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- Faculty Publications (278)
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- All HBS Web (465)
- Faculty Publications (278)
- February 2019
- Article
Who Benefits Most in Disease Management Programs: Improving Target Efficiency
By: Timothy Simcoe, Maryaline Catillon and Paul Gertler
Disease management programs aim to reduce cost by improving the quality of care for chronic diseases. Evidence of their effectiveness is mixed. Reducing health care spending sufficiently to cover program costs has proved particularly challenging. This study uses a... View Details
Keywords: Health Economics; Target Efficiency; Diabetes; Disease Management; Program Evaluation; Heterogeneity; Economics; Health; Quality; Health Care and Treatment; Cost Management; Health Industry
Simcoe, Timothy, Maryaline Catillon, and Paul Gertler. "Who Benefits Most in Disease Management Programs: Improving Target Efficiency." Health Economics 28, no. 2 (February 2019): 189–203.
- September–October 2013
- Article
The Dynamic Advertising Effect of Collegiate Athletics
By: Doug J. Chung
I measure the spillover effect of intercollegiate athletics on the quantity and quality of applicants to institutions of higher education in the United States, popularly known as the "Flutie Effect." I treat athletic success as a stock of goodwill that decays over... View Details
Keywords: Choice Modeling; Entertainment Marketing; Heterogeneity; Panel Data; Structural Modeling; Rights; Analytics and Data Science; Higher Education; Ethics; Consumer Behavior; Advertising; Sports; Advertising Industry; Education Industry
Chung, Doug J. "The Dynamic Advertising Effect of Collegiate Athletics." Marketing Science 32, no. 5 (September–October 2013): 679–698. (Lead article. Featured in HBS Working Knowledge.)
- 21 Apr 2018
- Panel Discussion
Discussion of "Identification of Dynamic Games with Multiple Equilibria and Unobserved Heterogeneity with Application to Fast Food Chains In China," by Yao Luo, Ping Xiao, Ruli Xiao
By: David Hao Zhang
Zhang, David Hao. Discussion of "Identification of Dynamic Games with Multiple Equilibria and Unobserved Heterogeneity with Application to Fast Food Chains In China," by Yao Luo, Ping Xiao, Ruli Xiao. International Industrial Organization Conference (IIOC), Indianapolis, IN, April 21, 2018.
- February 2018
- Article
Retention Futility: Targeting High-Risk Customers Might Be Ineffective.
By: Eva Ascarza
Companies in a variety of sectors are increasingly managing customer churn proactively, generally by detecting customers at the highest risk of churning and targeting retention efforts towards them. While there is a vast literature on developing churn prediction models... View Details
Keywords: Retention/churn; Proactive Churn Management; Field Experiments; Heterogeneous Treatment Effect; Machine Learning; Customer Relationship Management; Risk Management
Ascarza, Eva. "Retention Futility: Targeting High-Risk Customers Might Be Ineffective." Journal of Marketing Research (JMR) 55, no. 1 (February 2018): 80–98.
- February 2015
- Article
Location Choices under Strategic Interactions
By: Juan Alcacer, Cristian Dezso and Minyuan Zhao
The literature on location choices has mostly emphasized the impact of location and firm characteristics. However, most industries with a significant presence of multi-location firms are oligopolistic in nature, which suggests that strategic interaction among firms... View Details
Keywords: Location Strategies; Multinational Strategy; Oligopolistic Competition; Firm Heterogeneity; Geographic Location; Multinational Firms and Management; Balance and Stability; Decision Choices and Conditions; Game Theory
Alcacer, Juan, Cristian Dezso, and Minyuan Zhao. "Location Choices under Strategic Interactions." Strategic Management Journal 36, no. 2 (February 2015): 197–215.
- 2023
- Article
Experimental Evaluation of Individualized Treatment Rules
By: Kosuke Imai and Michael Lingzhi Li
The increasing availability of individual-level data has led to numerous applications of individualized (or personalized) treatment rules (ITRs). Policy makers often wish to empirically evaluate ITRs and compare their relative performance before implementing them in a... View Details
Keywords: Causal Inference; Heterogeneous Treatment Effects; Precision Medicine; Uplift Modeling; Analytics and Data Science; AI and Machine Learning
Imai, Kosuke, and Michael Lingzhi Li. "Experimental Evaluation of Individualized Treatment Rules." Journal of the American Statistical Association 118, no. 541 (2023): 242–256.
