How to Harness XGBoost Algorithm to Optimise Spot Pricing in Road Freight Transport: Key Capabilities

Navigating the complexities of the logistics industry and road freight transport requires a robust pricing strategy that can adapt to fluctuating market conditions, customer demand and operational costs. Though traditional pricing models like the cost-plus approach can be reliable, they often fall short when it comes to capturing the nuanced interplay of all the factors involved. Enter machine learning and the XGBoost algorithm: a powerful tool that brings precision and adaptability to pricing.

An accurate pricing engine boosts the company's profitability, competitive edge and customer satisfaction. In an industry like road freight transport, characterised by tight margins and intense competition, even slight pricing inaccuracies and any resulting missed opportunities can have significant repercussions on a company’s bottom line.

What’s more, margins in road freight transport have been decreasing in recent years. In fact, according to the consultancy firm CSI Market, on a trailing twelve months basis, net margin in Q4 2023 fell to 5.82% (back in 2021, it was nearly 9%).

Machine learning techniques can offer a solution to these challenges. By analysing vast datasets and learning from historical pricing scenarios, machine learning models can uncover hidden patterns and insights that are not immediately apparent. The XGBoost algorithm, in particular, excels in this regard. This blog post delves into its mechanics and how it can be applied to pricing in the road freight transport industry.

What Is the XGBoost algorithm in simple terms?

The XGBoost algorithm, short for eXtreme Gradient Boosting, is a powerful machine learning technique used for supervised learning problems, where training data with multiple features are used to predict a target variable. At its core, XGBoost is a sophisticated program that predicts outcomes, like the price of a service, based on the data it was trained on. In the context of a pricing engine, it learns from historical prices to make educated guesses about new ones.

Despite its sophistication and complexity, the essence of how XGBoost works can be understood without a deep dive into machine learning concepts. The XGBoost algorithm builds multiple simple models, one after the other, and each model tries to correct the mistakes made by the previous one.

The models XGBoost builds are called decision trees, which can be pictured as flowcharts that make decisions based on the data’s features. Each decision tree in the sequence tries to correct the previous tree’s errors, with the goal of making better predictions with each iteration.

How does an XGBoost model work?

Here’s a breakdown of how this process works:

1. Building decision trees: Each decision tree can be viewed as a series of questions about the data that lead to a prediction. For example, in the context of predicting the price of a logistics service, a question might be ‘is the shipment weight more than 1000 kg?’ Based on the answer, the tree branches out until it reaches a prediction.

2. Learning from errors: After building a decision tree, XGBoost evaluates how well it predicted the prices. It then identifies where it made mistakes and measures the severity of these errors.

3. Boosting: The next tree is built with a focus on the hardest predictions (that is, those where the previous tree was most inaccurate). This is the ‘boosting’ part: it boosts the algorithm’s ability to handle difficult predictions by concentrating on correcting past mistakes.

4. Combining trees: Rather than starting from scratch, each new tree learns from the cumulative knowledge of all the trees built before it. XGBoost combines all these decision trees in ensembles, weighing them based on their accuracy to form a single, more accurate prediction model.

The XGBoost algorithm works through an iterative enhancement process – a cycle of building a decision tree ensemble, evaluating its performance, then building another tree ensemble to improve upon those errors – that continues for a specified number of iterations or until the improvements become negligible. This is what allows XGBoost to refine its predictions to a high degree of accuracy.

The implementation of the XGBoost algorithm in machine learning also involves a concept known as regularisation, which penalises overly complex models. This discourages the model from becoming too specific to the training data (a problem known as overfitting), thus ensuring that the predictions remain robust and are generalisable to new, unseen data.

Applying the XGBoost algorithm to road freight transport pricing

Applying the XGBoost algorithm to optimise pricing in road freight transport calls for a structured, methodical approach, by which complex data are transformed into actionable pricing strategies. This is a breakdown of the main steps required:

1. Selecting the data

Compiling historical data that include various factors that influence pricing, such as shipment details, distances or delivery parameters like service type or urgency. The data must be clean, comprehensive and relevant to the pricing strategies.

2. Selecting the target variable

Defining what the model should predict. For pricing models, this is typically the price itself, but that could be a base price, full price or a similar metric indicating the cost of the service.

