论文标题
在不确定环境下赞助的搜索广告中的最佳关键字分组
Optimal Keywords Grouping in Sponsored Search Advertising under Uncertain Environments
论文作者
论文摘要
在赞助的搜索广告中，广告客户需要做出一系列关键字决策。其中，如何将这些关键字分组以在广告系列中形成几个ADGROUP是一项具有挑战性的任务，这是由于搜索广告的不确定环境。本文提出了一个用于关键字分组的随机编程模型，以随机变量为单击率和转换率，并考虑了预算约束和广告商的风险耐受性。开发了分支和结合算法来解决我们的模型。此外，我们进行了计算实验，以评估模型和解决方案的有效性，并从报告和搜索广告活动日志中收集了两个现实世界中的数据集。实验结果表明，我们的关键字分组方法的表现优于五个基础，并且可以以稳定的方式接近最佳。这项研究产生了一些有趣的发现，这些发现阐明了广告商在赞助搜索广告中的关键管理见解。首先，关键字分组对广告客户确实很重要，尤其是在有大量关键字的情况下。其次，在关键字分组决策中，边际利润并不一定表明随着预算的增加，现象的边缘减少。因此，为了获得额外的利润，广告商在关键字分组决策中增加预算是值得尝试的。第三，最佳关键字分组解决方案是各种广告因素之间多方面权衡的结果。特别是，将更多关键字分配给ADGROUP或拥有更多预算并不会带来更高的利润。这表明广告商警告说，将关键字数量作为关键字分组决策的标准是不明智的。
In sponsored search advertising, advertisers need to make a series of keyword decisions. Among them, how to group these keywords to form several adgroups within a campaign is a challenging task, due to the highly uncertain environment of search advertising. This paper proposes a stochastic programming model for keywords grouping, taking click-through rate and conversion rate as random variables, with consideration of budget constraints and advertisers' risk-tolerance. A branch-and-bound algorithm is developed to solve our model. Furthermore, we conduct computational experiments to evaluate the effectiveness of our model and solution, with two real-world datasets collected from reports and logs of search advertising campaigns. Experimental results illustrated that our keywords grouping approach outperforms five baselines, and it can approximately approach the optimum in a steady way. This research generates several interesting findings that illuminate critical managerial insights for advertisers in sponsored search advertising. First, keywords grouping does matter for advertisers, especially in the situation with a large number of keywords. Second, in keyword grouping decisions, the marginal profit does not necessarily show the marginal diminishing phenomenon as the budget increases. Such that, it's a worthy try for advertisers to increase their budget in keywords grouping decisions, in order to obtain additional profit. Third, the optimal keywords grouping solution is a result of multifaceted trade-off among various advertising factors. In particular, assigning more keywords into adgroups or having more budget won't certainly lead to higher profits. This suggests a warning for advertisers that it's not wise to take the number of keywords as the criterion for keywords grouping decisions.
