Byungyeon Kim is a Doctoral Student in the Marketing Unit at Harvard Business School. He is mainly interested in personal selling and sales management, empirical industrial organization, and microeconomic utility models. Byungyeon received Master of Science in Marketing and Bachelor in Business Administration, both from Korea University Business School. Prior to his studies, Byungyeon worked as a marketing manager at POSCO.
Byungyeon Kim is a Doctoral Student in the Marketing Unit at Harvard Business School. He is mainly interested in personal selling and sales management, empirical industrial organization, and microeconomic utility models. Byungyeon received Master of Science in Marketing and Bachelor in Business Administration, both from Korea University Business School. Prior to his studies, Byungyeon worked as a marketing manager at POSCO.
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 on agents’ behavior takes into account many of the key elements that constitute a realistic sales force setting—allocation of effort, forward-looking behavior, present bias, effectiveness of training, and employee selection and attrition. By understanding how these elements jointly influence agents’ behavior, the study provides guidance on the optimal design of sales management policies. Counterfactual experiments show ways in which alternative compensation schemes, recruiting and termination policies, and sales training opportunities affect the selection and performance of the sales force. A field validation, by comparing counterfactual and actual outcomes under a new policy, attests the accuracy of the model. In addition, the study offers a key methodological contribution by providing formal identification conditions for hyperbolic time preferences. The key to identification is that, under a multi-period nonlinear incentive scheme, an agent’s proximity to a goal affects only future payoffs in non-pecuniary benefit periods, providing exclusion restrictions on the current payoff.
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 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.
