Publication Date: September 2019
We develop the ﬁrst structural model of a multitasking salesforce to address questions of job design and incentive compensation design. The model incorporates three novel features: (i) multitasking eﬀort choice given a multidimensional incentive plan; (ii) salesperson’s private information about customers and (iii) dynamic intertemporal tradeoﬀs in eﬀort choice across the tasks. The empirical application uses data from a micro nance bank where loan oﬀicers are jointly responsible and incentivized for both loan acquisition repayment but has broad relevance for salesforce management in CRM settings involving customer acquisition and retention. We extend two-step estimation methods used for unidimensional compensation plans for the multitasking model with private information and intertemporal incentives by combining flexible machine learning (random forest) for the inference of private information and the ﬁrst-stage multitasking policy function estimation. Estimates reveal two latent segments of salespeople-a “hunter” segment that is more eﬀicient in loan acquisition and a “farmer” segment that is more eﬀicient in loan collection. We use counterfactuals to assess how (1) multi-tasking versus specialization in job design; (ii) performance combination across tasks (multiplicative versus additive); and (iii) job transfers that impact private information impact ﬁrm proﬁts and speciﬁc segment behaviors.
Keywords: Salesforce compensation, Multitasking, Multi-dimensional incentives, Private information, Adverse selection, Moral hazard
JEL Classification Codes: C61, J33, L11, L23, L14, M31, M52, M55