Joint Work with Brett Gordon, Mitchell J. Lovett, and Bowen Luo
Management Science, 2022
Joint Work with Paul B. Ellickson and Wreetabrata Kar
Marketing Science, 2022
Winner - Guy O. and Rosa Lee Mabry Best Paper Award
Joint Work with Wreetabrata Kar and Gary L. Lilien
2nd Round Revise and Resubmit at Journal of Marketing Research
Most B2B buyer-seller relationships involve a connection between a sales rep and a customer. If the relationship is suboptimal, the selling firm can move customers from one sales rep to another, a process we call proactive reassignment. Historically, geographic considerations severely constrained feasible reassignments. Technological advancements have made virtual connections between a customer and a rep the norm, removing geographic constraints on proactive reassignment. Using data from a quasi-field experiment in a partner seller firm and machine learning methodology, we develop a decision support system (DSS) to guide sales managers in revenue-enhancing customer-rep reassignments. We find that reassigning a customer to a long-standing rep does not significantly affect post-period sales, but reassigning to a new rep typically leads to a decline in sales. Significantly, our methodology identifies situations when proactive replacement, even with new reps, can be beneficial. For instance, customers with past relationship disruptions perform better when reassigned to new reps than to other, existing reps. Integrating these findings into the DSS, we identify numerous situations where future proactive reassignments can increase the focal firm’s revenue. Our counterfactual analysis shows a potential 14% improvement in post-period revenue compared to the selling firm's actual actions.
Joint Work with Daniel Kebede
Revise and Resubmit at Journal of Marketing
This paper examines the extent that political polarization moderates the effectiveness of promotions in driving public policy outcomes. Specifically, heterogeneous effects of COVID-19 vaccination promotions on county-level vaccination rates are studied to quantify the effect. A central challenge of this work is both accounting for the endogenous action of state-level officials and modeling the underlying diffusion pattern associated with vaccination rates. Blending the utility-based Bass Diffusion model of Cosguner and Seetharaman (2022) with counterfactual estimation techniques and machine learning solves the aforementioned challenges. Though other studies show a positive effect of promotions on vaccinations, this paper finds the opposite. Studying the estimated heterogeneous effects show that the underlying negative response is driven by vaccination hesitation and political ideology causing a backfire effect, which enhances an individual's resistance to vaccination. For example, counterfactual analysis shows that in regions where the promotions had a negative impact, the result is a 1.28% reduction in the vaccination rate per period. The net impact of these broad vaccination promotions is approximately 195,000 individuals delayed or forgo vaccination altogether. This paper shows that a backfire effect can manifest even with small incentives in a politically charged environment.
Joint Work with Arie Beresteanu, Paul B. Ellickson, and Sanjog Misra
Revise and Resubmit at Management Science
This paper empirically examines competition between supermarkets, treating it as a dynamic discrete game between heterogeneous firms. We focus on the overall impact of Wal-Mart's entry on incumbent supermarket firms, quantifying the effects on prices, producer surplus, consumer welfare and overall competitive structure. Employing a thirteen-year panel dataset of store level observations that includes every supermarket firm operating in the United States across a large sample of geographic markets, alongside the rapid proliferation of Wal-Mart Supercenters, we propose and estimate a dynamic structural model of chain level competition. In this model, incumbent firms decide each period whether to add or subtract stores or exit the market entirely, and potential entrants choose whether or not to enter. Product market competition is captured via a discrete-choice demand system, incorporating detailed information on prices and characteristics of chains, as well as unobserved heterogeneity in chain-level quality. Our estimation approach combines two-step estimation techniques with a novel random forest based value function approximation technique that can accommodate the high-dimensional structure of the state space.
