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(653)
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Show Results For
- All HBS Web
(653)
- News (145)
- Research (425)
- Events (20)
- Multimedia (12)
- Faculty Publications (300)
- February 2011 (Revised February 2012)
- Case
Online Marketing at Big Skinny
By: Benjamin Edelman and Scott Duke Kominers
Describes a wallet maker's application of seven Internet marketing technologies: display ads, algorithmic search, sponsored search, social media, interactive content, online distributors, and A/B testing. Provides concise introductions to the key features of each... View Details
Keywords: Advertising Campaigns; Digital Marketing; Resource Allocation; Marketing Strategy; Performance Evaluation; Internet and the Web; Retail Industry
Edelman, Benjamin, and Scott Duke Kominers. "Online Marketing at Big Skinny." Harvard Business School Case 911-033, February 2011. (Revised February 2012.) (request a courtesy copy.)
- Forthcoming
- Article
Human-Algorithm Collaboration with Private Information: Naïve Advice Weighting Behavior and Mitigation
By: Maya Balakrishnan, Kris Ferreira and Jordan Tong
Even if algorithms make better predictions than humans on average, humans may sometimes have private information which an algorithm does not have access to that can improve performance. How can we help humans effectively use and adjust recommendations made by... View Details
Keywords: AI and Machine Learning; Analytics and Data Science; Forecasting and Prediction; Digital Marketing
Balakrishnan, Maya, Kris Ferreira, and Jordan Tong. "Human-Algorithm Collaboration with Private Information: Naïve Advice Weighting Behavior and Mitigation." Management Science (forthcoming). (Pre-published online March 24, 2025.)
- Article
Vungle Inc. Improves Monetization Using Big-Data Analytics
By: Bert De Reyck, Ioannis Fragkos, Yael Grushka-Cockayne, Casey Lichtendahl, Hammond Guerin and Andrew Kritzer
The advent of big data has created opportunities for firms to customize their products and services to unprecedented levels of granularity. Using big data to personalize an offering in real time, however, remains a major challenge. In the mobile advertising industry,... View Details
Keywords: Big Data; Monetization; Data and Data Sets; Advertising; Mobile Technology; Customization and Personalization; Performance Improvement
De Reyck, Bert, Ioannis Fragkos, Yael Grushka-Cockayne, Casey Lichtendahl, Hammond Guerin, and Andrew Kritzer. "Vungle Inc. Improves Monetization Using Big-Data Analytics." Interfaces 47, no. 5 (September–October 2017): 454–466.
- 17 Jan 2020
- News
Review: Competing in the Digital Age
- November 2018
- Case
Sportradar (A): From Data to Storytelling
By: Ramon Casadesus-Masanell, Karen Elterman and Oliver Gassmann
In 2013, the Swiss sports data company Sportradar debated whether to expand from its core business of data provision to bookmakers into sports media products. Sports data was becoming a commodity, and in the future, sports leagues might reduce their dependence on... View Details
Keywords: Sports Data; Data; Sport; Sportradar; Football; Soccer; Gambling; Betting; Betting Markets; Statistics; Odds; Live Data; Bookmakers; Betradar; Visualization; Integrity; Monitoring; Gaming; Streaming; 2013; St.Gallen; Algorithm; Mathematical Modeling; Carsten Koerl; Betandwin; Bwin; Wagering; Probability; Sports; Analytics and Data Science; Mathematical Methods; Games, Gaming, and Gambling; Transition; Strategy; Media; Sports Industry; Technology Industry; Information Technology Industry; Media and Broadcasting Industry; Europe; Switzerland; Asia; Austria; Germany; England
Casadesus-Masanell, Ramon, Karen Elterman, and Oliver Gassmann. "Sportradar (A): From Data to Storytelling." Harvard Business School Case 719-429, November 2018.
- Research Summary
Overview
I develop machine learning tools and techniques which enable human decision makers to make better decisions. More specifically, my research addresses the following fundamental questions pertaining to human and algorithmic decision-making:
1. How to build... View Details
1. How to build... View Details
- 2021
- Working Paper
First Law of Motion: Influencer Video Advertising on TikTok
By: Jeremy Yang, Juanjuan Zhang and Yuhan Zhang
This paper engineers an intuitive feature that is predictive of the causal effect of influencer video advertising on product sales. We propose the concept of m-score, a summary statistic that captures the extent to which a product is advertised in the most engaging... View Details
Keywords: Influencer Advertising; Video Advertising; Computer Vision; Machine Learning; Advertising; Online Technology
Yang, Jeremy, Juanjuan Zhang, and Yuhan Zhang. "First Law of Motion: Influencer Video Advertising on TikTok." Working Paper, March 2021.
