A Salesforce-Driven Model of Consumer Choice
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 paper studies how salespeople affect the choices of which products consumers choose, and from that, how a firm should set optimal commissions as a function of the appeal, substitutability, and profit margins of different products. We also examine whether firms are better off promoting products through sales incentives or price discounts. To achieve these goals, we develop a salesforce-driven consumer choice model to study how performance-based commissions incentivize a salesperson’s service effort toward heterogeneous, substitutable products carried by a firm. The model treats the selling process as a joint decision by the salesperson and the consumer. It allows the salesperson’s efforts to vary across different transactions, depending on the unique preferences of each consumer, and incorporates the effects of commissions and other marketing mix elements on the selling outcome in a unified framework. We estimate the model using data from a car dealership. We find that the optimal commissions should be lower for popular items and for items that are closer substitutes with other products. We also find that for the car industry we study, the cost of selling more cars using sales incentives is cheaper than the cost of selling the same number of cars using price discounts.
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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 | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 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