A segment‐level hazard approach to studying household purchase timing decisions
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Bibliographic record
Abstract
Abstract The increasing availability of customer‐level data and the willingness of marketers to customize the timing of their offers to consumers makes the accurate segment‐level description of household purchase timing decisions a compelling issue. In this paper we employ a finite mixture accelerated failure time model to identify and characterize household segments in terms of their purchasing rates and their propensity to accelerate purchases due to marketing mix activities. Such an approach also promises to alleviate possible aggregation problems arising from the use of a common hazard rate for all households. An application to household panel data suggested that infrequent buyers show higher propensity to accelerate than frequent buyers do, and that positive duration effects are underestimated when not accounting for segment‐specific hazard rates. Copyright © 2002 John Wiley & Sons, Ltd.
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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 | 0.003 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 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