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Updated: Apr 3, 2026

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Published on: September 27, 2019
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Involving Sales Managers in Sales Force Compensation Design
Summary
Sales managers can provide valuable territory information for incentive design. A new "request mechanism" leverages this knowledge efficiently, improving firm outcomes compared to traditional methods.
Area of Science:
- Business Administration
- Management Science
- Economics
Background:
- Sales force incentive design often includes sales managers in compensation plan creation.
- Sales managers possess crucial territory-specific information but may have misaligned incentives.
- Existing methods for incorporating managerial input can be inefficient or prone to bias.
Purpose of the Study:
- To develop a game theoretic model for efficiently leveraging sales managers' territory knowledge in incentive design.
- To determine the conditions under which involving sales managers in incentive design is optimal for the firm.
- To propose a novel "request mechanism" as an alternative to traditional input methods.
Main Methods:
- A game theoretic model was employed to analyze the interaction between a firm and a sales manager.
- The model explores a "request mechanism" where firms delegate decisions within constraints, allowing managers to request relaxed constraints.
- The study compares this mechanism to established theoretical approaches, such as menu contracts for salespeople.
Main Results:
- The proposed "request mechanism" enables firms to efficiently leverage managers' true knowledge.
- Specific conditions are identified under which involving managers in incentive design is optimal.
- The mechanism provides an efficient and reliable alternative to negotiation and lobbying for managerial input.
Conclusions:
- The "request mechanism" effectively incorporates sales managers' territory-level information into incentive design.
- This approach ensures the firm's optimal outcome by setting appropriate constraints and requirements.
- The mechanism demonstrates superior performance over existing theoretical models in certain scenarios.
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