Person:

Chung, Doug

Personal selling represents one of the most important elements in the marketing mix, and appropriate management of the sales force is vital to achieving the organization’s objectives. Among the various instruments of sales management, compensation plays a pivotal role in motivating and incentivizing sales agents. This monograph reviews the evolution of research in sales compensation and discusses future trends and opportunities. Specifically, it examines the managerial relevance of the theoretical foundations, discussing the underlying reasons for their applicability (or lack thereof) in practice. Furthermore, the monograph surveys recent empirical methods—including field experiments and structural econometrics—that are practical for analyzing sales agents’ behavior under various compensation systems. It also discusses prominent areas of future research in the midst of a changing sales environment. In particular, this monograph sheds light on how the use of big data, machine learning, and artificial intelligence can affect sales strategy formulation and, thus, sales compensation systems to better motivate and incentivize an organization’s sales force.

This study provides a comprehensive model of an agent’s behavior in response to multiple sales management instruments, including compensation, recruiting/termination, and training. The model takes into account many of the key elements that constitute a realistic sales force setting: allocation of effort, forward-looking behavior, present bias, training effectiveness, and employee selection and attrition. By understanding how these elements jointly affect agents’ behavior, the study provides guidance on the optimal design of sales management policies. A field validation, by comparing counterfactual and actual outcomes under a new policy, attests to the accuracy of the model. The results demonstrate a tradeoff between adjusting fixed and variable pay; how sales training serves as an alternative to compensation; a potential drawback of hiring high-performing, experienced salespeople; and how utilizing a leave package leads to sales force restructuring. In addition, the study offers a key methodological contribution by providing formal identification conditions for hyperbolic time preference. The key to identification is that under a multiperiod nonlinear incentive system, an agent’s proximity to a goal affects only future payoffs in nonpecuniary benefit periods, providing exclusion restrictions on the current payoff.

We estimate a sales response model to evaluate the short- and long-term value of pharmaceutical sales representatives’ detailing visits to physicians of different types. By understanding the dynamic effect of sales calls across heterogeneous doctors, we provide guidance on the design of optimal call patterns for route sales. Our analyses reveal that the long-term persistence effect of detailing is more pronounced for specialist physicians; the contemporaneous marginal effect is higher for generalists. Free samples have little effect on any type of physician. We also introduce a key methodological innovation to the marketing and economics literatures. We show that moment conditions—typically used in traditional dynamic panel data methods—are vulnerable to serial correlation in the error structure. However, traditional tests to detect serial correlation have weak power and can be misleading, resulting in misuse of moment conditions and incorrect inference. We present an appropriate set of moment conditions to properly address serially correlated errors in analyzing dynamic panel data.

This paper evaluates the short- and long-term value of sales representatives’ detailing visits to different types of physicians. By understanding the dynamic effect of sales calls across heterogeneous physicians, we provide guidance on the design of optimal call patterns for route sales. The findings reveal that the long-term persistence effect of detailing is more pronounced for specialist physicians, whereas the contemporaneous marginal effect is higher for generalists. The paper also provides a key methodological insight to the marketing and economics literature. In the Nerlove-Arrow framework, moment conditions that are typically used in conventional dynamic panel data methods become vulnerable to serial correlation in the error structure. We discuss the associated biases and present a robust set of moment conditions for both lagged dependent and predetermined explanatory variables. Furthermore, we show that conventional tests to detect serial correlation have weak power, resulting in the misuse of moment conditions that leads to incorrect inference. Theoretical illustrations and Monte Carlo simulations are provided for validation.