About

Sharad Borle has been a marketing professor at Rice University since 2003. He holds an M.S. and Ph.D. in marketing from Carnegie Mellon University, an MBA from XLRI Institute of Management in India, and a B.Tech in electronics engineering from Banaras Hindu University in India. Prior to his academic career, Professor Borle worked in India in the Department for International Development, Suzuki Motor Corporation, and Network Limited. His teaching interests include Marketing Research, Marketing Strategy, Data Analysis, and Marketing Models, with a focus on Bayesian applications and quantitative models of consumer behavior. His research contributions encompass a range of topics such as count data modeling, customer community participation, customer lifetime value estimation, and online auction categories, with multiple publications in reputable journals.

Research topics

• Business
• Economics
• Marketing
• Social psychology
• Psychology
• Commerce
• Industrial organization
• Microeconomics

Selected publications

• An empirical investigation of unique traits of retailing industry in emerging economies: The perspective of consumer-packaged goods manufacturers

  Journal of Business Research · 2022 · 6 citations

  • Business
  • Commerce
  • Industrial organization
  PublisherOA PDFDOI

• Online Training of Salespeople: Impact, Heterogeneity, and Spillover Effects

  Journal of Marketing Research · 2021 · 25 citations

  Senior authorCorresponding
  • Marketing
  • Business
  • Psychology

  The authors use data from a field study concerning an online salesperson training program to investigate (1) the overall impact of program participation on sales performance for two kinds of products, “Focus” and “Other” (the direct impact); (2) heterogeneity in the impact of program participation across salespeople; and (3) spillover effect of program participation by others in the vicinity on salesperson performance (the indirect impact). The program contains short-duration training modules accessed via an online platform. Salespeople choose whether to take any module, how many modules to take, and when to take them. Results show that although training improved sales performance, the average impact of training on Other product sales was immediate, significant, and positive, and that on Focus product sales was delayed. Further, the impact of training diminishes over time. The authors find significant heterogeneity in the impact of training across salespeople and regions. Finally, the results show a mixed spillover effect of training by peers. There is a positive spillover effect on sales of the focal salesperson with an increase in the total number of trainings taken by peer salespeople, and a negative or no spillover effect with an increase in the number of peer salespeople taking training.

  PublisherDOI

• Note of correction: “Conjugate Analysis of the Conway-Maxwell-Poisson Distribution”

  Bayesian Analysis · 2018-07-30

  articleOpen access
  PublisherDOI

• Computing with the COM-Poisson distribution

  2018-06-30 · 56 citations

  articleOpen access

  The Conway-Maxwell-Poisson (COM-Poisson) is a generalization of the Poisson distribution which can model both under-dispersed and over-dispersed data. However, the distribution, moments, and MLE cannot be computed in closed form. This paper describes computational schemes and handy approximations for the COM-Poisson.

  PublisherOA PDFDOI

• Using Computational and Mathematical Methods to Explore a New Distribution: The ν-Poisson

  Figshare · 2018-06-30

  articleOpen access

  A new distribution (the v-Poisson) and its conjugate density are introduced and explored using computational and mathematical methods. The v-Poisson is a two-parameter extension of the Poisson distribution that generalizes some well-known discrete distributions (Poisson, Bernoulli, Geometric). It also leads to the generalization of distributions derived from these discrete distributions (viz. the Binomial and Negative Binomial). We use mathematics as far as we can and then employ computational and graphical methods to explore the distribution and its conjugate density further. Three methods are presented for estimating the v-Poisson parameters: The first is a fast simple weighted least squares method, which leads to estimates that are sufficiently accurate for practical purposes. The second method of maximum likelihood can be used to refine the initial estimates. This method requires iterations and is more computationally intensive. The third estimation method is Bayesian. Using the conjugate prior, the posterior density of the v-Poisson parameters is easily computed. We derive the necessary and sufficient condition for the conjugate family to be proper. The v-Poisson is a flexible distribution that can account for over/under dispersion commonly encountered in count data. We also explore an empirical application demonstrating this flexibility of the v-Poisson to fit count data which does not seem to follow the Poisson distribution.

