Volume 56, Number 2, March-April 2022
|Page(s)||1031 - 1049|
|Published online||14 April 2022|
Optimal add-on items recommendation service strength strategy for e-commerce platform with full-reduction-promotion
Business and Tourism School, Sichuan Agricultural University, Chengdu 611830, Sichuan, P.R. China
Accepted: 2 March 2022
This purpose of the paper is to make an in-depth study on the selection of the optimal shopping add-on items recommendation service strength strategy of the e-commerce platform with full-reduction promotion based on consumers’ heterogeneity preferences for discount amount and add-on items recommendation. With respect to the optimal decision problem consisting of an e-commerce platform who maximizes the profits and consumers who make purchase decision based on their utility, we construct a Stackelberg game model that reflects the interaction between platform’s recommendation service strength and consumers’ purchase willingness. Furthermore, through the derivative function analysis method, we examine the effect of reservation price, recommended commodity price and discount amount on the platform’s optimal recommendation service strength strategy. The results show that the discount amount, reservation price and consumer preference have different effects on the optimal add-on items recommendation service strength and the profit of the platform. Additionally, appropriate recommendation services strength is beneficial to enhance consumers’ willingness-to-pay and then increase the profits of the platform. Therefore, it is an effective way to improve the performance of the platform to reasonably formulate the basic discount amount, full-reduction promotion threshold and add-on items recommendation service strength.
Mathematics Subject Classification: 90B06
Key words: Full-reduction promotion / add-on items recommendation service / consumer preference / Stackelberg game
© The authors. Published by EDP Sciences, ROADEF, SMAI 2022
This is an Open Access article distributed under the terms of the Creative Commons Attribution License (https://creativecommons.org/licenses/by/4.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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