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Comparing performance metrics in organic search with sponsored search advertising
| Content Provider | ACM Digital Library |
|---|---|
| Author | Yang, Sha Ghose, Anindya |
| Abstract | With the rapid growth of search advertising, there has been an increased interest amongst both practitioners and academics in enhancing our understanding of how consumers respond to contextual and sponsored search advertising on the Internet. An emerging stream of work has begun to explore these issues. In this paper, we focus on a previously unexplored question: How does sponsored search advertising compare to organic listings with respect to predicting conversion rates, order values and profits from a keyword ad? We use a Hierarchical Bayesian modeling framework and estimate the model using Markov Chain Monte Carlo (MCMC) methods. Our analysis suggests that on an average while the conversion rates, order values and profits from paid search advertisements were much higher than those from natural search, most of the keyword-level characteristics have a statistically significant and stronger impact on these three performance metrics for organic search than paid search. This could shed light on understanding what the most "attractive" keywords are from advertisers' perspective, and how advertisers should invest in search engine advertising campaigns relative to search engine optimization. |
| Starting Page | 18 |
| Ending Page | 26 |
| Page Count | 9 |
| File Format | |
| ISBN | 9781605582771 |
| DOI | 10.1145/1517472.1517475 |
| Language | English |
| Publisher | Association for Computing Machinery (ACM) |
| Publisher Date | 2008-08-24 |
| Publisher Place | New York |
| Access Restriction | Subscribed |
| Subject Keyword | Paid search advertising Electronic commerce Organic search Internet economics Hierarchical bayesian modeling |
| Content Type | Text |
| Resource Type | Article |
