- Machine Learning
The freight brokerage industry is at a pivotal juncture, with digital platforms reshaping market dynamics and carrier preferences. This capstone project, undertaken in partnership with Nolan Transportation Group (NTG), employs a predictive machine learning model to decode and understand these evolving preferences. The study leverages a dataset comprising nearly 2 million brokerage transactions, enriched with comprehensive feature engineering, to model the likelihood of digital vs. traditional booking methods. The research uses advanced machine learning algorithms, especially Gradient Boosting with XGBoost, to identify key shipment characteristics that influence carriers' digital booking decisions; piercing through the complex interplay of shipment characteristics that decisively influence digital booking decisions. Central to these findings is the pivotal role of the time a load remains available on digital platforms in determining its likelihood of being digitally booked. The analysis underscores a critical insight: the probability of a load being booked digitally diminishes significantly with time, highlighting a narrow window for digital engagement. This discovery has valuable operational implications, suggesting a strategic shift towards minimizing internal competition for loads in the period of initial listing, thereby enhancing the effectiveness of digital channels. By offering a nuanced understanding of the temporal dynamics at play in digital freight booking, this research provides actionable strategies for fostering digital adoption and optimizing brokerage operations in the digital age. Through this lens, the study not only contributes to academic discourse but also equips industry practitioners with the insights needed to navigate the evolving landscape of freight brokerage.