Mindset Metrics in Market Response Models: An Integrative Approach
Bibliographic record
Abstract
Demonstrations of marketing effectiveness currently proceed on two parallel tracks: quantitative researchers model the direct sales effects of the marketing mix, while advertising and branding experts trace customer mindset metrics like awareness and affect. We merge the two tracks and analyze the added explanatory value of including customer mindset metrics in a sales response model that already accounts for short and long-term effects of advertising, price, distribution and promotion. Vector Autoregressive modeling of the metrics for over 60 brands of four consumer goods shows that advertising awareness, brand consideration and brand liking account for almost one-third of explained sales variance. Interestingly, competitive and own mindset metrics make a similar contribution. Wear-in times reveal that mindset metrics can be used as advance warning signals that allow enough time for managerial action before market performance itself is affected. Specific marketing actions impact specific mindset metrics, with the strongest overall impact for distribution. Our findings suggest that modelers should include mindset metrics in sales response models, while branding experts should include competition in their tracking research.
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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 | 0.016 | 0.055 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.005 | 0.003 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.005 | 0.006 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.005 | 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".