Joint promotional effort and assortment optimization under the multinomial logit model

IF 1.9 4区 管理学 Q3 OPERATIONS RESEARCH & MANAGEMENT SCIENCE Naval Research Logistics Pub Date : 2024-04-09 DOI:10.1002/nav.22187
Hua Xiao, Min Gong, Zhaotong Lian, Kameng Nip
{"title":"Joint promotional effort and assortment optimization under the multinomial logit model","authors":"Hua Xiao, Min Gong, Zhaotong Lian, Kameng Nip","doi":"10.1002/nav.22187","DOIUrl":null,"url":null,"abstract":"Promotional effort is a common strategy to induce sales and broaden the market scope by enhancing the products' utility to customers. In this article, we incorporate promotional effort into the customer choice model and study the joint promotional effort and assortment optimization problems, where the customer's choice behavior follows the widely used multinomial‐logit (MNL) model. Motivated by various marketing scenarios, we introduce two distinct models that address the allocation of promotional effort: (1) <jats:italic>differentiated promotional effort</jats:italic>—the retailer can arbitrarily allocate promotional resources to each offered product; (2) <jats:italic>uniform promotional effort</jats:italic>—the retailer can determine a promotional level, and the promotional effort is equally distributed to each offered product. In the first model, the revenue‐ordered assortment strategy is optimal, and we can efficiently determine the optimal promotional effort level. In the second model, the revenue‐ordered assortment is no longer optimal. We develop a polynomial time algorithm to solve the joint optimization problem under this model. Using the algorithmic results, we conduct comparative analyses between the assortment optimization problem under the proposed models and the classic MNL model, which does not exert any promotional effort. We show that the assortment size shrinks when the retailer makes the promotional effort in the decision, which indicates that product variety and promotional effort are strategic substitutes. Moreover, the retailer and customers can be better off in the presence of promotional efforts, irrespective of the format. Additionally, we conduct extensive numerical experiments to demonstrate our analytical results and gain more managerial insights.","PeriodicalId":49772,"journal":{"name":"Naval Research Logistics","volume":null,"pages":null},"PeriodicalIF":1.9000,"publicationDate":"2024-04-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Naval Research Logistics","FirstCategoryId":"91","ListUrlMain":"https://doi.org/10.1002/nav.22187","RegionNum":4,"RegionCategory":"管理学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q3","JCRName":"OPERATIONS RESEARCH & MANAGEMENT SCIENCE","Score":null,"Total":0}
引用次数: 0

Abstract

Promotional effort is a common strategy to induce sales and broaden the market scope by enhancing the products' utility to customers. In this article, we incorporate promotional effort into the customer choice model and study the joint promotional effort and assortment optimization problems, where the customer's choice behavior follows the widely used multinomial‐logit (MNL) model. Motivated by various marketing scenarios, we introduce two distinct models that address the allocation of promotional effort: (1) differentiated promotional effort—the retailer can arbitrarily allocate promotional resources to each offered product; (2) uniform promotional effort—the retailer can determine a promotional level, and the promotional effort is equally distributed to each offered product. In the first model, the revenue‐ordered assortment strategy is optimal, and we can efficiently determine the optimal promotional effort level. In the second model, the revenue‐ordered assortment is no longer optimal. We develop a polynomial time algorithm to solve the joint optimization problem under this model. Using the algorithmic results, we conduct comparative analyses between the assortment optimization problem under the proposed models and the classic MNL model, which does not exert any promotional effort. We show that the assortment size shrinks when the retailer makes the promotional effort in the decision, which indicates that product variety and promotional effort are strategic substitutes. Moreover, the retailer and customers can be better off in the presence of promotional efforts, irrespective of the format. Additionally, we conduct extensive numerical experiments to demonstrate our analytical results and gain more managerial insights.
查看原文
分享 分享
微信好友 朋友圈 QQ好友 复制链接
本刊更多论文
多项式对数模型下的联合促销和分类优化
促销是一种常见的策略,通过提高产品对顾客的效用来促进销售和扩大市场范围。在本文中,我们将促销活动纳入顾客选择模型,并研究了促销活动与分类优化的联合问题,其中顾客的选择行为遵循广泛使用的多项式对数(MNL)模型。受各种营销场景的启发,我们引入了两种不同的模型来解决促销力度的分配问题:(1) 有区别的促销力度--零售商可以任意地将促销资源分配给所提供的每种产品;(2) 统一的促销力度--零售商可以确定一个促销水平,促销力度平均分配给所提供的每种产品。在第一种模式中,收入排序分类策略是最优的,我们可以有效地确定最优促销力度水平。在第二个模型中,收入排序分类不再是最优的。我们开发了一种多项式时间算法来解决该模型下的联合优化问题。利用算法结果,我们对所提模型下的分类优化问题和经典的 MNL 模型进行了比较分析。我们发现,当零售商在决策中做出促销努力时,分类规模会缩小,这表明产品种类和促销努力是战略替代品。此外,无论采用哪种促销形式,零售商和顾客都能获得更好的收益。此外,我们还进行了大量的数字实验来证明我们的分析结果,并获得更多的管理启示。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
求助全文
约1分钟内获得全文 去求助
来源期刊
Naval Research Logistics
Naval Research Logistics 管理科学-运筹学与管理科学
CiteScore
4.20
自引率
4.30%
发文量
47
审稿时长
8 months
期刊介绍: Submissions that are most appropriate for NRL are papers addressing modeling and analysis of problems motivated by real-world applications; major methodological advances in operations research and applied statistics; and expository or survey pieces of lasting value. Areas represented include (but are not limited to) probability, statistics, simulation, optimization, game theory, quality, scheduling, reliability, maintenance, supply chain, decision analysis, and combat models. Special issues devoted to a single topic are published occasionally, and proposals for special issues are welcomed by the Editorial Board.
期刊最新文献
Partial information sharing in supply chains with ARMA demand Efficient online estimation and remaining useful life prediction based on the inverse Gaussian process Double‐sided queues and their applications to vaccine inventory management Optimal condition‐based parameter learning and mission abort decisions Single machine scheduling with the total weighted late work and rejection cost
×
引用
GB/T 7714-2015
复制
MLA
复制
APA
复制
导出至
BibTeX EndNote RefMan NoteFirst NoteExpress
×
×
提示
您的信息不完整,为了账户安全,请先补充。
现在去补充
×
提示
您因"违规操作"
具体请查看互助需知
我知道了
×
提示
现在去查看 取消
×
提示
确定
0
微信
客服QQ
Book学术公众号 扫码关注我们
反馈
×
意见反馈
请填写您的意见或建议
请填写您的手机或邮箱
已复制链接
已复制链接
快去分享给好友吧!
我知道了
×
扫码分享
扫码分享
Book学术官方微信
Book学术文献互助
Book学术文献互助群
群 号:481959085
Book学术
文献互助 智能选刊 最新文献 互助须知 联系我们:info@booksci.cn
Book学术提供免费学术资源搜索服务,方便国内外学者检索中英文文献。致力于提供最便捷和优质的服务体验。
Copyright © 2023 Book学术 All rights reserved.
ghs 京公网安备 11010802042870号 京ICP备2023020795号-1