Joint Work with Paul B. Ellickson, Wreetabrata Kar, and Guang Zeng
Under Review
Our study demonstrates the power of contextual embeddings for predicting the performance of novel heterogeneous treatments. Our proposed framework leverages four key benefits of machine learning: prediction, enhanced causal estimation, estimation of heterogeneous effects, and generative capability. To test our framework, we exploit a targeted marketing setting in which 34 email promotions were sent to 1.3 million customers over a 45-day period. Using these emails as treatments, we start by estimating the doubly robust scores of customer-level purchase amounts to serve as our target variable. We incorporate customer-level demographics and contextual embeddings, which capture the context of the latent states, to estimate the response function of these emails. Using a series of leave-one-out exercises, we show how our approach can accurately extrapolate the average performance, heterogeneous performance, and recommended targeting policies of novel promotions. We find that our framework recovers 78.6% of the variation of the aggregate treatment effects, an average of 65.36% of the variation in the heterogeneous treatment effects of each novel treatment, and matches 82% of policy recommendations made using the true signals. We conclude our study with an example of leveraging Generative AI to create novel treatments and then evaluate their performance with our framework.
Joint Work with Mohammad S. Rahman
Under Review
The digital revolution has transformed the way we interact with each other. It is unknown how effective these digital interactions are compared to in-person interaction within a high-touch environment. This study delves into this question by comparing the relative value of in-person and virtual interactions (e.g., WebEx, Facetime) in the context of direct sales. Using a pseudo-random experiment with thousands of clients overseen by agents from a leading medical device firm, this paper explores the effects of switching from in-person visits (IPV) to a combination of remote visits (RV) and IPV. The findings reveal that, on average, IPV is nearly one-and-a-half times more valuable than RV. By leveraging machine learning techniques, this study uncovers factors that can either widen or narrow the gap between IPV and RV. For example, clients with stronger ties to the company are less responsive to RV, thus increasing the gap. Conversely, clients in technology-friendly regions are more responsive to RV, closing the gap to near parity with IPV. This study provides valuable insights for businesses looking to optimize their sales strategies in the digital age.
Joint Work with Nawar N. Chaker and Johannes Habel
Under Review
Our study examines whether unstructured data in customer relationship management (CRM) software can enhance sales forecasting. While unstructured CRM data provides useful information for managers to review salesperson performance, it is unclear whether and when such data can predict changes in sales revenue with customers. By leveraging advances in machine learning, we seek an answer to this question by combining Generative AI (GenAI) with large language model fine-tuning. Specifically, we construct a measure of positive sales change by scoring over 180,000 sales activity logs associated with 11,201 customers served by a medical device manufacturer. We find that our constructed measure predicts a statistically significant growth in sales revenue; the effect remains stable through a battery of different specifications and robustness tests. We test a series of moderators of this effect by using variables grounded in information processing theory. For example, our measure is only predictive of changes in sales revenue for outside (vs. inside) salespeople or for salespeople operating in smaller territories. Our study contributes to the broader literature on leveraging unstructured text and helping managers exploit internal information to better understand changes in customer outcomes.
Joint Work with Candace Jens and T. Beau Page
Causal forest is part of a growing class of doubly-robust machine learning based estimators that non-parametrically recovers heterogeneity in treatment effects. However, causal forest's usefulness is currently limited because the group-level heterogeneity present in many economics settings violates a key assumption of causal forest. We provide a solution: estimate group-level fixed effects in a regression, create a vector of estimated group-level coefficients, and include this vector in the casual forest estimation. We use a series of Monte Carlo experiments and two applications to demonstrate our solution's success and the shortcomings of alternatives. We compare techniques' recovery of both average and heterogeneity in treatment effects. Examining bias in observation-level effects is important because a key benefit of forests is their ability to recover heterogeneity. We show that a key driver of differences between techniques' performances is the number of groups relative to sample size. In simulated settings with fewer, larger groups, several alternative techniques (e.g., binary group-level fixed effects and a vector of group-level average outcome) recover relatively unbiased average effects. However, in no scenario do these alternatives recover average effects with lower bias than our method. Moreover, these alternatives struggle to capture heterogeneity in effects, recovering more biased observation-level treatment effects than our solution across all simulated settings. We confirm our Monte Carlo results that show the effectiveness of our solution in two applications, one with randomized treatment and the other using observational data. Our study greatly increases the number of settings in which unbiased, heterogeneous treatment effects are recoverable.
Joint Work with Paul Ellickson and Wreetabrata Kar
Finalizing Estimation and Preparing Manuscript
Joint Work with Guang Zeng
Estimation in Progress
Joint Work with Majedeh Esmizadeh
Estimation Finalization and Preparing Manuscript
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Updated July 2024
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