A Multicategory Model of Consumers' Purchase Incidence, Quantity, and Brand Choice Decisions: Methodological Issues and Implications on Promotional Decisions
Bibliographic record
Abstract
The authors propose a multicategory model of consumers' purchase incidence, quantity, and brand choice decisions. The model specification allows for cross-category promotion effects in both components of the primary demand (incidence and quantity decisions) and uses a flexible functional form of consumer's utility to accurately measure those cross-category effects. To demonstrate the importance of the methodology, the authors investigate two issues of relevance to retailers, namely, how retailers should (1) allocate promotional expenditures across brands in a category and (2) coordinate timing of promotions of brands across categories. The authors estimate the proposed model using consumers' purchases in pasta sauce and pasta categories. The results reveal that using restrictive functional forms of utilities or ignoring cross-category effects in incidence and quantity decisions leads to incorrect assessments on relative allocation of promotional expenditures across brands. Furthermore, retailers are better off contemporaneously promoting brands across the two categories than promoting them in different periods, and ignoring cross-category effects in quantity decisions leads to the opposite inference, namely, that retailers are better off promoting brands across the two categories in different periods.
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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 | 0.006 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.004 | 0.002 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.009 | 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".