Investigating Consumers’ Purchase Incidence and Brand Choice Decisions Across Multiple Product Categories: A Theoretical and Empirical Analysis
Bibliographic record
Abstract
We propose a framework to investigate consumers’ brand choice and purchase incidence decisions across multiple categories, where both decisions are modeled as an outcome of a consumer’s basket utility maximization. We build the model from first principles by theoretically explicating a general model of basket utility maximization and then examining the reasonable restrictions that can be placed to make the solution tractable without sacrificing its flexibility. Comparing with prior models, we show why prior multicategory purchase incidence models overemphasize the role of the cross effects of a market mix of brands in other categories on the purchase incidence decision of a given category. Additionally, we show that prior single-category models are a special case of the proposed model when further restrictions are placed on the basket utility structure. We estimate the model on household basket data for the laundry family of categories. We show (i) why prior single-category and multicategory models would systematically bias the estimates of the own- and cross-price/promotional purchase incidence elasticities; and (ii) how the market mix of each brand in each category affects the purchases across all categories, which can help retailers make promotional decisions across a portfolio of products.
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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 | 0.022 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.010 | 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".