Multicategory purchase behavior: basket choice, shopping frequency, and promotional analysis
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
This research introduces a new tool for analyzing both what customers buy and how often they shop. Unlike traditional models that focus only on in-store purchases, the MVL-Poisson Model captures shopping frequency, basket composition, and consumer response to prices and promotions. It segments customers by preferences and visit-frequency, reveals cross-category demand relationships, and highlights how promotions influence not just purchases but also store visits. It is computationally practical and can be implemented with standard retail data and analytics software. In an application to convenience store data, the model had high predictive accuracy and generated insights aligned with managerial intuition. We found that shoppers with similar preferences may visit at very different rates—a critical finding for targeting promotions effectively. Focusing only on in-store behavior underestimates the impact of promotions, as promotions also drive store traffic. Using insights on consumer preferences and cross-category relationships, we show how our model can be used to create optimal bundle promotions customized to particular segments.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it