Assessing the Success of Automotive Sales Transactions Using Selected Machine Learning Algorithms
Year: 2025
Type: article
Abstract: Distributed operational data rarely translates directly into business decisions. Meanwhile, in almost all industries, including the automotive industry, especially in the premium segment, it is crucial to identify the factors conducive to closing the transaction at an early stage. The aim of this study is to develop classification models that make it possible to predict the probability of success of a particular Mercedes-Benz offer with regard to vehicle configuration. Such a tool enables optimal allocation of resources (salespeople’s time, media budgets, production capacity), which is confirmed by the literature on customer relationship management. This study evaluates the usefulness of four machine learning algorithms—Random Forest (RF), Gradient Boosting Machine (GBM), eXtreme Gradient Boosting (XGBoost), and Support Vector Machine with an RBF kernel (SVM-RBF)—in forecasting sales, which was encoded as the binary variable Success. Among the tested models, Random Forest achieved the best results with an accuracy of 84.3%, F1-score of 0.73, and AUC of 0.90, indicating a very good ability to distinguish between successful and unsuccessful transactions. The results can be used for lead prioritization, dynamic discounting, optimization of marketing campaigns, and distribution/production planning.
Source: Applied Sciences
DOI: 10.3390/app152111562
Authors: Mateusz Mazur, Ondrej Stopka, Mária Stopková, Jiří Hanzl, Anna Borucka, Robert Czerniak
Language: English
Subfield: Marketing
Domain: Social Sciences
Open Access status: gold
Estimated APC paid: $2,896
OpenAlex ID: W4415672880