Personalized Trucking Orders and Pricing with LLMs and Analytics

In 2024, FedEx Ground Segment delivered an average of 9.23 million packages per day—that’s a serious number of orders for back-office teams to manage. Customer service representatives must compare multiple data sources, cross-reference rates, and adjust for fluctuating variables. These repetitive tasks eat up their time and are error-prone. Large language models (LLMs) and AI-powered analytics enter the scene to offer some relief.

Let’s say your primary client, a pharmaceutical manufacturer, needs a truck from Lyon to Sofia, and they need it now. AI looks at previous trucking orders, prices for this origin, current spot rates, and driver salaries. The decision is based on factors like distance, weight, truck volume, and market conditions. With this information, the advanced algorithms can calculate competitive prices and share them with LLMs to set up an offer with an optimal and profitable price point and suggest an email response that can be confirmed or modified by a sales agent. 

E-commerce popularity and major players committing to deliveries within the hour have driven up trucking expenses. Global logistics costs are forecasted to increase by 12% and hit US$14.4 trillion by 2029. Geopolitical conflicts, varying toll agreements, and market volatility create an unfair playing field in which trucking companies’ processes must be slick and streamlined to come out on top.

Besides navigating constant change, trucking companies must constantly find better ways to serve customers to retain business and gain new clients. Could LLMs and AI analytics be the perfect solution?

Leveraging Analytics for Smart Pricing

AI is objective, can analyze vast amounts of data simultaneously, and continuously monitors data around the clock, allowing for minute-by-minute price adjustments based on the set parameters. 

For instance, the players in the retail sector have increased gross profit by 5% to 10% with dynamic pricing. They use historical and real-time data analysis of forecasted customer needs, competitors’ pricing, and rounding rules to improve customer perception. Other factors influencing trucking companies’ price optimization include fuel costs, weather, demand fluctuations, and truck availability.

Trucking companies can use AI-powered analytics in pricing to help charge more when demand is high (like during a busy season or for a popular route). Or, when demand is low, they can lower prices to attract customers and avoid inactive fleets. In cases where trucks are driving to the next pickup location empty for a significant amount of time, AI can proactively suggest load-to-capacity matching to find nearby loads on the way and offer an extremely competitive price. This creates a win-win situation for both the client, who gains a bargain, and the trucking company, which increases utilization.

By incorporating market spot rates for relevant origin-destination pairs into the algorithm, trucking companies can react to price changes from other truckers, ensuring they don’t lose out on business. The same goes for fluctuating fuel prices, tolls, and spot rates. Over time, understanding demand and price fluctuations will help truckers better plan their routes and schedules.

Shifting away from rigid annual contracts to a more dynamic, real-time model aligns carrier costs with shipper pricing expectations. Rates fluctuate throughout the year based on shipment details such as size and weight, carrier capacity constraints, lane imbalances, embargoes, and broader operating costs. Dynamic pricing creates opportunities to capitalize on real-time cost savings, especially when surplus capacity becomes available on specific routes.

LLMs and Personalized Trucking Orders

Speed and accuracy in processing trucking orders can make or break profitability. LLMs can analyze vast amounts of unstructured data, understand natural language, and generate written email responses almost instantly. 

Let’s say a trucking company gets 100-200 requests for quotes (RFQs) per day. Keep in mind that clients usually send these requests to competitors, too, to compare and decide on the most profitable offer. That creates two main challenges for the trucking companies:

1. The speed and efficiency of responses: A single or even small team of sales agents would not be able to properly analyze all the requests. So, the responses might be delayed, and the window of opportunity to get the client can already be closed. 
2. How to present the right price: Sometimes customers even share their price expectations in the original email. LLMs can quickly extract key information from order documents (like expectations, addresses, weights, and delivery instructions), reducing manual data entry and speeding up the order process. AI analytics can process this information to ensure the right price point and LLMs can set up an offer with a suggested email response that can be confirmed or modified by a sales agent. 

When these two challenges are resolved with AI, a sales agent’s job switches focus to seeing the most profitable trucking orders based on what AI suggests. Then they use this information to make decisions and send emails with quotes/order confirmations back to the client—in under an hour from receiving the order with a competitive price—this is the ideal case scenario that such a tool can provide.

Reducing manual data entry in order processing can reduce errors, such as incorrect addresses or mismatched information, leading to fewer delays and happier customers. LLMs can also personalize communications, making interactions feel more human.

The increasing pressures of e-commerce, escalating logistics costs, and geopolitical tension demand innovative solutions. Dynamic pricing and automated personalized quotes offer valuable mechanisms for optimizing revenue, enhancing operational efficiency, and navigating the inherent uncertainties of the current market. By using LLMs and AI analytics to power dynamic pricing and real-time order processing, trucking companies can eliminate inefficiencies, improve load utilization, and create a more resilient operation that adapts to shifting market demands.