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

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. 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 sponsored search metrics such as click-through rates, conversion rates, cost-per-click, and ranking of advertisements. Our paper proposes a novel framework and data 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 (MCMC) 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 (i) retailer-specific keywords are associated with an increase in click-through and conversion rates while brand-specific keywords are associated with a decrease in click-through and conversion rates, (ii) the monetary value of a click is not uniform across all positions because conversion rates are highest at the top and decrease with rank on as one goes down the search engine results page, (iii) while 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 click-through rates, (iv) an increase in landing page quality scores is associated with an increase in conversion rates and a decrease in advertiser’s cost-per-click and (v) 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.

Keywords: Online advertising, Search engines, Hierarchical Bayesian modeling, Paid search, Clickthrough rates, Conversion rates, Keyword ranking, Bid price, Electronic commerce, Cross-Selling, Internet economics

JEL Classification: C33, C51, D12, L10, M31, M37, L81