Improving freight quoting through business analytics: a case study of a logistics service provider

In the transportation sector, the process of estimating the profit margins to be applied to customer freight quotation requests is a problem of particular interest, which impacts strongly on customer relationship management. In practice, this process is typically conducted based on the subjective business experience of transport managers, posing challenges and delays in decision-making that can damage the customer relationship. This article explores a multivariate predictive analytics approach to support the process of estimating profit margins applied to customers for road freight transportation requests. Our approach consists of developing statistical learning models that make it possible to generalize historical relationships between a set of independent variables related to quotation requests and the respective profit margin applied and accepted by customers. The proposed approach is tested on empirical data from a portuguese logistics service provider. The results show that the proposed models have a good generalization capacity when tested on independent data under a rolling window evaluation mechanism. We discuss the managerial implications of the proposed approach and how it can serve as a decision support tool for applying profit margins to future requests for road freight transport quotation.