考虑消费者广告疲劳的最佳动态广告政策

IF 6.7 1区 计算机科学 Q1 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE
Rui Guo , Zhengrui Jiang
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引用次数: 0

摘要

在数字广告时代,消费者每天都会受到各种渠道铺天盖地的广告轰炸。虽然反复接触广告可以吸引消费者的注意力并刺激他们购买,但必须认识到,过度的广告活动会导致消费者疲劳、反应迟钝甚至恼怒。在本研究中,我们的目标是在考虑消费者广告疲劳影响的情况下制定最佳广告政策。通过引入一个衰减因子来考虑过度广告支出的负面影响,我们修改了经典 Nerlove-Arrow 模型中的商誉动态。这一修改导致广告响应函数与广告支出呈倒 U 型曲线。我们的分析表明,当消费者出现广告疲劳时,企业最好按比例减少所有时段的广告支出。然而,尽管消费者出现了广告疲劳,企业仍应保持最佳广告支出的原有轨迹方向。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Optimal dynamic advertising policy considering consumer ad fatigue

In the age of digital advertising, consumers are bombarded with an overwhelming number of advertisements from various channels every day. While repeated exposures to advertising can capture consumers' attention and stimulate their purchases, it is crucial to recognize that excessive advertising campaigns can cause consumer fatigue, diminished responsiveness, or even irritation. In the present study, our objective is to formulate the optimal advertising policy considering the effect of consumer ad fatigue. By introducing an attenuation factor that accounts for the negative impact of excessive advertising expenditure, we modify the dynamics of goodwill in the classical Nerlove-Arrow model. This modification results in the advertising response function exhibiting an inverted U-shaped curve with respect to the advertising expenditure. Our analysis indicates that when consumers experience ad fatigue, it is advisable for the firm to proportionally reduce its advertising expenditure in all time periods. However, the firm should maintain its original direction of trajectory of optimal advertising expenditure despite the presence of consumer ad fatigue.

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来源期刊
Decision Support Systems
Decision Support Systems 工程技术-计算机:人工智能
CiteScore
14.70
自引率
6.70%
发文量
119
审稿时长
13 months
期刊介绍: The common thread of articles published in Decision Support Systems is their relevance to theoretical and technical issues in the support of enhanced decision making. The areas addressed may include foundations, functionality, interfaces, implementation, impacts, and evaluation of decision support systems (DSSs).
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