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An Optimal Customized Pricing Strategy for Truckload Transportation

An Optimal Customized Pricing Strategy for Truckload Transportation

Ayşenur Budak, Alp Ustundag, Bülent Güloğlu
Copyright: © 2021 |Pages: 23
ISBN13: 9781799880400|ISBN10: 1799880400|ISBN13 Softcover: 9781799880417|EISBN13: 9781799880424
DOI: 10.4018/978-1-7998-8040-0.ch012
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MLA

Budak, Ayşenur, et al. "An Optimal Customized Pricing Strategy for Truckload Transportation." Handbook of Research on Decision Sciences and Applications in the Transportation Sector, edited by Said Ali Hassan and Ali Wagdy Mohamed, IGI Global, 2021, pp. 257-279. https://doi.org/10.4018/978-1-7998-8040-0.ch012

APA

Budak, A., Ustundag, A., & Güloğlu, B. (2021). An Optimal Customized Pricing Strategy for Truckload Transportation. In S. Hassan & A. Mohamed (Eds.), Handbook of Research on Decision Sciences and Applications in the Transportation Sector (pp. 257-279). IGI Global. https://doi.org/10.4018/978-1-7998-8040-0.ch012

Chicago

Budak, Ayşenur, Alp Ustundag, and Bülent Güloğlu. "An Optimal Customized Pricing Strategy for Truckload Transportation." In Handbook of Research on Decision Sciences and Applications in the Transportation Sector, edited by Said Ali Hassan and Ali Wagdy Mohamed, 257-279. Hershey, PA: IGI Global, 2021. https://doi.org/10.4018/978-1-7998-8040-0.ch012

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Abstract

The impacts of optimal pricing are rarely explored when it comes to truckload transportation. In this study, the question of what price should be given to which customer and for what service characteristics are investigated for truckload transportation. Accordingly, customers' attitudes and responses to the bid price must be modeled, and their flexibility in regards to the price must be analyzed. Bid response function is developed, and logit model is considered. The bid response function is examined from two different perspectives: the first one is a general model by which all data is used, and the second one is the logit model by using partitioned data obtained by clustering customers. Logit model sensitivity analysis is applied. After developing bid response functions, non-linear optimization model is developed to determine the bid price. The developed model will contribute to the logistics companies' profit margins in the long term.

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