Advertiser Prominence Effects in Search Advertising
Bibliographic record
Abstract
Search advertising is the ordered list of advertisements that appears when a user searches for something in an online search engine. By construction, these ads differ in prominence: ads higher up the list are more prominent than ads lower down the list. However, search ads also differ in prominence in another way: prominence of advertiser. This paper examines how these two types of prominence interact in determining the click-through rate (CTR) of these ads. Using individual-level click-stream data from Microsoft’s Live Search platform and measures of advertiser prominence from Alexa.com , we find that ad position and advertiser prominence are substitutes. Specifically, in searches for camera brands, a retailer not in the top 100 of Alexa rankings has a 30%–50% higher CTR in position 1 than in position 2, whereas a retailer in the top 100 of Alexa rankings has only a 0%–13% higher CTR for the same position improvement. Qualitatively similar results are obtained for several other search strings. These findings demonstrate, first, that advertiser brand matters even for search ads, and, second, the way it matters is the opposite of what is usually assumed in the theoretical literature on search advertising. The online appendix is available at https://doi.org/10.1287/mnsc.2016.2677 . This paper was accepted by Matthew Shum, marketing.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.031 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.018 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".