- February 2017
- Article
How Much Is a Win Worth? An Application to Intercollegiate Athletics
By: Doug J. Chung
Intercollegiate athletics in the United States have become a multibillion-dollar industry over the past several decades. In this study, we investigate the short- and long-term direct monetary effects of operating a winning athletics program for an academic institution... View Details
Keywords: Dynamic Panel Data; Heterogeneity; Instrumental Variables; Intercollegiate Athletics; Educational Finance; Entertainment Marketing; Higher Education; Marketing; Sports; Revenue; Education Industry; United States
Chung, Doug J. "How Much Is a Win Worth? An Application to Intercollegiate Athletics." Management Science 63, no. 2 (February 2017): 548–565.
- 2024
- Working Paper
Incrementality Representation Learning: Synergizing Past Experiments for Intervention Personalization
This paper introduces Incrementality Representation Learning (IRL), a novel multitask representation learning framework that predicts heterogeneous causal effects of marketing interventions. By leveraging past experiments, IRL efficiently designs and targets... View Details
Keywords: Heterogeneous Treatment Effect; Multi-task Learning; Representation Learning; Personalization; Promotion; Deep Learning; Field Experiments; Customer Focus and Relationships; Customization and Personalization
Huang, Ta-Wei, Eva Ascarza, and Ayelet Israeli. "Incrementality Representation Learning: Synergizing Past Experiments for Intervention Personalization." Harvard Business School Working Paper, No. 24-076, June 2024.
- Research Summary
Overview
Professor MacKay combines theory and measurement to deliver new insights about price competition and consumer preferences. In current and published papers, his research addresses how strategic pricing decisions may be influenced by algorithms, long-term contracts,... View Details
- July–August 2024
- Article
Doing More with Less: Overcoming Ineffective Long-Term Targeting Using Short-Term Signals
By: Ta-Wei Huang and Eva Ascarza
Firms are increasingly interested in developing targeted interventions for customers with the best response,
which requires identifying differences in customer sensitivity, typically through the conditional average treatment
effect (CATE) estimation. In theory, to... View Details
Keywords: Long-run Targeting; Heterogeneous Treatment Effect; Statistical Surrogacy; Customer Churn; Field Experiments; Consumer Behavior; Customer Focus and Relationships; AI and Machine Learning; Marketing Strategy
Huang, Ta-Wei, and Eva Ascarza. "Doing More with Less: Overcoming Ineffective Long-Term Targeting Using Short-Term Signals." Marketing Science 43, no. 4 (July–August 2024): 863–884.
- October 2024
- Article
Canary Categories
By: Eric Anderson, Chaoqun Chen, Ayelet Israeli and Duncan Simester
Past customer spending in a category is generally a positive signal of future customer spending. We show that there exist “canary categories” for which the reverse is true. Purchases in these categories are a signal that customers are less likely to return to that... View Details
Keywords: Churn; Churn Management; Churn/retention; Assortment Planning; Retail; Retailing; Retailing Industry; Preference Heterogeneity; Assortment Optimization; Customers; Retention; Consumer Behavior; Forecasting and Prediction; Retail Industry
Anderson, Eric, Chaoqun Chen, Ayelet Israeli, and Duncan Simester. "Canary Categories." Journal of Marketing Research (JMR) 61, no. 5 (October 2024): 872–890.
- June 2023
- Simulation
Artea Dashboard and Targeting Policy Evaluation
By: Ayelet Israeli and Eva Ascarza
Companies deploy A/B experiments to gain valuable insights about their customers in order to answer strategic business problems. In marketing, A/B tests are often used to evaluate marketing interventions intended to generate incremental outcomes for the firm. The Artea... View Details
Keywords: Algorithm Bias; Algorithmic Data; Race And Ethnicity; Experimentation; Promotion; Marketing And Society; Big Data; Privacy; Data-driven Management; Data Analysis; Data Analytics; E-Commerce Strategy; Discrimination; Targeted Advertising; Targeted Policies; Pricing Algorithms; A/B Testing; Ethical Decision Making; Customer Base Analysis; Customer Heterogeneity; Coupons; Marketing; Race; Gender; Diversity; Customer Relationship Management; Marketing Communications; Advertising; Decision Making; Ethics; E-commerce; Analytics and Data Science; Retail Industry; Apparel and Accessories Industry; United States
- September 2020 (Revised July 2022)
- Technical Note
Algorithmic Bias in Marketing
By: Ayelet Israeli and Eva Ascarza
This note focuses on algorithmic bias in marketing. First, it presents a variety of marketing examples in which algorithmic bias may occur. The examples are organized around the 4 P’s of marketing – promotion, price, place and product—characterizing the marketing... View Details
Keywords: Algorithmic Data; Race And Ethnicity; Promotion; ＂Marketing Analytics＂; Marketing And Society; Big Data; Privacy; Data-driven Management; Data Analysis; Data Analytics; E-Commerce Strategy; Discrimination; Targeting; Targeted Advertising; Pricing Algorithms; Ethical Decision Making; Customer Heterogeneity; Marketing; Race; Ethnicity; Gender; Diversity; Prejudice and Bias; Marketing Communications; Analytics and Data Science; Analysis; Decision Making; Ethics; Customer Relationship Management; E-commerce; Retail Industry; Apparel and Accessories Industry; United States
Israeli, Ayelet, and Eva Ascarza. "Algorithmic Bias in Marketing." Harvard Business School Technical Note 521-020, September 2020. (Revised July 2022.)
