Abstract
We estimate a dynamic structural model of sales force response to a bonus-based compensation plan. This paper provides substantive insight into how different elements of the compensation plan enhance productivity. We find evidence that (1) bonuses enhance productivity across all segments; (2) overachievement commissions help sustain the high productivity of the best performers, even after attaining quotas; and (3) quarterly bonuses help improve performance of the weak performers by serving as pacers to keep the sales force on track in achieving its annual sales quotas. The paper also introduces two main methodological innovations to the marketing literature: First, we implement empirically the method proposed by Arcidiacono and Miller [Arcidiacono P, Miller RA (2011) Conditional choice probability estimation of dynamic discrete choice models with unobserved heterogeneity. Econometrica 79(6):1823–1867] to accommodate unobserved latent-class heterogeneity using a computationally light two-step estimator. Second, we illustrate how discount factors can be estimated in a dynamic structural model using field data through a combination of (1) an exclusion restriction separating current and future payoff and (2) a finite-horizon model in which there is no forward-looking behavior in the last period.
Highlights
Personal selling is one of the most important elements of the marketing mix, especially in the context of B2B firms
We find that only the interaction term between lagged annual quota and indirect sales
This paper develops and estimates a dynamic structural model of sales force response to a compensation plan with various components: salary, commissions, lump-sum bonus for achieving quotas, and different commission rates beyond achieving quotas
Summary
Personal selling is one of the most important elements of the marketing mix, especially in the context of B2B firms. Marketing researchers routinely create response models for marketing mix instruments such as price, sales promotion and advertising. Meta-analysis of various research studies estimate that the sales force expenditure elasticity is about 0.34 (Albers et al, 2010), relative to about 0.22 for advertising (Assmus, Farley and Lehmann 1983) and about -2.62 for price (Bijmolt et al 2005). While relative sales force expenditure elasticity is useful in determining the relative effectiveness of different instruments in the marketing mix, they give us little insight on how to design a sales force compensation plan, which is widely understood to be the primary tool by which firms can induce the sales force to exert the optimal levels of effort and to optimize the use of sales force expenditures
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