Truckload spot rate pricing

Understanding AI, machine learning and the role of science 

06/01/2023 | 6 min

With the state of the truckload spot rates market as chaotic and volatile as it is today, it’s getting harder for shippers to not only procure spot capacity but also to negotiate effectively and reduce tender rejections. As noted by Coyote Curve, a proprietary spot rate freight index, “On the (incredibly) bright side, the economy is opening back up as vaccinations increase globally and COVID-19 restrictions start to lift in the U.S. But this bullwhip effect is causing a lot of volatility and difficulty for supply chains everywhere.” 

What we’re encountering is a market context where a manual approach to spot freight procurement is failing — where shippers and brokers alike are unable to create an edge using the same tactics as everybody else. Here’s where the promise of AI comes in. 

In this article, we’ll establish some basics related to artificial intelligence, behavioural science, and machine learning and explore why embedding them in a science-based approach is the future of spot procurement. 

AI, machine learning and the sciences defined 

Though often used interchangeably with each other, artificial intelligence (AI) and machine learning (ML) are distinct technologies. AI is all about creating computer systems that can demonstrate human-like intelligence, but at faster speeds and with more processing power, according to CSU Global. Machine learning is best thought of as a subfield of AI; the goal of ML typically is to help any intelligent system make faster, more accurate decisions. Science augments this mix of AI and machine learning in one of two forms: data science and behavioural science, or a combination of both.

In a discussion on the future of freight pricing, the interplay between data science and behavioural science is exactly where we'll focus. We'll start by defining both to explain why the combined impact is so powerful.

Data science: numbers guide the course of action 

Data science is the leveraging of data in some sort of automated fashion to improve specific business decisions. Think of it as being on a scale of 0 to 10; at zero there is no data in the decision. That means it is strictly driven by human interpretation, intuition and quite likely a splash of emotions. On the opposite end of the scale, at 10 the software alone is making the decision. Humans can interject themselves into the process, or the software could provide a definitive list of options from which to choose.   

Behavioural science: interjecting human influence 

Behavioural science acknowledges the human presence in decision-making, which is biased, noisy and alterable. The decision-making process is subject to the conditions and context surrounding the decision. For example, Facebook uses behavioural science to keep users scrolling through their newsfeed, which influences their decision-making until they reach the intended behaviour Facebook is looking for more time spent on Facebook to serve up more ads and get more revenue. In essence, behavioural science gently nudges users toward a particular decision or desired outcome. 

Their role in truckload spot pricing and negotiations 

Shippers engaging in the spot freight market are just starting to incorporate AI, machine learning and the sciences today, but few appreciate its true potential. 

This opportunity exists due to the fragmented nature of the spot rate market. Shippers who are looking to secure spot capacity could be considering 50 carriers. Many of those carriers might only have one truck but are receiving six other offers from different brokers or shippers. It’s a super-active, highly variable process with many outcomes at the ultimate price. Given the high variability, the volume of relevant data points, and the psychology that is inherent in human decision-making, the spot freight market is the ideal playing field for AI-based strategies. 

The impacts of AI, machine learning and science 

The goal of any shipper entering the market for a truckload spot rate is to secure the best rate possible in a short amount of time. Today, the market standard is to broadcast the available freight out to a group of carriers and wait for rates to come in. It’s highly reactive and leaves the shipper in the position of being a price taker. 

But by using AI, the shipper is able to perform pricing discovery that would be too time-consuming to run manually. Data science provides the guide rails, and behavioural science the mechanisms to influence the right user towards a certain price target. 

In practice, the first step is to predict an end price or goal. From there, the next step is to play off of it — the key is to get authentic reactions to different prices for that shipment at that moment in time, and ultimately understand the price willingness of individual carriers. Continuous market testing ensures the shipper is securing capacity for the best possible rate. The software does most of the work, leaving the shipper in a supervisory role. 

By using AI, machine learning and data/behavioural science to drive a procurement and pricing strategy, shippers can gain: 

  • Greater efficiency of operations 

  • A sustainable price advantage 

  • Actionable insights into carrier and market dynamics   

Those benefits are extremely important for any logistics director at a company who is managing millions in spot freight spend. After all, they are the ones who will pay the price if the transportation budget is exceeded or they fail to ship critical goods on time. 

Wait, don’t digital freight brokers and marketplaces do this?  

Digital freight brokers flaunt a ‘new way’ of transacting freight, but as it relates to the sciences they are still at an early stage. The truth about digital brokers is this: 

  1. They aren’t changing the pricing/negotiating process. These technologies are employing the same approach as everyone else, albeit faster.  

  2. They are not trying to make the market more rational and structured. Digital freight brokers feed off that noise and volatility, which helps keep their margins high.    

  3. Even if they could rationalise pricing for some parts of the market, they are not big enough to cover the rest. Consider that the biggest digital freight brokers—UberFreight, Convoy, Coyote and Arrive—still only make up approximately 1% of the entire full truckload sector. Collectively there’s not enough influence to create a tipping point with regard to price; therefore, they cannot drive pricing. 

To reap the benefits of AI, shippers must align themselves with partners that are more advanced in their application and better incentivised to deliver advantages to their shipper customers. 

Don’t just manage truckload spot rates—drive them 

Transporeon studies how carriers make their pricing decisions, using a robust combination of data science, behavioural science and automation to nudge them toward lower truckload spot rates. 

By partnering with Transporeon, you will offer spot prices rather than request bids. This streamlines the back-and-forth negotiations and improves the shipper-carrier relationship while the use of algorithms and machine learning helps ensure correct spot pricing. To learn more, schedule a demo with Transporeon today. 


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