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Improving the profit by using a mixed after-sales service as a market segmentation

Authors :
Yi Liao
Ye Tian
Yufei Yan
Miao Sun
Source :
Nankai Business Review International. 10:233-258
Publication Year :
2019
Publisher :
Emerald, 2019.

Abstract

PurposeThis paper aims to study the mixed after-sales service which simultaneously offers return and replacement services. The authors develop a model to propose what kind of after-sales service the firm should choose and how to make the after-sales service policy to improve the profit. The study aims to extend the literature on the mixed after-sales service and give some support to the managers to make decisions.Design/methodology/approachIn this paper, the authors use the optimization modeling method to describe the situations of a firm offering two exclusive after-sales service policies and a mixed after-sales service policy, respectively. They compare the results in different cases and analyze the impact of different parameters on the boundary values and other results. Finally, the authors include three numerical examples to illustrate the major results.FindingsThe authors find that the mixed after-sales service can successfully segment the market, meet various customers’ distinct needs and differentiate the service prices to improve the total profit. Moreover, the authors find the boundary values which indicate the optimal interval for each service. Then, for a certain situation, they can clearly tell which after-sales service dominates and provides the optimal selling price, order quantity and total profit. Besides, the authors show the impact of different parameters on the boundary values and other results.Originality/valueThis paper combines after-sales service into traditional models and provides a new mixed service to segment the market and improve total revenue. It provides some managerial implications for the decision-makers.

Details

ISSN :
20408749
Volume :
10
Database :
OpenAIRE
Journal :
Nankai Business Review International
Accession number :
edsair.doi...........82e26fd8126c8cc94688baf44efe730b
Full Text :
https://doi.org/10.1108/nbri-10-2017-0057