An Empirical Analysis of Search Engine Advertising: Sponsored Search in Electronic Markets

Abstract

T he phenomenon of sponsored search advertising-where advertisers pay a fee to Internet search engines to be displayed alongside organic (nonsponsored) Web search results-is gaining ground as the largest source of revenues for search engines. Using a unique six-month panel data set of several hundred keywords collected from a large nationwide retailer that advertises on Google, we empirically model the relationship between different sponsored search metrics such as click-through rates, conversion rates, cost per click, and ranking of advertisements. Our paper proposes a novel framework to better understand the factors that drive differences in these metrics. We use a hierarchical Bayesian modeling framework and estimate the model using Markov Chain Monte Carlo methods. Using a simultaneous equations model, we quantify the relationship between various keyword characteristics, position of the advertisement, and the landing page quality score on consumer search and purchase behavior as well as on advertiser's cost per click and the search engine's ranking decision. Specifically, we find that the monetary value of a click is not uniform across all positions because conversion rates are highest at the top and decrease with rank as one goes down the search engine results page. Though search engines take into account the current period's bid as well as prior click-through rates before deciding the final rank of an advertisement in the current period, the current bid has a larger effect than prior clickthrough rates. We also find that an increase in landing page quality scores is associated with an increase in conversion rates and a decrease in advertiser's cost per click. Furthermore, our analysis shows that keywords that have more prominent positions on the search engine results page, and thus experience higher click-through or conversion rates, are not necessarily the most profitable ones-profits are often higher at the middle positions than at the top or the bottom ones. Besides providing managerial insights into search engine advertising, these results shed light on some key assumptions made in the theoretical modeling literature in sponsored search.

How Does Ranking Affect User Choice in Online Search?

“…Athey and Meidan (2011) and Athey and Nekipelov (2011) are important papers on the analysis of user behavior in the paid results. Previous research has indicated that CTRs can increase markedly for results placed at the top of a results page compared to other ranks and pages (Smith and Brynjolfsson 2001;Xu and Kim 2008;Ghose and Yang 2009. Of these studies, Xu and Kim (2008) is based on a small-sample laboratory experiment, and Xu and Kim (2008) uses data on paid search from an advertiser.…”

Online keyword based advertising: Impact of ad impressions on own-channel and cross-channel click-through rates

“…To be consistent with previous literature (e.g. [9]), we also allow CTR on the search channel to be affected by the position of the ad (Rank).…”

“…improved rank allocation mechanisms ( [7], [17]); (iii) needs of advertisers -e.g. profitability of ad display rank [9]; (iv) behavior of advertisers, and implications for consumers who click on keyword based ads -e.g. quality uncertainty and adverse selection in performance based search advertising (use of cost per click) [1]; and (v) interaction effects between different aspects of keyword based search, with implications for advertisers -e.g.…”

“…Therefore, consistent with previous literature (e.g. [9]), we use both CPC and the one period lagged value of CTR to model rank in model (3) where the lagged dependent variable is again added to control for heterogeneity across ad groups. In a similar manner, we model impressions on the two channels as the functions of the CPC bids and the one period lagged value of CTRs on the two channels respectively in models (4) and (5).…”

Developing the Purchase Conversion Model of the Keyword Advertising Based on the Individual Search

“…In practice, many online sellers rely on keyword advertising to promote their sales in short term with limited budget. Most of the previous researches use direct revenue factors as dependent variables such as CTR (click through rate) and CVI (conversion per impression) in their researches on the keyword advertising [14,16,22,25,31,32]. Previous studies were, however, conducted in the context of aggregate-level due to the limitations on the data availability.…”