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Optimal Incentives for Salespeople with Learning Potential

Author

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  • Long Gao

    (School of Business, The University of California, Riverside, California 92521)

Abstract

We study a compensation problem for salespeople with learning potential. In our model, both the firm and sales agent are risk neutral and forward-looking; the agent can privately observe his skill, exert effort, and learn from experience; the firm can learn from the agent’s choice and revise sales targets over time. The problem entails a dynamic tradeoff between exploiting learning, screening information, and maximizing efficiency. We find the optimal compensation plan differs substantially from the existing ones: it sets aggressive targets for expediting skill development, and pays the information rent for neutralizing the agent’s misbehaving temptation over the entire relationship. We find learning drives the long-run outcomes; ignoring it can mislead compensation design and inflict substantial losses. Our results shed light on when and why firms distort sales, favor incumbents, and prefer long-term plans. By highlighting the critical role of learning in long-run performance, this study advances our understanding of salesforce theory and practice.

Suggested Citation

  • Long Gao, 2023. "Optimal Incentives for Salespeople with Learning Potential," Management Science, INFORMS, vol. 69(6), pages 3285-3296, June.
    • Handle: RePEc:inm:ormnsc:v:69:y:2023:i:6:p:3285-3296
    DOI: 10.1287/mnsc.2022.4509
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    References listed on IDEAS

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