Teklif tabanli işletmeler arasi fiyatlandirma optimizasyonu

This paper focuses on quotation based pricing for a Business to Business (B2B) firm, using a real world case of a distributor engaged in aftermarket sales. The study assigns quotation groups into clusters, each associated with distinct logistic regression curves, to enhance overall performance and define the elasticities of various quotation groups. The objective of this study is to determine the optimal assignment of groups to clusters while simultaneously identifying the corresponding cluster wise regression coefficients. Coupled with the combinatorial nature of the task, finding an optimal solution is particularly challenging and computationally intensive. Additionally, we introduce a complex teacher model designed to improve the performance while using logistic regression model to enhance interpretability. To generate clusters, apart from using solvers, we propose heuristic algorithms such as the Recursive Partitioning Algorithm and the Iterative Refit Classifier Algorithm to address the problem. The performance of these approaches is evaluated using both real world and simulated datasets. Furthermore, we demonstrate the impacts of implementing quotation based pricing strategies using the real dataset.