Elea McDonnell Feit is an Assistant Professor of Marketing at Drexel University and a Senior Fellow of Marketing at The Wharton School. Her research focuses on leveraging customer data to make better product design and advertising decisions, particularly when data is incomplete, unmatched or aggregated. Much of her career has focused on developing new quantitative methods and bringing them into practice, first working in product design at General Motors, then commercializing new methods at the marketing analytics firm,The Modellers, and most recently as the Executive Director of the Wharton Customer Analytics Initiative, where she built the academic-industry partnership program. She brings a rich understanding of industry problems to her research, which has been published in Management Science and the Journal of Marketing Research. She enjoys making analytics and statistics accessible to a broad audience and has recently co-authored a book on R for Marketing Research and Analytics with Chris Chapman. She regularly teaches popular tutorials and workshops for practitioners on digital marketing, marketing experiments, marketing analytics in R, discrete choice modeling and hierarchical Bayes methods as well as undergraduate and MBA classes at Drexel and Wharton. She holds a PhD in Marketing from the University of Michigan, an MS in Industrial Engineering from Lehigh University and a BA in Mathematics from University of Pennsylvania.
Elea McDonnell Feit, John Helveston, Jeremy J. Michalek (2018), Pooling Stated and Revealed Preferences in the Presence of RP Endogeneity, Transportation Research.
Abstract: Advances in data collection have made it increasingly easy to collect information on advertising exposures. However, translating this seemingly rich data into measures of advertising response has proven difficult, largely because of concerns that advertisers target customers with a higher propensity to buy or increase advertising during periods of peak demand. We show how this problem can be addressed by studying a setting where a firm randomly held out customers from each campaign, creating a sequence of randomized field experiments that mitigates (many) potential endogeneity problems. Exploratory analysis of individual holdout experiments shows positive effects for both email and catalog; however, the estimated effect for any individual campaign is imprecise, because of the small size of the holdout. To pool data across campaigns, we develop a hierarchical Bayesian model for advertising response that allows us to account for individual differences in purchase propensity and marketing response. Building on the traditional ad-stock framework, we are able to estimate separate decay rates for each advertising medium, allowing us to predict channel-specific short- and long-term effects of advertising and use these predictions to inform marketing strategy. We find that catalogs have substantially longer-lasting impact on customer purchase than emails. We show how the model can be used to score and target individual customers based on their advertising responsiveness, and we find that targeting the most responsive customers increases the predicted returns on advertising by approximately 70% versus traditional recency, frequency, and monetary value–based targeting.
Abstract: Search engines record the queries that users submit, including a large number of queries that include brand names. This data holds promise for assessing brand health. However, before adopting brand search volume as a brand metric, marketers should understand how brand search relates to traditional survey-based measures of brand attitudes, which have been shown to be predictive of sales. We investigate the relationship between brand attitudes and search engine queries using a unique micro-level data set collected from a panel of Google users who agreed to allow us to track their individual brand search behavior over eight weeks and link this search history to their responses to a brand attitude survey. Focusing on the smartphone and automotive markets, we find that users who are actively shopping in a category are more likely to search for any brand. Further, as users move from being aware of a brand to intending to purchase a brand, they are increasingly more likely to search for that brand, with the greatest gains as customers go from recognition to familiarity and from familiarity to consideration. Additionally, users that own and use a particular automotive or smartphone brand are much more likely to search for that brand, even when they are not in market suggesting that a substantial volume of brand search in these categories is not related to shopping or product search. We discuss the implications of these findings for assessing brand health from search data.
Grace Haaf, W. Ross Morrow, Ines Alzevedo, Elea McDonnell Feit, Jeremy J. Michalek (2016), Forecasting light-duty vehicle demand using alternative-specific constants for endogeneity correction versus calibration,.
