Quantitative Marketing and Economics

, Volume 9, Issue 3, pp 211–257 | Cite as

A structural model of sales-force compensation dynamics: Estimation and field implementation

  • Sanjog Misra
  • Harikesh S. NairEmail author


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.


Compensation Scheme Demand Shock Policy Function Effort Policy Compensation Policy 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.



We thank Dan Ackerberg, Lanier Benkard, Adam Copeland, Paul Ellickson, Liran Einav, Wes Hartmann, Gunter Hitsch, Phil Haile, Sunil Kumar, Ed Lazear, Philip Leslie, Kathryn Shaw, Seenu Srinivasan, John Van Reenan, and seminar participants at Berkeley, Chicago, Kellogg, NUS, NYU, Rochester, Stanford, UC Davis, Yale, as well as the Marketing Science, Marketing Dynamics, NBER-IO, SICS, SITE, and UTD FORMS conferences, for their helpful feedback. Finally, we thank the management of the anonymous, focal firm in the paper for providing data, for innumerable interviews, and for their support, without which this research would not have been possible. We remain, however, responsible for all errors, if any.


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Copyright information

© Springer Science+Business Media, LLC 2011

Authors and Affiliations

  1. 1.Simon School of Business AdministrationUniversity of RochesterRochesterUSA
  2. 2.Graduate School of BusinessStanford UniversityStanfordUSA

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