A Structural Model of a Multitasking Salesforce: Multidimensional Incentives and Plan Design

The paper broadens the focus of empirical research on salesforce management to include multitasking settings with multidimensional incentives, where salespeople have private information about customers. This allows us to ask novel substantive questions around multidimensional incentive design and job design while managing the costs and benefits of private information. To this end, the paper introduces the first structural model of a multitasking salesforce in response to multidimensional incentives. The model also accommodates (i) dynamic intertemporal tradeoffs in effort choice across the tasks and (ii) salesperson’s private information about customers. We apply our model in a rich empirical setting in microfinance and illustrate how to address various identification and estimation challenges. We extend two-step estimation methods used for unidimensional compensation plans by embedding a flexible machine learning (random forest) model in the first-stage multitasking policy function estimation within an iterative procedure that accounts for salesperson heterogeneity and private information. 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. Counterfactuals reveal heterogeneous effects: hunters’ private information hurts the firm as they engage in adverse selection; farmers’ private information helps the firm as they use it to better collect loans. The payoff complementarity induced by multiplicative incentive aggregation softens adverse specialization by hunters relative to additive aggregation, but hurts performance among farmers. Overall, task specialization in job design for hunters (acquisition) and farmers (collection) hurts the firm as adverse selection harm overwhelms efficiency gain

A Structural Model of a Multitasking Salesforce: Multidimensional Incentives and Plan Design

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

Multidimensional Sales Incentives in CRM Settings: Customer Adverse Selection and Moral Hazard

In many firms, incentivized salespeople with private information about their customers are responsible for customer relationship management (CRM). Private information can help the firm by increasing sales efficiency, but it can also hurt the firm if salespeople use it to maximize own compensation at the expense of the firm. Specifically, we consider two negative outcomes due to private information — ex-ante customer adverse selection at the time of acquisition and ex-post customer moral hazard after acquisition. This paper investigates potential positive and negative responses of a salesforce to managerial levers — multidimensional incentives for acquisition and retention performance and job transfers that affect the level of private information. Salespeople are responsible for managing customer relationships and compensated through multidimensional performance incentives for customer acquisition and maintenance at many firms. This paper investigates how a salesperson’s private information on customers affect their response to multiple dimensions of incentives. Using unique matched panel data that links individual salesperson performance metrics with customer level loans and repayments from a microfinance bank, we find that sales people indeed possess private information that is not available to the firm. Salespeople use the private information to engage in adverse selection of customers in response to acquisition incentives. Customer maintenance incentives serve a dual purpose; they not only reduce loan defaults, but also moderate adverse selection in customer acquisition. Transfers that eliminate private information reduces the adverse selection effects of acquisition incentives, but increase loan defaults — customer moral hazard. Despite the potential negative adverse selection effects due to private information, the effort increasing effect of each of the three dimensions of sales management we investigate — acquisition incentive, maintenance incentive and transfers all have a net positive effect on firm value. Methodologically, the paper introduces an identification strategy to separate customer adverse selection and customer moral hazard (loan repayment), by leveraging the multidimensional incentives of an intermediary (salesperson) responsible for both customer selection and repayment with private information about customers

When Salespeople Manage Customer Relationships: Multidimensional Incentives and Private Information

At many firms, incentivized salespeople with private information about customers are responsible for CRM. While incentives motivate sales performance, private information can induce moral hazard by salespeople to gain compensation at the expense of the firm. We investigate the sales performance–moral hazard tradeoff in response to multidimensional performance (acquisition and maintenance) incentives in the presence of private information. Using unique panel data on customer loan acquisition and repayments linked to salespeople from a microfinance bank, we detect evidence of salesperson private information. Acquisition incentives induce salesperson moral hazard leading to adverse customer selection, but maintenance incentives moderate it as salespeople recognize the negative effects of acquiring low-quality customers on future payoffs. Critically, without the moderating effect of maintenance incentives, adverse selection effect of acquisition incentives overwhelms the sales enhancing effects, clarifying the importance of multidimensional incentives for CRM. Reducing private information (through job transfers) hurts customer maintenance, but has greater impact on productivity by moderating adverse selection at acquisition. The paper also contributes to the recent literature on detecting and disentangling customer adverse selection and customer moral hazard (defaults) with a new identification strategy that exploits the time-varying effects of salesperson incentives

