A Salesforce-Driven Model of Consumer Choice
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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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.004 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.003 | 0.001 |
| Research integrity | 0.004 | 0.002 |
| Insufficient payload (model declined to judge) | 0.019 | 0.003 |
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 source (direct Gemma or distilled Codex), 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".