Assessment of Advertising Effectiveness Through Audience’s Eye Movements
Bibliographic record
Abstract
The evaluation of advertisement effectiveness during the advertisement design phase and pre-launch phase is critical for the advertisement’s success in the targeted market. This evaluation should predict advertisement’s final performance as accurately as possible. In today’s advertisement business, questionnaire-based evaluation methods, such as attitude and opinion rating are widely used. To obtain good survey results, high quality questionnaires and proper interviewing procedures have to be developed with the support of the competent execution and supervision. These activities are usually costly even though some of them can be conducted online. This paper proposes a novel method for assessing the advertisement effectiveness through the automated capturing and analyzing of audiences’ eye movements. This method is based on the assumption that some attributes of audiences’ eye movements are correlated to their visual attention defined in the context of advertisement effectiveness. To validate our research hypotheses, experiments were conducted. In the experiments, subjects were required to watch several advertisements in sequence and the subjects’ eye movement data were collected simultaneously. By analyzing the data patterns and comparing them with the effectiveness evaluation obtained from questionnaire-based method, we found that the proposed method produces similar evaluations to those resulted from the traditional attitude and opinion rating method.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 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".