- 2024
- Working Paper
What Drives Variation in Investor Portfolios? Estimating the Roles of Beliefs and Risk Preferences
By: Mark Egan, Alexander MacKay and Hanbin Yang
We present an empirical model of portfolio choice that allows for the nonparametric estimation of investors' (subjective) expectations and risk preferences. Utilizing a comprehensive dataset of 401(k) plans from 2009 through 2019, we explore heterogeneity in asset... View Details
Keywords: Stock Market Expectations; Demand Estimation; Retirement Planning; Defined Contribution Retirement Plan; 401 (K); Finance; Investment Portfolio; Investment; Retirement; Behavioral Finance; Financial Services Industry; United States
Egan, Mark, Alexander MacKay, and Hanbin Yang. "What Drives Variation in Investor Portfolios? Estimating the Roles of Beliefs and Risk Preferences." Harvard Business School Working Paper, No. 22-044, December 2021. (Revisions Requested at the Review of Financial Studies. Revised April 2024. Direct download. NBER Working Paper Series, No. 29604, December 2021)
- September 2020 (Revised June 2023)
- Exercise
Artea: Designing Targeting Strategies
By: Eva Ascarza and Ayelet Israeli
This collection of exercises aims to teach students about 1)Targeting Policies; and 2)Algorithmic bias in marketing—implications, causes, and possible solutions. Part (A) focuses on A/B testing analysis and targeting. Parts (B),(C),(D) Introduce algorithmic bias. The... View Details
Keywords: Algorithmic Data; Race And Ethnicity; Experimentation; Promotion; ＂Marketing Analytics＂; Marketing And Society; Big Data; Privacy; Data-driven Management; Data Analytics; Data Analysis; E-Commerce Strategy; Discrimination; Targeted Advertising; Targeted Policies; Targeting; Pricing Algorithms; A/B Testing; Ethical Decision Making; Customer Base Analysis; Customer Heterogeneity; Coupons; Algorithmic Bias; Marketing; Race; Gender; Diversity; Customer Relationship Management; Marketing Communications; Advertising; Decision Making; Ethics; E-commerce; Analytics and Data Science; Retail Industry; Apparel and Accessories Industry; United States
Ascarza, Eva, and Ayelet Israeli. "Artea: Designing Targeting Strategies." Harvard Business School Exercise 521-021, September 2020. (Revised June 2023.)
- 2023
- Working Paper
'De Gustibus' and Disputes about Reference Dependence
By: Thomas Graeber, Pol Campos-Mercade, Lorenz Goette, Alexandre Kellogg and Charles Sprenger
Existing tests of reference-dependent preferences assume universal loss aversion. This paper examines the implications of heterogeneity in gain-loss attitudes for such tests. In experiments on labor supply and exchange behavior we measure gain-loss attitudes and then... View Details
Graeber, Thomas, Pol Campos-Mercade, Lorenz Goette, Alexandre Kellogg, and Charles Sprenger. "'De Gustibus' and Disputes about Reference Dependence." Harvard Business School Working Paper, No. 24-046, January 2024.
- 23 Oct 2024
- HBS Seminar
Rosa Ferrer, University of Pompeu Fabra and BSE
- 02 May 2016
- HBS Seminar
Chiara Farronato, Harvard Business School
- Research Summary
Overview
My research lies at the intersection of strategic management and international business, with a focus on how firms’ heterogeneous national environments affect competition, corporate strategy, and firm performance. View Details
- 2009
- Chapter
Plant-Size Distribution and Cross-Country Income Differences
By: Laura Alfaro, Andrew Charlton and Fabio Kanczuk
We investigate, using plant-level data for 79 developed and developing countries, whether differences in the allocation of resources across heterogeneous plants are a significant determinant of cross-country differences in income per worker. For this purpose, we use a... View Details
Keywords: Factories, Labs, and Plants; Developing Countries and Economies; Wages; Resource Allocation; Mathematical Methods
Alfaro, Laura, Andrew Charlton, and Fabio Kanczuk. "Plant-Size Distribution and Cross-Country Income Differences." In NBER International Seminar on Macroeconomics 2008, edited by Jeffrey A. Frankel and Christopher Pissarides. Cambridge, MA: National Bureau of Economic Research, 2009.