An Empirical Analysis of Search Engine Advertising: Sponsored Search in Electronic Markets
Social Science Research Network
The phenomenon of sponsored search advertising -where advertisers pay a fee to Internet search engines to be displayed alongside organic (non-sponsored) web search results -is gaining ground as the largest source of revenues for search engines. Using a unique 6 month panel dataset of several hundred keywords collected from a large nationwide retailer that advertises on Google, we empirically model the relationship between different metrics such as click-through rates, conversion rates,
... ion rates, cost-per-click, and ranks of these advertisements. Our paper proposes a novel framework and data to better understand what drives these differences. We use a Hierarchical Bayesian modeling framework and estimate the model using Markov Chain Monte Carlo (MCMC) methods. Using a simultaneous equations model, we quantify the impact of keyword type and length, position of the advertisement and the landing page quality on consumer search and purchase behavior as well as on advertiser's cost per click and the search engine's ranking decision for different ads. Our results provide descriptive and quantitative insights to advertisers about what attributes of sponsored keyword advertisements contribute to variation in advertiser value, and how much to invest in search engine optimization campaigns versus search engine marketing campaigns. Our analyses also lend quantitative insights into the relative economic impact of different kinds of advertisements such as retailer-specific ads, brand specific ads or generic ads. We also discuss how our empirical estimates shed light on some assumptions made by existing theoretical models in sponsored search advertising.