield consistently higher rates than others and exhibit higher volatility. For example, UK-related lanes show higher returns but also have a higher volatility profile due to Brexit. International lanes, on average, are more volatile than domestic ones—with higher pricing variance. This may be driven partially by border closures and higher complexity on international routes, such as challenges with language and filling up backhauls, as most trucking companies have more national customers or use load boards or brokers that focus on a specific market.
Also, a lane analysis could help companies to deploy trucks more efficiently, make the most of available capacity, and ultimately offer customers better service by accommodating their requests.
- Make the decision on contract-spot mix a dynamic one, and update it frequently. Our analysis of market shifts over recent years shows significant changes in the relative price levels of spot and contract markets. Companies could make frequent adjustments to lane-specific contract vs. spot decisions to unlock further value.
- Leverage demand forecasting to improve decisions and predict changes in rate differences between spot and contract per lane. Freight rate forecasting, nowcasting, and trend analytics have recently received substantial attention. Several intelligence providers have developed solutions to help companies with short-term forecasts in specific markets. And many leading logistics companies have developed tailored forecasting tools by creating a 360-degree data view consisting of internal data, industry data, as well as non-traditional data sets such as events, internet search trends, or weather data. Once such data is collected and combined, companies can build a forecasting engine, essentially a machine-learning model to predict future outcomes based on historical information, trends and seasonal patterns. These outcomes are then used for support in contracting and pricing decisions.
Getting the price right in logistics
For instance, historical seasonality in spot rates could be a key input for forecasting demand (Exhibit 4). The typical pattern depicts a price drop early in the new year, driven by low economic activity. There is usually an acceleration in Spring (though there was a drop in Spring 2020 at the start of the COVID-19 pandemic). After that, rates generally drop again in August and recover in September, with a final peak just before Christmas.
Consider the bigger picture
The benefits of a data-driven approach to optimizing contracting and pricing are clear. Companies could also consider the relationships behind the contracts to capture the full value of this approach. This is particularly important as trucking companies rely on customers’ contract adherence. Trucking companies could work closely with their customers, and jointly monitor contracts to ensure that contracts are followed and the agreement benefits both sides. There is always the potential to renegotiate, as maintaining and strengthening client relationships is of the utmost importance.
Taking a step back, this approach also applies to other pricing-related decisions. Overall, data-driven decisions could be embedded in a holistic pricing approach where various elements work hand in hand. Specifically, companies could take a holistic approach across their existing tools, systems, and infrastructure; organizational processes; and the mindset, behavior, and capabilities of the workforce when embarking on a journey to optimize pricing (Exhibit 5).
The trucking industry has seen significant change in recent years and is currently characterized by high and volatile spot prices. But simply responding to these market changes by increasing spot share can increase risk and may not bring returns. Carriers can realize significant value by approaching the contract versus spot decision lane-by-lane, updating it frequently, and leveraging demand forecasting. With the significantly increased access to data, data-driven decision making could become a reality for all stakeholders in the freight industry.
