The Impact of Advertising Creative Strategy on Advertising Elasticity
Bibliographic record
Abstract
This study provides a comprehensive assessment of the impact of advertising creative strategy (ACS) on advertising elasticity, founded on an integrative framework that distinguishes between the function (content) and form (execution) of an advertising creative. The authors evaluate function using a three-dimensional representation of content (experience, affect, cognition), whereas the representation of form accounts for both executional elements and the use of creative templates. The distinction between function and form allows for the investigation of potential synergies between content and execution, previously unaccounted for in the literature. The ACS framework also facilitates the calculation of composite metrics that capture holistic aspects of the creative strategy, such as focus (i.e., the extent of the emphasis on a specific content dimension) and variation (i.e., changes in content and execution over time). The empirical application focuses on a dynamic linear model analysis of 2,251 television advertising creatives from 91 brands in 16 consumer packaged goods categories. The findings show that for function, experiential content has the greatest effect on elasticity, followed by cognitive and affective content. Function and form produce synergies that advertisers can leverage to increase returns. Finally, focus, variation, and the use of templates increase advertising elasticity.
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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 | 0.005 | 0.040 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 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".