Lemmens, Roos, Gabel et al., Personalization and Targeting: How to Experiment, Learn & Optimize
[SSRN]
Bale, Feit and Bradlow, Dynamic Brand Equity and Retail Media [Request]
Zuo, Ye and Feit, A Gaussian process model for response time in conjoint surveys
[Request]][YouTube]
Dotson, Feit and Beltramo, Ratings-Informed Probit for Predicting Substitution
[SSRN]
Berman and Feit (2024) Latent Stratification for Incrementality Experiments, Marketing Science
[MKSC][SSRN] [arXiv]
Ada, Abou Nabout and Feit (2022) Context Information can Increase Revenue in Online Display Advertising Auctions: Evidence from a Policy Change, Journal of Marketing Research
[JMR][SSRN][Twitter thread][LinkedIn article]
Feinberg, Bruch, Braun, Falk, Fefferman, Feit, Helveston, Larremore, McShane, Patania and Small (2020) A Framework for Studying Choices in Networks, Marketing Letters
[MKLET]
Feit and Berman (2019) Test & Roll: Profit-Maximizing Marketing Experiments, Marketing Science
[MKSC] [arXiv] [SSRN] [replication files][Twitter thread][Post][shiny app]
Han, Feit and Srinivasan (2019) Can Negative Buzz Increase Purchase Intent?, Marketing Letters
[MKLET]
Helveston, Feit and Michalek (2018) Pooling Stated and Revealed Preferences in the Presence of RP Endogeneity, Transportation Research Part B: Methodological
[TRB]
Zantedeschi, Feit and Bradlow (2017) Measuring Multi-Channel Advertising Response, Management Science
[MS] [SSRN]
Dotson, Fan, Feit, Oldham, and Yeh (2017) Brand Attitudes and Search Engine Queries, Journal of Interactive Marketing
[JIM] [SSRN]
Haaf, Morrow, Azevedo, Feit and Michalek (2016) Forecasting light-duty vehicle demand using alternative-specific constants for endogeneity correction versus calibration, Transportation Research Part B: Methodological
[TRB]
Helveston, Liu, Feit, Fuchs, Klampfl and Michalek, (2015) Will Subsidies Drive Electric Vehicle Adoption? Measuring Consumer Preferences in the U.S. and China, Transportation Research Part A: Policy and Practice
[TRA]
Feit, Wang, Bradlow and Fader (2013) Fusing Aggregate and Disaggregate Data with an Application to Multi-Platform Media Consumption, Journal of Marketing Research
[JMR]
Feit, Beltramo, and Feinberg (2010) Reality Check: Combining survey and market data to estimate choice models, Management Science
[MS]
Netzer, Olivier, et al. (2008) Beyond Conjoint Analysis: Advances in Preference Measurement, Marketing Letters
[MKLET]
Feit and Sahni (expected 2025) How Digital Ads Work: A Review for Foundations and Trends in Marketing
Papies, Ebbes and Feit (2022) Endogeneity and Causal Inference in Winer and Neslin History of Marketing Science [SSRN]
Feit and Bradlow (2019) Fusion Models in Homburg, Klarmann and Vomberg, Handbook of Marketing Research
[Springer] [code]
Feit, Feinberg and Lenk (2017) Bayesian Analysis in Leeflang, Wieringa, Bijmolt and Pauwels, Advanced Methods for Modeling Markets
[Springer]
Stahl, Valli and Feit (2017) Field Experiments in Homburg, Klarmann and Vomberg, Handbook of Marketing Research
[Springer]
Christian, Feit and Beltramo (2007, 2011) Expert Feature: Conjoint Analysis at General Motors in Feinberg, Kinnear and Taylor, Modern Marketing Research: Methods and Cases
[Centgage]
Feit, Pensig and Harper (2011) Expert Feature: Customer Analytics in Feinberg, Kinnear and Taylor, Modern Marketing Research: Methods and Cases
[Centgage]
Barr, Dokko and Feit (2012), Preferences for Plastic, in Barr, No Slack
[Brookings] [SSRN]
Chapman and Feit (2015, 2019) R for Marketing Research and Analytics
[Springer] [authors’ website]
Schwartz, Chapman and Feit (2020) Python for Marketing Research and Analytics [Springer] [authors’ website]
Novak, Feit, Jensen and Bradlow, Bayesian Imputation for Anonymous Visits in CRM Data
[SSRN]
Phan, Bradlow and Feit, An Integrated Model for Dynamic Brand Equity
Feit, Extending the Generalized Multinomial Logit Model: Error Scale and Decision Maker Characteristics
[SSRN]