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竞价排名广告的关键词投放策略及其绩效研究——基于淘宝网的实证分析 被引量:15

Empirical study of Keywords biding strategy and search engine advertising performance
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摘要 我国的竞价排名广告市场正在快速发展,然而广告主对于不同的关键词投放策略及其对绩效的影响却仍处于初期探索阶段,少有精确的实证研究可以参考.本文以淘宝网的直通车竞价排名市场为研究对象,从点击量、直接销售和间接销售多维度来测量竞价排名广告的绩效及投资回报率,并通过计量分析探讨关键词选择与投放策略对这些绩效的影响.研究发现在同样的成本下,提高关键词的出价比增加关键词的数量更有价值;同时选择不同属性的关键词也会显著地影响竞价排名的绩效;该样本的竞价排名广告的投资回报率平均为4.5倍左右.本文完善了竞价排名市场的相关理论,也为改进当前广告主粗放式的关键词投放模式提供了理论的参考. The search engine advertising (SEA) market has been developing rapidly in recent years. The empirical research on keywords biding strategy and performances in China, however, is very limited. Based on a unique dataset of Zhitongche SEA in Taobao. corn, this paper applies an econometrics analysis to explore the relationship between keywords biding strategy and SEA performances. The multi-dimensional analysis indicates that increasing the biding price of keywords is more efficient in generating payback than increasing the number of keywords under equal costs. Meanwhile, considering brand information and specific products information during the keywords design is also helpful to raise the sales. The ROI of Zhitongche SEA reaches about 450 % in this study, suggesting a good return of SEA in Taobao. com. This study contributes to the SEA theory with the empirical results from the pure ecommerce websites. It also provides managerial implications for advertisers to improve their keyword biding strategy in SEA.
作者 卢向华
出处 《管理科学学报》 CSSCI 北大核心 2013年第6期1-9,共9页 Journal of Management Sciences in China
基金 国家自然科学基金资助项目(71172037 71128002) 阿里巴巴青年学者支持计划资助项目(Ali-2010-B-3)
关键词 竞价排名 关键词投放 投资回报 实证研究 search engine advertising keywords biding strategy ROI empirical study
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参考文献22

  • 1艾瑞咨询.2011年中国搜索引擎年度研究报告[R].2012.
  • 2Chen J Q, Liu D, Whinston A B. Auctioning keywords in online search[J]. Journal of Marketing, 2009, (73): 125 - 141.
  • 3张娥,汪应洛.关键字广告位拍卖的收益等价性研究[J].中国管理科学,2006,14(3):92-96. 被引量:22
  • 4Katona Z, Sarvary M. The race for sponsored links: Bidding patterns for search advertising[ W]. SSRN E-library. University of California, Berkeley, 2008.
  • 5Feng J, Bhargava H K, Pennock D. Implementing paid placement in search engines : Computational evaluation of alternative mechanisms[J]. INFORMS Journal of Computing, 2007, 19(1) : 137 - 148.
  • 6陈磊,刘奕群,茹立云,马少平.基于用户日志挖掘的搜索引擎广告效果分析[J].中文信息学报,2008,22(6):92-97. 被引量:16
  • 7Rutz 0 J, Trusov M. Zooming in on paid search ads : A consumer-level model calibrated on aggregated data[ J]. Marketing Science, 2011, 30(5): 789-800.
  • 8Montgomery A L, Li S, Srinivasan K, et al. Modeling online browsing and path analysis using click stream data[ J] . Mar- keting Science, 2004, 23(4) : 579 -595.
  • 9Ghose A, Yang S. Modeling Cross-Category Purchases in Sponsored Search Advertising[ W]. SSRN eLibrary, 2009.
  • 10Drze X, Hussherr F. Internet advertising: Is anybody watching? [ J]. Journal of Interactive Marketing, 2003, 17(4): 8 -23.

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