Measuring the Efficiency of Category-Level Sales Response to Promotions
Bibliographic record
Abstract
In this study, we focus on measuring the efficiency of category-level sales response to promotions across various categories and stores. Our heterogeneous stochastic frontier model allows us to attribute portions of this efficiency to specific characteristics of the stores and categories. Using our full PEM (promotional efficiency frontier) model, we analyze the efficiency of 20 frequently bought categories of a supermarket retailer and apply it to store-category-level data. We find that the average efficiency of category and store sales response across all categories and stores is 84.34%, with low values in categories such as spreads and fresh seafood and high values in categories such as frozen entrées and meat. We find that the variation in efficiency of this sales response can be attributed to specific store and category characteristics such as selling area of store, distance to competition, number of stock-keeping units in the category, and average interpurchase time. Unobserved heterogeneity is captured by the latent class approach that provides support for the existence of three segments. An understanding of the roles played by these characteristics in the efficiency of sales response can aid managers in devising a strategy that maximizes sales. This paper was accepted by Pradeep Chintagunta, marketing.
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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 | 0.003 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.001 |
| 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 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".