An empirical analysis of sponsored search performance in search engine advertising

Abstract

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. Despite the growth of search advertising, we have little understanding of how consumers respond to contextual and sponsored search advertising on the Internet. Using a unique 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, bid prices and keyword ranks. 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. We empirically estimate the impact of keyword attributes on consumer search and purchase behavior as well as on firms' decision-making behavior on bid prices and ranks. We find that the presence of retailer-specific information in the keyword increases click-through rates, and the presence of brand-specific information in the keyword increases conversion rates. We also demonstrate that as suggested by anecdotal evidence, search engines like Google factor in both the auction bid price as well as prior click-through rates before allotting a final rank to an advertisement. To the best of our knowledge, this is the first study that uses real world data from an advertiser and jointly estimates the effect of sponsored search advertising at a keyword level on consumer search, click and purchase behavior in electronic markets