Elea McDonnell Feit

• Elea McDonnell Feit, John Helveston, Jeremy J. Michalek (2018), Pooling Stated and Revealed Preferences in the Presence of RP Endogeneity, Transportation Research.
• Daniel Zantedeschi, Elea McDonnell Feit, Eric Bradlow (2017), Measuring Multi-Channel Advertising Response, Management Science, 63 (8), pp. 2706-2708. 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.

• Jeffrey Dotson, Ruixue Rachel Fan, Elea McDonnell Feit, Jeffrey Oldham, Yi-Hsin Yeh (2017), Brand Attitudes and Search Engine Queries, Journal of Interactive Marketing, 37 (), pp. 105-116. 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.

• Chris Chapman and Elea McDonnell Feit, R for Marketing Research and Analytics (:, 2015)
• 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.
• Elea McDonnell Feit, Jane K. Dokko, Michael Barr, “Preferences for Plastic”. In No Slack, edited by Jane K. Dokko, (:, 2012)
• 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.

Past Courses

• MKTG3090 - Experiments for Bus Dec Making

  EXPERIMENTS FOR BUSINESS DECISION MAKING: In the past decade, massive shifts in how companies interact with their customers have suddenly made field experiments an economically feasible way to learn about a variety of business questions such as what types of promotions are most effective, what products should be stocked at a store, how e-mail promotions should be designed, how sales staff should be compensated, etc. Many marketers engaged in online retailing, direct-marketing, online advertising, media management, etc. are rapidly embracing a "test and learn" philosophy and a number of platforms such as Google Website Optimizer, have been developed to facilitate rigorous field experiments in the online environment. Just as with the quality revolution in manufacturing during the 1980s and 1990s, the rapid rise of the "test and learn" philosophy in marketing has created a huge demand for those who can design, field, and analyze marketing experiments. Through this course, you will learn and practice a wide range of critical skills, from the statistical methods used to design and analyze experiments to the management and strategy required to execute an experiment and act on the results. Although the cases and examples will focus on marketing problems, the material covered can be applied in a number of other domains particularly operations management and product design.

• MKTG8090 - Experiments for Business Dec

  In the past decade, massive shifts in how companies interact with their customers have suddenly made field experiments an economically feasible way to learn about a variety of business questions such as what types of promotions are most effective, what products should be stocked at a store, how e-mail promotions should be designed, how sales staff should be compensated, etc. Many marketers engaged in online retailing, direct-marketing, online advertising, media management, etc. are rapidly embracing a "test and learn" philosophy and a number of platforms such as Google Website Optimizer, have been developed to facilitate rigorous field experiments in the online environment. Just as with the quality revolution in manufacturing during the 1980s and 1990s, the rapid rise of the "test and learn" philosophy in marketing has created a huge demand for those who can design, field, and analyze marketing experiments. Through this course, you will learn and practice a wide range of critical skills, from the statistical methods used to design and analyze experiments to the management and strategy required to execute an experiment and act on the results. Although the cases and examples will focus on marketing problems, the material covered can be applied in a number of other domains particularly operations management and product design.

• MKTG8990 - Independent Study

  A student contemplating an independent study project must first find a faculty member who agrees to supervise and approve the student's written proposal as an independent study (MKTG 899). If a student wishes the proposed work to be used to meet the ASP requirement, he/she should then submit the approved proposal to the MBA adviser who will determine if it is an appropriate substitute. Such substitutions will only be approved prior to the beginning of the semester.

• American Marketing Association ART Forum Best Demo, 2017
• Drexel University LeBow College of Business Junior Teaching Award, 2017
• Adobe University Research Award ($25,000), 2017
• Drexel University LeBow College of Business Research Award, 2016
• American Marketing Association "4 Under 40" Emerging Leaders Award, 2013
• INFORMS Young Researcher Connection, 2012

• It's 'Take and Give' for Maketing in Today's World, CMO.com - 09/30/2013
• Women in Analytics: Wharton's Elea Feit, All Analytics - 08/27/2013
• In cutting loyalty cards, Shaw's is in the minority, Boston Globe - 07/06/2013
• Bridging the Gap, Marketing Insights - 01/10/2013
• What does big data really mean and how do you create value from it?, Deloitte University Press - 12/15/2012
• Eight Questions to Ask Your Director of Analytics, Wharton on Forbes - 11/12/2012
• Inside Wharton's Research Center for Customer Analytics, DataInformed - 10/03/2012
• Customer Analytics: A new lifeline for the Red Cross and other nonprofits?, Knowledge@Wharton - 02/01/2012
• How do people follow the World Cup? ESPN is interested, LA Times - 07/02/2010

Knowledge @ Wharton

• How Dynamic Electricity Pricing Can Improve Market Efficiency, Knowledge @ Wharton - 11/12/2024
• Is Algorithmic Management Too Controlling?, Knowledge @ Wharton - 11/12/2024
• The Hidden Cost of AI Energy Consumption, Knowledge @ Wharton - 11/12/2024
• Charitable and Political Giving | Pinar Yildirim, Knowledge @ Wharton - 11/12/2024
• The Future of Banking, Knowledge @ Wharton - 11/11/2024
• Election Statistics, Knowledge @ Wharton - 11/06/2024
• Robots Are Taking Over Low-skilled Jobs — and Changing Votes, Knowledge @ Wharton - 11/05/2024
• Crypto Politics: Liberals’ and Conservatives’ Confidence in Cryptocurrencies, Knowledge @ Wharton - 11/05/2024
• Leadership and Elections | Mike Useem, Knowledge @ Wharton - 11/05/2024
• Automation Is Harming Low-skilled Workers and Possibly Their Voting Preferences, Knowledge @ Wharton - 11/01/2024