Forecasting using reference prices with exposure effect
Bibliographic record
Abstract
Abstract Reference prices (RPs) are consumers' subjective perceptions of prices that have important influences on purchase decisions. The standard RP formulation, which defines RP as an exponentially weighted average of past prices, ignores a certain asymmetry in weights between the regime of a price decrease and that of a price increase, which can be observed by the demand trend during the few days after a price decrease or increase. Such oversight usually leads to overestimation in demand as we illustrate by empirical evidence. We introduce the novel concept of RP with exposure effect (RPEE) that captures such asymmetry in RP formulation by imposing a weight proportional to how much the price is exposed to consumers. The exposure effect can be measured by clickstream data that are available for most e‐retailing platforms. We develop a customer behavioral model that can explain the formation of standard RP, and extend it in a natural way to provide foundation to the use of RPEE, especially for products with few repeat purchases. We then establish empirically the extensive benefit of forecasting from RPEE for e‐retailers that sell thousands of products. We demonstrate that RPEE exhibits significant and consistent improvement over standard RP for products, with around reduced weighted mean absolute percentage error.
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.
How this classification was reachedexpand
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 | 0.011 | 0.013 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".