Optimal Salesforce Quota Plans Under Salesperson Job Equity Constraints
Bibliographic record
Abstract
Abstract This article shows how management can set motivating and profit maximizing salesforce quota plans and at the same time provide various forms of equity to salespersons, especially occupational equity (relative to external salespersons), job equity (relative to internal salespersons), or corporate equity (relative to the firm itself). Management can decide the type of equity it wants to provide to the sales force, taking into account the costs involved. To that effect, the article proposes an operational sales‐quota‐reward setting procedure based on a parsimonious number of parameters. Résumé Cet article montre comment une entreprise peut bâtir un plan de quotas motivant pour sa force de vente (et optimal en terme de profits pour l'entreprise), tout en assurant différentes formes d'équité aux vendeurs, et en particulier, l'équité par rapport aux vendeurs externes à l'entreprise, l'équité par rapport aux autres vendeurs de la force de vente, ou l'équité par rapport à l'entreprise elle‐même. La direction peut alors choisir la forme d'équité qu'elle veut assurer à ses commerciaux à la lueur des coûts encourus. Pour cela, l'article propose une procédure opérationnelle pour bâtir de tels plans de quotas‐primes basés sur un nombre parcimonieux de paramètres.
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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 | 0.003 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 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 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".