Aggregate Effects of Advertising Decisions: A Complex Systems Look at Search Engine Advertising Via an Experimental Study

Yang, Yanwu; Li, Xin; Jansen, Bernard J.; Zeng, Daniel

doi:10.2139/ssrn.3206383

Cited by 9 publications

(14 citation statements)

References 68 publications

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“…We present modeling frameworks with 1-order and/or 2-order feature interactions and their performance in advertising CTR prediction in Sections 4.1 and 4.2. Although low-order models are easy to implement, they suffer from the underperformed prediction ability (Guo et al, 2018;Lian et al, 2018;Yan et al, 2020;, especially in complex advertising systems (Yang et al, 2018). Hence, it calls for prediction models enabling the high-order feature representation.…”

Section: Deep Learning Modelsmentioning

confidence: 99%

Click-through rate prediction in online advertising: A literature review

Yang

Zhai

2022

Information Processing & Management

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Section: Deep Learning Modelsmentioning

confidence: 99%

Click-through rate prediction in online advertising: A literature review

Yang

Zhai

2022

Information Processing & Management

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“…Theoretically, time-varying models break the study period into more fine-grained time intervals (rather than treat it as a single interval) and can reveal much more information about relationships between the explanatory variables and the dependent variable (Tan et al, 2012). Also, the best fit by cubic spline function, compared to the linear and quadratic functions, highlights the dynamic complexity of SEA markets (Yang et al, 2018).…”

Section: Model Fit Comparisonsmentioning

confidence: 99%

How Search Engine Advertising Affects Sales over Time: An Empirical Investigation

Yang,

Zhao,

Zeng

et al. 2020

Preprint

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As a mainstream marketing channel on the Internet, Search Engine Advertising (SEA) has a huge business impact and attracts a plethora of attention from both academia and industry.One important goal of advertising is to increase sales. Nevertheless, while previous research has studied multiple factors that are potentially related to the outcome of SEA campaigns, effects of these factors on actual sales generated by SEA remain understudied. It is also unclear whether and how such effects change over time in highly dynamic SEA campaigns. As the first empirical investigation of the dynamic advertisement-sales relationship in SEA, this study builds an advertising response model within a time-varying coefficient (TVC) modeling framework, and estimates the model using a unique dataset from a large E-Commerce retailer in the United States. Results reveal the effects of the advertising expenditure, consumer behaviors and advertisement characteristics on realized sales, and demonstrate that such effects on sales do change over time in non-linear ways. More importantly, we find that carryover has a stronger effect in generating sales than direct response does, conversion rate is much more important than click-through rate, and ad position does not have significant effects on sales. These findings have direct implications for advertisers to launch more effective SEA campaigns.

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“…In sponsored search advertising, an advertiser aims to obtain a set of frequent and relevant keywords for her products/services. Relevance is a kernel factor influencing advertising performance (Yang et al, 2018) and even the prospect of ecosystem of search advertising (Jansen, 2011). In particular, a higher relevance among keywords, advertisements and promoted products/services leads to a larger click-through rate and quality score 1 (Hillard et al, 2010).…”

Section: Framework Instantiationmentioning

confidence: 99%

“…Generally, a keyword in 𝑦 𝑖,𝑗 can only belong to one adgroup 𝑧 𝑖,𝑗,𝑙 . Organizing closely related keywords with adgroups allows advertisers to reach the right consumers with her ad campaigns (Yang et al, 2018). For an advertiser, well-organized keyword groups improve ad group performance and her account's relevance, resulting in lower PPC charges (WordStream, 2018).…”

Section: Solutionsmentioning

confidence: 99%

Keyword Optimization in Sponsored Search Advertising: A Multilevel Computational Framework

et al. 2019

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In sponsored search advertising, keywords serve as an essential bridge linking advertisers, search users and search engines. Advertisers have to deal with a series of keyword decisions throughout the entire lifecycle of search advertising campaigns. This paper proposes a multi-level and closed-form computational framework for keyword optimization (MKOF) to support various keyword decisions. Based on this framework, we develop corresponding optimization strategies for keyword targeting, keyword assignment and keyword grouping at different levels (e.g., market, campaign and adgroup). With two real-world datasets obtained from past search advertising campaigns, we conduct computational experiments to evaluate our keyword optimization framework and instantiated strategies. Experimental results show that our method can approach the optimal solution in a steady way, and it outperforms two baseline keyword strategies commonly used in practice. The proposed MKOF framework also provides a valid experimental environment to implement and assess various keyword strategies in sponsored search advertising.

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Aggregate Effects of Advertising Decisions: A Complex Systems Look at Search Engine Advertising Via an Experimental Study

Cited by 9 publications

References 68 publications

Click-through rate prediction in online advertising: A literature review

Click-through rate prediction in online advertising: A literature review

How Search Engine Advertising Affects Sales over Time: An Empirical Investigation

Keyword Optimization in Sponsored Search Advertising: A Multilevel Computational Framework

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