The Anatomy of the Advertising Budget Decision: How Analytics and Heuristics Drive Sales Performance
Bibliographic record
Abstract
The authors investigate how heuristics and analytics contribute to the advertising budget decision by decomposing it into four components: (1) baseline spending, (2) adaptive experimentation, (3) advertising-to-sales ratio, and (4) competitive parity. They propose a methodology to estimate and infer the weights of these four components. Applying this methodology to sales and advertising data across eight brands from three categories substantiates for the first time, and uniformly across all brands, that managers depart from optimality through adaptive experimentation, which is in line with dual control theory that suggests they do so to learn about advertising effectiveness. The adaptive experimentation finding, combined with evidence on the use of heuristic methods, suggests that budget decision making is characterized by bounded rationality. Furthermore, budgeting decisions are brand-specific, reflecting the considerations of a brand’s market position and performance. Finally, simulation studies show that brands from categories with high uncertainty in advertising effectiveness can benefit from double-digit revenue lifts by placing higher emphasis on adaptive experimentation.
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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 | 0.045 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.007 | 0.005 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".