21.17 Principal-agent Models and Salesforce Compensation

21.17.1 Gerstner and Hess (1987)

21.17.2 Basu et al. (1985)

21.17.3 Raju and Srinivasan (1996)

Compare to (Basu et al. 1985), basic quota plan is superior in terms of implementation

Different from (Basu et al. 1985), basic quota plan has

  1. Shape-induced nonoptimality: not a general curvilinear form
  2. Heterogeneity-induced nonoptimality: common rate across salesforce

However, only 1% of cases in simulation shows up with nonoptimality. Hence, minimal loss in optimality

Basic quota plan is a also robust against changes in

  • salesperson switching territory

  • territorial changes (e.g., business condition)

Heterogeneity stems from

  • Salesperson: effectiveness, risk level, disutility for effort, and alternative opportunity

  • Territory: Sales potential and volatility

Adjusting quotas can accommodate the heterogeneity

To assess nonoptimality, following Basu and Kalyanaram (1990)

Moral hazard: cannot assess salesperson’s true effort.

Assumptions:

  • The salesperson reacts to the compensation scheme by deciding on an effort level that maximizes his overall utility, i.e., the expected utility from the (stochastic) compensation minus the effort distuility.

  • Firm wants to maximize its profit

  • compensation is greater than saleperson’s alternative.

  • Dollar sales \(x_i \sim Gamma\) (because sales are non-negative and standard deviation getting proportionately larger as the mean increases) with density \(f_i(x_i|t_i)\)

Expected sales per period

\[ E[x_i |t_i] = h_i + k_i t_i , (h_i > 0, k_i >0) \]

where

  • \(h_i\) = base sales level
  • \(k_i\) = effectiveness of effort

and \(1/\sqrt{c}\) = uncertainty in sales (coefficient of variation) = standard deviation / mean where \(c \to \infty\) means perfect certainty

salesperson’s overall utility

\[ U_i[s_i(x_i)] - V_i(t_i) = \frac{1}{\delta_i}[s_i (x_i)]^{\delta_i} - d_i t_i^{\gamma_i} \] where

  • \(0 < \delta_i <1\) (greater \(\delta\) means less risk-averse salesperson)
  • \(\gamma_i >1\) (greater \(\gamma\) means more effort)
  • \(V_i(t_i) = d_i t_i^{\gamma_i}\) is the increasing disutility function (convex)

21.17.4 Lal and Staelin (1986)

  • A menu of compensation plans (salesperson can select, which depends on their own perspective)

  • Proposes conditions when it’s optimal to offer a menu

  • Under (Basu et al. 1985), they assume

    • Salespeople have identical risk characteristics

    • identical reservation utility

    • identical information about the environment

  • When this paper relaxes these assumptions, menu of contract makes sense

  • If you cannot distinguish (or have a selection mechanisms) between high performer and low performer, a menu is recommended. but if you can, you only need 1 contract like (Basu et al. 1985)

21.17.5 Simester and Zhang (2010)

References

Basu, Amiya K., and Gurumurthy Kalyanaram. 1990. “On the Relative Performance of Linear Versus Nonlinear Compensation Plans.” International Journal of Research in Marketing 7 (2-3): 171–78. https://doi.org/10.1016/0167-8116(90)90019-j.
Basu, Amiya K., Rajiv Lal, V. Srinivasan, and Richard Staelin. 1985. “Salesforce Compensation Plans: An Agency Theoretic Perspective.” Marketing Science 4 (4): 267–91. https://doi.org/10.1287/mksc.4.4.267.
Gerstner, Eitan, and James D. Hess. 1987. “Why Do Hot Dogs Come in Packs of 10 and Buns in 8s or 12s? A Demand-Side Investigation.” The Journal of Business 60 (4): 491. https://doi.org/10.1086/296410.
Lal, Rajiv, and Richard Staelin. 1986. “Salesforce Compensation Plans in Environments with Asymmetric Information.” Marketing Science 5 (3): 179–98. https://doi.org/10.1287/mksc.5.3.179.
Raju, Jagmohan S., and V. Srinivasan. 1996. “Quota-Based Compensation Plans for Multiterritory Heterogeneous Salesforces.” Management Science 42 (10): 1454–62. https://doi.org/10.1287/mnsc.42.10.1454.
Simester, Duncan, and Juanjuan Zhang. 2010. “Why Are Bad Products So Hard to Kill?” Management Science 56 (7): 1161–79. https://doi.org/10.1287/mnsc.1100.1169.