Eric M. Schwartz

What Drives Immediate and Ongoing Word of Mouth

Word of mouth (WOM) affects diffusion and sales, but why are certain products talked about more than others, both right after consumers first experience them and in the months that follow? This article examines psychological drivers of immediate and ongoing WOM. The authors analyze a unique data set of everyday conversations for more than 300 products and conduct both a large field experiment across various cities and a controlled laboratory experiment with real conversations. The results indicate that more interesting products get more immediate WOM but, contrary to intuition, do not receive more ongoing WOM over multiple months or overall. In contrast, products that are cued more by the environment or are more publicly visible receive more WOM both right away and over time. Additional analyses demonstrate which promotional giveaways in WOM marketing campaigns are associated with increased WOM. overall, the findings shed light on psychological drivers of WOM and provide insight into designing more effective WOM campaigns.

Model Selection Using Database Characteristics: Developing a Classification Tree for Longitudinal Incidence Data

When managers and researchers encounter a data set, they typically ask two key questions: 1 Which model from a candidate set should I use? And 2 if I use a particular model, when is it going to likely work well for my business goal? This research addresses those two questions and provides a rule, i.e., a decision tree, for data analysts to portend the “winning model” before having to fit any of them for longitudinal incidence data. We characterize data sets based on managerially relevant and easy-to-compute summary statistics, and we use classification techniques from machine learning to provide a decision tree that recommends when to use which model. By doing the “legwork” of obtaining this decision tree for model selection, we provide a time-saving tool to analysts. We illustrate this method for a common marketing problem i.e., forecasting repeat purchasing incidence for a cohort of new customers and demonstrate the methods ability to discriminate among an integrated family of a hidden Markov model HMM and its constrained variants. We observe a strong ability for data set characteristics to guide the choice of the most appropriate model, and we observe that some model features e.g., the “back-and-forth” migration between latent states are more important to accommodate than are others e.g., the inclusion of an “off” state with no activity. We also demonstrate the methods broad potential by providing a general “recipe” for researchers to replicate this kind of model classification task in other managerial contexts outside of repeat purchasing incidence data and the HMM framework.

‘Children of the HMM’: Modeling Longitudinal Customer Behavior at Hulu.Com

Stand-alone marketing models are well-suited to deal with different behavioral features such as variation in transaction frequency (customer heterogeneity with latent classes), recency and attrition (“buy ‘till you die” models), and more general changes in customer transaction rates (hidden Markov models, HMMs). We unite these modeling approaches in a integrative framework as special cases or “children” of the HMM. We then selectively constrain the general model to assess the impact of each component on model performance. Instead of selecting latent-state models primarily using likelihood-based criteria, we favor a multi-faceted empirical evaluation using summaries of posterior predictive distributions; thus focusing model checking on managerially relevant features of the data, such as reach, frequency, and “streakiness.”We apply our methods to daily viewing incidence data from Hulu.com, a leading U.S. online streaming video provider. We find that increasing model complexity can improve some aspects of model performance (as expected) but worsen others in non-obvious ways. For instance, only models allowing back-and-forth movements among latent states can capture streakiness (a pattern of growing importance given the increasing availability of digital media data); but as a trade-off, these models still perform worse than their simpler counterparts in both forecasting and capturing other audience measurement criteria. Finally, using machine-learning classification techniques, customers are grouped based on similar model fit and features of their past consumption patterns. This allows researchers and managers to portend the “winning model” prior to having to fit the models for all customers. We discuss the generality of the methods and findings for different mixes of patterns of customer behavior.

A Data Science Approach to Understanding Residential Water Contamination in Flint

When the residents of Flint learned that lead had contaminated their water system, the local government made water-testing kits available to them free of charge. The city government published the results of these tests, creating a valuable dataset that is key to understanding the causes and extent of the lead contamination event in Flint. This is the nations largest dataset on lead in a municipal water system. In this paper, we predict the lead contamination for each households water supply, and we study several related aspects of Flints water troubles, many of which generalize well beyond this one city. For example, we show that elevated lead risks can be (weakly) predicted from observable home attributes. Then we explore the factors associated with elevated lead. These risk assessments were developed in part via a crowd sourced prediction challenge at the University of Michigan. To inform Flint residents of these assessments, they have been incorporated into a web and mobile application funded by Google.org. We also explore questions of self-selection in the residential testing program, examining which factors are linked to when and how frequently residents voluntarily sample their water.