  OA PDFDOI

• The shape of Word-of-Mouth response function

  Technological Forecasting and Social Change · 2017-10-12 · 6 citations

  articleSenior author
  PublisherDOI

• A MCMC approach for modeling customer lifetime behavior using the COM‐Poisson distribution

  Applied Stochastic Models in Business and Industry · 2017-09-29 · 13 citations

  article

  One of the major challenges associated with the measurement of customer lifetime value is selecting an appropriate model for predicting customer future transactions. Among such models, the Pareto/negative binomial distribution (Pareto/NBD) is the most prevalent in noncontractual relationships characterized by latent customer defections; ie, defections are not observed by the firm when they happen. However, this model and its applications have some shortcomings. Firstly, a methodological shortcoming is that the Pareto/NBD, like all lifetime transaction models based on statistical distributions, assumes that the number of transactions by a customer follows a Poisson distribution. However, many applications have an empirical distribution that does not fit a Poisson model. Secondly, a computational concern is that the implementation of Pareto/NBD model presents some estimation challenges specifically related to the numerous evaluation of the Gaussian hypergeometric function. Finally, the model provides 4 parameters as output, which is insufficient to link the individual purchasing behavior to socio‐demographic information and to predict the behavior of new customers. In this paper, we model a customer's lifetime transactions using the Conway‐Maxwell‐Poisson distribution, which is a generalization of the Poisson distribution, offering more flexibility and a better fit to real‐world discrete data. To estimate parameters, we propose a Markov chain Monte Carlo algorithm, which is easy to implement. Use of this Bayesian paradigm provides individual customer estimates, which help link purchase behavior to socio‐demographic characteristics and an opportunity to target individual customers.

  PublisherDOI

• A Review of Choice Modeling in the Marketing-Operations Management Interface

  SSRN Electronic Journal · 2017-01-01 · 6 citations

  reviewOpen access
  PublisherDOI

• Estimating the Contextual Risk of Data Breach: An Empirical Approach

  Journal of Management Information Systems · 2015-04-03 · 211 citations

  articleSenior author

  Data breach incidents are on the rise, and have resulted in severe financial and legal implications for the affected organizations. We apply the opportunity theory of crime, the institutional anomie theory, and institutional theory to identify factors that could increase or decrease the contextual risk of data breach. We investigate the risk of data breach in the context of an organization’s physical location, its primary industry, and the type of data breach that it may have suffered in the past. Given the location of an organization, the study finds support for application of the opportunity theory of crime and the institutional anomie theory in estimating the risk of data breach incidents within a state. In the context of the primary industry in which an organization operates, we find support for the institutional theory and the opportunity theory of crime in estimating risk of data breach incidents within an industry. Interestingly though, support for the opportunity theory of crime is partial. We find that investment in information technology (IT) security corresponds to a higher risk of data breach incidents within both a state and an industry, a result contrary to the one predicted by the opportunity theory of crime. A possible explanation for the contradiction is that investments in IT security are not being spent on the right kind of data security controls, a fact supported by evidence from the industry. The work has theoretical and practical implications. Theories from criminology are used to identify the risk factors of data breach incidents and the magnitude of their impact on the risk of data breach. Insights from the study can help IT security practitioners to assess the risk environment of their firm (in terms of data breaches) based on the firm’s location, its industry sector, and the kind of breaches that the firm may typically be prone to.

  PublisherDOI

• Does Membership in a Nominal Online Group Affect Long-Term Customer Behaviors? Results from a Naturalistic Field Experiment on Kiva.org

  SSRN Electronic Journal · 2015-01-01

  articleOpen accessSenior author
  PublisherDOI

Frequent coauthors

• Siddharth S. Singh

  Ambedkar University Delhi

  15 shared

• Peter Boatwright

  Carnegie Mellon University

  11 shared

• Joseph B. Kadane

  Carnegie Mellon University

  10 shared

• Utpal M. Dholakia

  Rice University

  8 shared

• Galit Shmueli

  8 shared

• Dipak C. Jain

  China Europe International Business School

  6 shared

• Thomas P. Minka

  Baker Engineering (United States)

  6 shared

• Ravi Sen

  3 shared