Energy response of X-rays under high flux conditions using a thin APD for the energy range of 6–33 keV

This paper reports on the demonstration of a high-rate energy measurement technique using a thin depletion layer silicon avalanche photodiode (Si-APD). A dedicated amplitude-to-time converter is developed to realize simultaneous energy and timing measurement in a high rate condition. The energy response of the system is systematically studied by using monochromatic X-ray beam with an incident energy ranging from 6 to 33 keV. The obtained energy spectra contain clear peaks and tail distributions. The peak fraction monotonously decreases as the incident photon energy increases. This phenomenon can be explained by considering the distribution of the energy deposit in silicon, which is investigated by using a Monte Carlo simulation

SiPM module for the ACME III electron EDM search

This report shows the design and the performance of a large area Silicon Photomultiplier (SiPM) module developed detection of fluorescent light emitted from a 10 cm scale volume. The module was optimized for the planned ACME III electron electric dipole moment (eEDM) search, which will be a powerful probe for the existence of physics beyond the Standard Model of particle physics. The ACME experiment searched for the eEDM with the world's highest sensitivity using cold ThO polar molecules (ACME II). In ACME III, SiPMs will be used for detection of fluorescent photons (the fundamental signal of the experiment) instead of PMTs, which were used in the previous measurement. We have developed an optimized SiPM module, based on a 16-channel SiPM array. Key operational parameters are characterized, including gain and noise. The SiPM dark count rate, background light sensitivity, and optical crosstalk are found to all be well suppressed and more than sufficient for the ACME III application.Comment: 10 pages, 6 figures, proceedings for NDIP2

A scalable quantum computer with an ultranarrow optical transition of ultracold neutral atoms in an optical lattice

We propose a new quantum-computing scheme using ultracold neutral ytterbium atoms in an optical lattice. The nuclear Zeeman sublevels define a qubit. This choice avoids the natural phase evolution due to the magnetic dipole interaction between qubits. The Zeeman sublevels with large magnetic moments in the long-lived metastable state are also exploited to address individual atoms and to construct a controlled-multiqubit gate. Estimated parameters required for this scheme show that this proposal is scalable and experimentally feasible.Comment: 6 pages, 6 figure

Ice Nucleating Particle Connections to Regional Argentinian Land Surface Emissions and Weather During the Cloud, Aerosol, and Complex Terrain Interactions Experiment

Here, we present a multi-season study of ice-nucleating particles (INPs) active via the immersion freezing mechanism, which took place in north-central Argentina, a worldwide hotspot for mesoscale convective storms. INPs were measured untreated, after heating to 95°C, and after hydrogen peroxide digestion. No seasonal cycle of INP concentrations was observed. Heat labile INPs, which we define as “biological” herein, dominated the population active at −5 to −20°C, while non-heat-labile organic INPs (decomposed by peroxide) dominated at lower temperatures, from −20 to −28°C. Inorganic INPs (remaining after peroxide digestion), were minor contributors to the overall INP activity. Biological INP concentration active around −12°C peaked during rain events and under high relative humidity, reflecting emission mechanisms independent of the background aerosol concentration. The ratio of non-heat-labile organic and inorganic INPs was generally constant, suggesting they originated from the same source, presumably from regional arable topsoil based on air mass histories. Single particle mass spectrometry showed that soil particles aerosolized from a regionally common agricultural topsoil contained known mineral INP sources (K-feldspar and illite) as well as a significant organic component. The INP activity observed in this study correlates well with agricultural soil INP activities from this and other regions of the world, suggesting that the observed INP spectra might be typical of many arable landscapes. These results demonstrate the strong influence of regional continental landscapes, emitting INPs of types that are not yet well represented in global models