Optimal Incentives for Salespeople with Learning Potential
Management Science · 2023 · Volume 69 · Issue 6
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. This paper was accepted by Juanjuan Zhang, marketing. Supplemental Material: The e-companion is available at https://doi.org/10.1287/mnsc.2022.4509 .
Keywords
salesforce, learning, compensation, dynamic incentives, agency theory, information asymmetry
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