A structural model of sales-force compensation dynamics: Estimation and field implementation |
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Authors: | Sanjog Misra Harikesh S Nair |
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Institution: | (1) Simon School of Business Administration, University of Rochester, Rochester, NY, USA;(2) Graduate School of Business, Stanford University, Stanford, CA, USA |
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Abstract: | We present an empirical framework to analyze real-world sales-force compensation schemes, and report on a multi-million dollar,
multi-year project involving a large contact lens manufacturer at the US, where the model was used to improve sales-force
contracts. The model is built on agency theory, and solved using numerical dynamic programming techniques. The model is flexible
enough to handle quotas and bonuses, output-based commission schemes, as well as “ratcheting” of compensation based on past
performance, all of which are ubiquitous in actual contracts. The model explicitly incorporates the dynamics induced by these
aspects in agent behavior. We apply the model to a rich dataset that comprises the complete details of sales and compensation
plans for the firm’s US sales-force. We use the model to evaluate profit-improving, theoretically-preferred changes to the
extant compensation scheme. These recommendations were then implemented at the focal firm. Agent behavior and output under
the new compensation plan is found to change as predicted. The new plan resulted in a 9% improvement in overall revenues,
which translates to about $12 million incremental revenues annually, indicating the success of the field-implementation. The
results bear out the face validity of dynamic agency theory for real-world compensation design. More generally, our results
fit into a growing literature that illustrates that dynamic programming-based solutions, when combined with structural empirical
specifications of behavior, can help significantly improve marketing decision-making, and firms’ profitability. |
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