Optimizing online recurring promotions for dual-channel retailers: Segmented markets with multiple objectives
Optimizing online recurring promotions for dual-channel retailers: Segmented markets with multiple objectives
复制标题
优化双渠道零售商的在线定期促销：具有多个目标的细分市场
DOI：
10.1016/j.ejor.2017.11.059
复制
发表时间：
2017-12
影响因子：
6.4
通讯作者：
Qingfu Zhang
中科院分区：
文献类型：
--
作者：
Yuanchun Jiang;Yezheng Liu;Jennifer Shang;Pinar Yildirim;Qingfu Zhang
网上推广有助提高品牌知名度及促进销售。尽管它吸引了顾客流量，但考虑不周的价格促销会产生严重影响，因为它会不成比例地吸引廉价买家，导致利润侵蚀，并因不稳定的需求而造成运营混乱。这项研究提出了一个长期的优化模型，以帮助双渠道（点击和迫击炮）零售商了解必要的条件，以促进产品在线所有市场。当推荐部分市场时，我们研究如何在每个时间段内定价和选择市场组合进行促销。我们开发了一个多目标进化算法，以有效地解决复杂和大规模的问题。理论分析和数值研究表明，该模型优于传统的在线促销策略，但由于其动态性，多周期重复促销问题难以得到最优解决。我们的模型能够规划多个时期、多个市场和多个目标，以最大限度地提高长期盈利能力和竞争力。点击和实体零售商将发现我们的方法非常有效，可以最大限度地提高利润，增强品牌知名度，提高客户满意度。
Online promotion helps enhance brand awareness and boost sales. Although it attracts customer traffic, an ill-conceived price promotion has serious repercussions because it disproportionately draws bargain hunters, results in profit erosion and causes operational chaos due to erratic demands. This research proposes a long-term optimization model to help dual channel (click-and-mortar) retailers understand the conditions necessary to promote products online across all markets. When partial markets are recommended, we investigate how to price and select the market portfolio for promotion in each time period. We develop a multi-objective evolutionary algorithm to efficiently solve complex and large-scale problems. Both theoretical analysis and numerical study show that the proposed model outperforms the conventional strategy of promoting online across the board.Due to its dynamic nature, the multi-period recurring promotion problem is difficult to address optimally. Our model is capable of planning for multiple periods, multiple markets, and multiple objectives to maximize long term profitability and competitiveness. Click-and-mortar retailers will find our approach extremely effective for maximizing profit, enhancing brand awareness, and improving customer satisfaction.
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影响因子：
6.1
作者：
Dinner, Isaac M.;Van Heerde, Harald J.;Neslin, Scott A.
通讯作者：
Neslin, Scott A.
影响因子：
12.9
作者：
Sridhar, Shrihari;Germann, Frank;Grewal, Rajdeep
通讯作者：
Grewal, Rajdeep
DOI：
10.1086/367748
发表时间：
2003-04
期刊：
ERN: Pricing (Topic)
影响因子：
--
作者：
R. Venkatesh;W. Kamakura
通讯作者：
R. Venkatesh;W. Kamakura
DOI：
10.1287/mnsc.1060.0604
发表时间：
2007
期刊：
IO: Empirical Studies of Firms & Markets
影响因子：
--
作者：
S. Sriram;Manohar U. Kalwani;S. Balachander;Pradeep Chintagunta;V. Kumar;Harikesh S. Nair;J. Pancras-J.-Panc
通讯作者：
S. Sriram;Manohar U. Kalwani;S. Balachander;Pradeep Chintagunta;V. Kumar;Harikesh S. Nair;J. Pancras-J.-Panc
多周期报童环境中服务相关需求的统计学习
影响因子：
2.7
作者：
Deng Tianhu;Shen Zuo-Jun Max;Shanthikumar J. George
通讯作者：
Shanthikumar J. George