Mitchell Lovett

Associate Professor of Marketing | University of Rochester

Rochester, NY, UNITED STATES

Mitchell Lovett applies and develops quantitative methods to study marketing problems; Artificial Intelligence (AI) expert

Areas of Expertise (13)

AI in Business Analytics

AI in Business

Consumer Learning

Branding

Advertising Content and Schedule Choices

Retail Strategy

Quantitative Marketing

Targeted Advertising

Online and Offline Word-of-Mouth

Social Media Listening

Political Advertising

AI

Artificial Intelligence

Media

Publications Photos Videos Documents Audio/Podcasts

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Links (2)

Biography

Professor Lovett is the Senior Associate Dean of Education and Innovation. He is also a leading scholar and teacher as the Benjamin Forman Professor of Marketing. He joined the Simon Business School in 2008 after earning his PhD in marketing from Duke University. In his administrative role, he has been instrumental in launching the AI Initiative, a cross-disciplinary effort to integrate AI into Simon’s business education. He also helped to develop the Online Masters in Business Analytics and Applied AI, a cutting-edge program that prepares students for the rapidly evolving data and AI-driven economy. His research interests span a wide range of topics in marketing, such as advertising, branding, word-of-mouth, political marketing, consumer and firm learning, retailing, and conjoint analysis. He applies and develops empirical methods to study marketing phenomena and to inform marketing decisions. His research has been published in top journals and garnered recognitions such as the Marketing Science Institute's Young Scholars and Scholars distinctions, and the William F. O'Dell award finalist for long-term impact. His research has also attracted national media attention, and he has been cited in outlets such as The New York Times, Forbes, and Ad Age. He is frequently invited to speak at academic and industry conferences and events. He also advises PhD students and is an award-winning teacher including courses on marketing research, marketing strategy, analytics design and applications, advertising strategy, consumer behavior, and PhD seminars in quantitative marketing.

Education (3)

Ohio Wesleyan University: BA, Business Administration 1997

Duke University: PhD, Business Administration 2008

Boise State University: MBA, Business Administration 2004

Selected Media Appearances (1)

Preparing Future Leaders: The Impact of AI on Business Education

Forbes

2024-02-27

In a recent conversation with Mitch Lovett, Senior Associate Dean of Education and Innovation at Simon Business School, University of Rochester, the depth of these changes and the strategies for navigating them were brought to light. Lovett emphasizes the importance of equipping students not just with knowledge but with the ability to adapt and thrive in a technologically fluid landscape.

Selected Articles (4)

Empirical Research on Political Marketing: A Selected Review

Consumer Needs and Solutions

Mitchell J. Lovett

2019-09-03

This article reviews empirical research on political marketing. The goal of this selective review is to provide an overview of this body of research that crosses fields including economics, political science, marketing, information systems, and communications in order to make it easier for newcomers to quickly identify key papers and understand the state of the field. The review takes the perspective of the marketing literature and includes a discussion of data sources, modeling and methodological issues, and some selected, prominent topics.

Can Your Advertising Really Buy Earned Impressions? The Effect of Brand Advertising on Word of Mouth

Quantitative Marketing and Economics /Springer

Mitchell J. Lovett, Renana Peres, and Linli Xu

2019-04-06

Paid media expenditures could potentially increase earned media exposures such as social media posts and other word-of-mouth (WOM). However, academic research on the effect of advertising on WOM is scarce and shows mixed results. We examine the relationship between monthly Internet and TV advertising expenditures and WOM for 538 U.S. national brands across 16 categories over 6.5 years. We find that the average implied advertising elasticity on total WOM is small: 0.019 for TV, and 0.014 for Internet. On the online WOM (measured volume of brand chatter on blogs, user-forums, and Twitter), we find average monthly effects of 0.008 for TV and 0.01 for Internet advertising. Even the categories that have the strongest implied elasticities are only as large as 0.05. Despite this small average effect, we do find that advertising in certain events may produce more desirable amounts of WOM. Specifically, using a synthetic control approach, we find that being a Super Bowl advertiser causes a moderate increase in total WOM that lasts 1 month. The effect on online WOM is larger, but lasts for only 3 days. We discuss the implications of these findings for managing advertising and WOM.

Product Launches with New Attributes: A Conjoint-Loyalty Card Technique for Estimating Demand

Journal of Marketing Research

Mitchell J. Lovett, Paul Ellickson, and Bhoomija Ranjan

2017-01-01

We propose and empirically evaluate a new hybrid estimation approach that integrates choice-based conjoint with repeated purchase data for a dense consumer panel, and show that it increases the accuracy of conjoint predictions for actual purchases observed months later. Our key innovation lies in combining conjoint data with a long and detailed panel of actual choices for a random sample of the target population. By linking the actual purchase and conjoint data, we can estimate preferences for attributes not yet present in the marketplace, while also addressing many of the key limitations of conjoint analysis, including sample selection and contextual differences. Counterfactual product and pricing exercises then illustrate its managerial relevance.

Mobile Diaries Benchmark Against Metered Measurements: An Empirical Investigation

International Journal of Research in Marketing

Mitchell J. Lovett and Renana Peres

2018-06-01

Researchers seeking to study the relationships between consumers' communications, attitudes, and behaviors could benefit from monitoring consumers over time, across multiple locations and channels, and in a way that reflects consumers' subjective perceptions. Diaries on smartphones (mobile diaries) can be used as a research tool for such purposes. A mobile diary is a self-report instrument whereby people use their mobile handset to repeatedly report experiences of interest. Mobile diaries are increasingly used in psychology, geography, medicine, and commercial marketing. Yet they have rarely been used for quantitative marketing research, and were not benchmarked against best-practice metrics in marketing. In this study, we aim to set the ground for using mobile diaries in quantitative marketing research. We first lay out the theoretical infrastructure for the usage of mobile diaries, and describe possible respondent reporting concerns, including concerns related to non-reporting, reporting over time, and concerns stemming from individual-level heterogeneity. We demonstrate the potential of mobile diaries, as well as the importance of the various concerns, using a benchmark test case in the context of primetime TV viewing. Our benchmark uses a sample of respondents with both mobile diary viewing reports and Nielsen People Meter (NPM) records. Our analysis reveals that averaging across all conditions, 47.4%–64.7% of the NPM records are reported by the diary. The major sources for mismatch are random time periods without alarms, short viewings, and periodic reporting inactivity (pulsing). Concerns such as a decrease in reporting rates over time (e.g., fatigue), smartphone ownership, and demographic variation across individuals have relatively small effects on reporting likelihood. Analyzing the cases in which diary reports do not have a matching NPM record, we find many of them can be attributed to out-of-home viewing and viewing on non-metered devices. This finding demonstrates how mobile diaries can complement metered measurements. Overall, aggregate diary-based ratings have a 0.90 correlation with NPM ratings. We discuss implications for designing and using mobile diary studies in marketing.