Competing “Creatively” in Sponsored Search Markets: The Effect of Rank, Differentiation Strategy, and Competition on Performance
Why this work is in the frame
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Bibliographic record
Abstract
Although efficiency-enhancing features of online markets have been well studied, much less is known about firms' differentiation strategies in these competitive markets or the outcomes of such differentiation. This study examines competition among firms in online sponsored search markets—one of the fastest growing and most competitive of online markets. We develop and test a model that predicts the clickthrough rate (CTR) of a seller's listing in a sponsored search setting. Drawing on consumer search theory and competitive positioning strategies, we theorize that CTR is jointly driven by a seller's positioning strategy as reflected by the unique selling proposition (USP) in its “ad creative,” by its rank in a sponsored search listing, and by the nature of competition around the focal firm's listing. We use data from a field experiment conducted by a leading firm in the mortgage industry where the firm varied its rank and USP dynamically. Results suggest that sponsored search listings can act as effective customer segmentation mechanisms, consistent with a model of consumer search in directional markets. We further find that the effect on CTR of a firm's positioning strategy and its rank in a listing is strongly moderated by its ability to differentiate itself from adjacent rivals. We discuss the implications of our findings for sellers' strategies in sponsored search markets and for extending the understanding of consumer search behavior in directional markets.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.007 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 it