An Empirical Analysis of Search Engine Advertising: Sponsored Search and Cross-Selling in Electronic Markets
AbstractThe 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 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. Our analysis provides some evidence that advertisers are not bidding optimally with respect to maximizing the profits. 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. Finally, we conduct a detailed analysis with product level variables to explore the extent of cross-selling opportunities across different categories from a given keyword advertisement. We find that there exists significant potential for cross-selling through search keyword advertisements. Latency (the time it takes for consumer to place a purchase order after clicking on the advertisement) and the presence of a brand name in the keyword are associated with consumer spending on product categories that are different from the one they were originally searching for on the Internet.
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Bibliographic InfoPaper provided by NET Institute in its series Working Papers with number 07-35.
Length: 39 pages
Date of creation: Sep 2007
Date of revision: Sep 2007
Contact details of provider:
Web page: http://www.NETinst.org/
Online advertising; Search engines; Hierarchical Bayesian modeling; Paid search; Clickthrough rates; Conversion rates; Keyword ranking; Bid price; Electronic commerce; Cross-Selling; Internet economics.;
Find related papers by JEL classification:
- C33 - Mathematical and Quantitative Methods - - Multiple or Simultaneous Equation Models; Multiple Variables - - - Models with Panel Data; Spatio-temporal Models
- C51 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Model Construction and Estimation
- D12 - Microeconomics - - Household Behavior - - - Consumer Economics: Empirical Analysis
- L10 - Industrial Organization - - Market Structure, Firm Strategy, and Market Performance - - - General
- M31 - Business Administration and Business Economics; Marketing; Accounting - - Marketing and Advertising - - - Marketing
- M37 - Business Administration and Business Economics; Marketing; Accounting - - Marketing and Advertising - - - Advertising
- L81 - Industrial Organization - - Industry Studies: Services - - - Retail and Wholesale Trade; e-Commerce
This paper has been announced in the following NEP Reports:
- NEP-ALL-2007-10-27 (All new papers)
- NEP-ICT-2007-10-27 (Information & Communication Technologies)
- NEP-MKT-2007-10-27 (Marketing)
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