3. Selecting the features

Identifying and choosing input variables that affect pricing. These could range from quantitative data, like shipment weight and distance, to categorical data, like service types and delivery routes.

4. Preprocessing data and feature engineering

Utilising XGBoost library capabilities for data cleaning and transformation. This includes handling missing values and outliers, encoding categorical variables into a machine-readable format and normalising numerical variables to enhance model performance. Also, its functionalities can be leveraged for feature engineering purposes, which may involve creating new variables from existing ones to better capture the complexities of pricing.

5. Training the model and tuning hyperparameters

Feeding the preprocessed data into the XGBoost model, allowing it to learn the relationship between features and the target variable from the data. The hyperparameters (configuration settings used to control the model’s learning process) can then be adjusted to optimise the model’s accuracy and performance. These include the learning rate, max depth of trees and number of estimators.

6. Measuring error metrics and tracking experiments

Evaluating the model’s performance post-training and the forecast accuracy. This involves analysing error metrics like Mean Absolute Error (MAE), Mean Absolute Percentage Error (MAPE) and Mean Squared Error (MSE). They can be tracked systematically for each experiment and model iteration in order to document changes in the metrics, thus facilitating model selection, ongoing refinement and optimisation of the final pricing model.

How XGBoost learns from price scenarios to optimise transport margins

One of the goals of applying machine learning and the XGBoost algorithm to pricing is to optimise profit margins without compromising competitiveness. XGBoost contributes to this objective in several ways:

• Pattern recognition: XGBoost excels at identifying complex patterns within the data. It distinguishes which factors most significantly impact pricing, learning how these variables interact to influence the final price. This facilitates the development of pricing strategies that can adjust to market conditions and customer demand dynamically, which directly contribute to enhanced profit margins by ensuring prices are optimised for both competitiveness and profitability.
• Dynamic pricing: By leveraging XGBoost's precise predictions, businesses can implement dynamic pricing models that are finely tuned to fluctuating market dynamics, varying demand and evolving cost structures. This adaptability ensures that prices are always set to maximise margins while remaining attractive to customers.
• Margin enhancement: Thanks to its ability to forecast prices with high precision, XGBoost helps to identify pricing opportunities that might have been overlooked. By adjusting prices based on predicted outcomes, businesses can enhance their margin through strategic pricing decisions.
• Cost reduction: Beyond direct pricing, XGBoost also aids in identifying inefficiencies and cost-saving opportunities within the logistics chain. By correlating costs with pricing data, it can highlight areas where operational adjustments could lead to reduced expenses and improved margins.
• Market responsiveness: The agility of XGBoost-driven models means that pricing strategies can quickly adapt to changes in the market, whether due to economic shifts, competitive actions or changes in customer demand. This responsiveness is crucial for maintaining a competitive edge in the logistics industry, and it helps businesses to manage the volatile dynamics of spot freight transport efficiently.

XGBoost Machine Learning algorithm: an invaluable tool for implementing sophisticated pricing strategies

To sum up, the XGBoost machine learning algorithm can play a significant role in how pricing strategies are formulated and implemented in the logistics and road freight transport sector. Its ability to analyse complex datasets and learn from historical pricing scenarios enables businesses to develop highly nuanced and adaptive pricing models. As well as being reactive to current market conditions, these models are also predictive, offering the foresight needed to stay one step ahead in a competitive landscape.

Moreover, the integration of XGBoost into pricing strategies underscores the broader digital transformation trend within the industry. Logistics service providers that leverage such advanced analytics tools are positioning themselves as leaders, ready to capitalise on data-driven insights for strategic advancement. By optimising pricing, these companies not only boost their profitability, but also enhance customer satisfaction by offering fair and dynamic pricing. 

If you want to learn more about how the latest forecast technology based on the XGBoost algorithm can improve operational profitability, request a demo today and our specialists will be happy to explain the ins and outs of our Ontruck AI Tech solutions in this regard.

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References:

• XGBoost. (n.d.) Introduction to Boosted Trees. https://xgboost.readthedocs.io/en/stable/tutorials/model.html

• CSIMarket. (2024).Transport & Logistics Industry Profitability. https://csimarket.com/Industry/industry_Profitability_Ratios.php?ind=1101#google_vignette