The Comprehensive Effects of Sales Force Management: A Dynamic Structural Analysis of Selection, Compensation, and Training

Doug J. Chung; Byungyeon  Kim; Byoung G. Park

Article | Management Science | Forthcoming

The Comprehensive Effects of Sales Force Management: A Dynamic Structural Analysis of Selection, Compensation, and Training

by Doug J. Chung, Byungyeon Kim and Byoung G. Park

Abstract

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.

Keywords: Salesforce Management; Recruitment; Selection and Staffing; Compensation and Benefits; Resignation and Termination; Training; Behavior; Analysis;

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Citation:

Chung, Doug J., Byungyeon Kim, and Byoung G. Park. "The Comprehensive Effects of Sales Force Management: A Dynamic Structural Analysis of Selection, Compensation, and Training." Management Science (forthcoming).

Article | Marketing Science | July–August 2020

Price Bargaining and Competition in Online Platforms: An Empirical Analysis of the Daily Deal Market

Lingling Zhang and Doug J. Chung

The prevalence of online platforms opens new doors to traditional businesses for customer reach and revenue growth. This research investigates platform choice in a setting where prices are determined by negotiations between platforms and businesses. We compile a unique and comprehensive dataset from the U.S. daily deal market, where merchants offer deals to generate revenues and attract new customers. We specify and estimate a two-stage supply-side model in which platforms and merchants bargain on the wholesale price of deals. Based on Nash bargaining solutions, our model generates insights into how bargaining and competition jointly determine the price and firm profits. Our results show that there is systematic variation in bargaining power between platforms and merchants with different characteristics. By working with a bigger platform, merchants enjoy a larger customer base, but they are subject to lower margins due to less bargaining power during negotiations. Our counterfactual results quantify the value of platforms for merchants and show that merchants with lower bargaining power benefit more by working with a smaller platform. Regarding the impact of price negotiations on platform competition, we estimate that roughly 11% of the deals on a smaller platform are attributable to the platform’s lower level of revenue sharing.

Keywords: business-to-business marketing; platform competition; Two-Sided Markets; price bargaining; daily deals; structural model; Two-Sided Platforms; Competition; Price; Negotiation;

Citation:

Zhang, Lingling, and Doug J. Chung. "Price Bargaining and Competition in Online Platforms: An Empirical Analysis of the Daily Deal Market." Marketing Science 39, no. 4 (July–August 2020): 687–706. View Details

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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.

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The Comprehensive Effects of Sales Force Management: A Dynamic Structural Analysis of Selection, Compensation, and Training

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