Abstract: We investigate parameter recovery and forecast accuracy implications of incorporating alternative-specific constants (ASCs) in the utility functions of vehicle choice models. We compare two methods of incorporating ASCs: (1) a maximum likelihood estimator that computes ASCs post-hoc as calibration constants (MLE-C) and (2) a generalized method of moments estimator that uses instrumental variables (GMM-IV) to correct for price endogeneity. In a synthetic study we observe significant coefficient bias with MLE-C when the price-ASC correlation (endogeneity) is large. GMM-IV successfully mitigates this bias given valid instruments but exacerbates the bias given invalid instruments. Despite greater coefficient bias, MLE-C yields better forecasts than GMM-IV with valid instruments in most of the cases examined, including most cases where the price-ASC correlation present in the estimation data is absent in the prediction data. In a market study of U.S. midsize sedan sales from 2002 – 2006 the GMM-IV model predicts the 1-year-forward market better, but the MLE-C model predicts the 5-year-forward market better. Including an ASC in predictions by any of the methods proposed improves share forecasts, and assuming that the ASC of each new vehicle matches that of its closest competitor vehicle yields the best long term forecasts. We find evidence that the instruments most frequently used in the automotive demand literature may be invalid.
John Helveston, Elea McDonnell Feit, Jeremy J. Michalek (2016), Will subsidies drive electric vehicle adoption? Measuring consumer preferences in the U.S. and China, Transportation Research , 73, pp. 96-112.
Abstract: We model consumer preferences for conventional, hybrid electric, plug-in hybrid electric (PHEV), and battery electric (BEV) vehicle technologies in China and the U.S. using data from choice-based conjoint surveys fielded in 2012–2013 in both countries. We find that with the combined bundle of attributes offered by vehicles available today, gasoline vehicles continue in both countries to be most attractive to consumers, and American respondents have significantly lower relative willingness-to-pay for BEV technology than Chinese respondents. While U.S. and Chinese subsidies are similar, favoring vehicles with larger battery packs, differences in consumer preferences lead to different outcomes. Our results suggest that with or without each country’s 2012–2013 subsidies, Chinese consumers are willing to adopt today’s BEVs and mid-range PHEVs at similar rates relative to their respective gasoline counterparts, whereas American consumers prefer low-range PHEVs despite subsidies. This implies potential for earlier BEV adoption in China, given adequate supply. While there are clear national security benefits for adoption of BEVs in China, the local and global social impact is unclear: With higher electricity generation emissions in China, a transition to BEVs may reduce oil consumption at the expense of increased air pollution and/or greenhouse gas emissions. On the other hand, demand from China could increase global incentives for electric vehicle technology development with the potential to reduce emissions in countries where electricity generation is associated with lower emissions.
Elea McDonnell Feit, Pengyuan Wang, Eric Bradlow, Peter Fader (2013), Fusing Aggregate and Disaggregate Data with an Application to Multiplatform Media Consumption, Journal of Marketing Research, 50, pp. 348-364.
Abstract: As firms collect greater amounts of data about their customers from an ever broader set of “touchpoints,” a new set of methodological challenges arises. Companies often collect data from these various platforms at differing levels of aggregation, and it is not clear how to merge these data sources to draw meaningful inferences about customer-level behavior patterns. In this article, the authors provide a method that firms can use, based on readily available data, to gauge and monitor multiplatform media usage. The key innovation in the method is a Bayesian data-fusion approach that enables researchers to combine individual-level usage data (readily available for most digital platforms) with aggregated data on usage over time (typically available for traditional platforms). This method enables the authors to disentangle the intraday correlations between platforms (i.e., the usage of one platform vs. another on a given day) from longer-term correlations across users (i.e., heavy/light usage of multiple platforms over time). The authors conclude with a discussion of how this method can be used in a variety of marketing contexts for which data have become readily available, such as gauging the interplay between online and brick-and-mortar purchasing behavior.
Dotson P. Jeffrey, Mark Beltramo, Elea McDonnell Feit, Randall C. Smith (Working), Controlling for styling and other "complex attributes" in a choice model.
Elea McDonnell Feit, Mark Beltramo, Fred Feinberg (2010), Reality Check: Combining survey and market data to estimate choice models, Management Science.
Elea occasionally teaches an elective course on A/B and multivariate testing called Business Experiments (MKTG309/809). This course is sponsored by the Wharton Customer Analtyics Initiative and focuses on developing concrete skills that student can use to provide immediate and measurable returns to digital and direct marketers.
Technology is opening new ways for companies in the travel and hospitality industries to engage with customers and raise revenues.Knowledge @ Wharton - 2014/08/19