- 14 Nov 2016
- News
Why Big Data Isn’t Enough
- September 2020 (Revised February 2024)
- Teaching Note
Artea (A), (B), (C), and (D): Designing Targeting Strategies
By: Eva Ascarza and Ayelet Israeli
Teaching Note for HBS No. 521-021,521-022,521-037,521-043. 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... View Details
- Article
Adaptive Machine Unlearning
By: Varun Gupta, Christopher Jung, Seth Neel, Aaron Roth, Saeed Sharifi-Malvajerdi and Chris Waites
Data deletion algorithms aim to remove the influence of deleted data points from trained models at a cheaper computational cost than fully retraining those models. However, for sequences of deletions, most prior work in the non-convex setting gives valid guarantees... View Details
Gupta, Varun, Christopher Jung, Seth Neel, Aaron Roth, Saeed Sharifi-Malvajerdi, and Chris Waites. "Adaptive Machine Unlearning." Advances in Neural Information Processing Systems (NeurIPS) 34 (2021).
- August 2021 (Revised November 2024)
- Case
Intenseye: Powering Workplace Health and Safety with AI (A)
By: Michael W. Toffel and Youssef Abdel Aal
Intenseye was a Turkey-based technology startup that deployed machine learning algorithms to workplace camera feeds in order to identify unsafe worker actions and unsafe working conditions, in order to help improve worker safety. The case describes how Intenseye’s... View Details
Keywords: Privacy; Product Development; Operations; Technological Innovation; Value Creation; Production; Distribution; Safety; Risk and Uncertainty; Technology Industry; Manufacturing Industry; Distribution Industry; Turkey; Middle East; United States
Toffel, Michael W., and Youssef Abdel Aal. "Intenseye: Powering Workplace Health and Safety with AI (A)." Harvard Business School Case 622-037, August 2021. (Revised November 2024.)
- Article
Advancing Computational Biology and Bioinformatics Research Through Open Innovation Competitions
By: Andrea Blasco, Michael G. Endres, Rinat A. Sergeev, Anup Jonchhe, Max Macaluso, Rajiv Narayan, Ted Natoli, Jin H. Paik, Bryan Briney, Chunlei Wu, Andrew I. Su, Aravind Subramanian and Karim R. Lakhani
Open data science and algorithm development competitions offer a unique avenue for rapid discovery of better computational strategies. We highlight three examples in computational biology and bioinformatics research where the use of competitions has yielded significant... View Details
Keywords: Computational Biology; Bioinformatics; Innovation Competitions; Research; Collaborative Innovation and Invention
Blasco, Andrea, Michael G. Endres, Rinat A. Sergeev, Anup Jonchhe, Max Macaluso, Rajiv Narayan, Ted Natoli, Jin H. Paik, Bryan Briney, Chunlei Wu, Andrew I. Su, Aravind Subramanian, and Karim R. Lakhani. "Advancing Computational Biology and Bioinformatics Research Through Open Innovation Competitions." PLoS ONE 14, no. 9 (September 2019).
- October 2019
- Article
Making Sense of Recommendations
By: Michael Yeomans, Anuj Shah, Sendhil Mullainathan and Jon Kleinberg
Computer algorithms are increasingly being used to predict people's preferences and make recommendations. Although people frequently encounter these algorithms because they are cheap to scale, we do not know how they compare to human judgment. Here, we compare computer... View Details
Keywords: Recommender Systems; Artificial Intelligence; Interpretability; Information Technology; Forecasting and Prediction; Decision Making; Attitudes
Yeomans, Michael, Anuj Shah, Sendhil Mullainathan, and Jon Kleinberg. "Making Sense of Recommendations." Journal of Behavioral Decision Making 32, no. 4 (October 2019): 403–414.
Edward McFowland III
Edward McFowland III is an Assistant Professor in the Technology and Operations Management Unit at Harvard Business School. He teaches the first-year TOM course in the required curriculum.
Professor McFowland’s research interests – which lie at the... View Details
- 25 Aug 2018
- News
Are Superstar Firms and Amazon Effects Reshaping the Economy?
- 2025
- Working Paper
Warnings and Endorsements: Improving Human-AI Collaboration in the Presence of Outliers
By: Matthew DosSantos DiSorbo, Kris Ferreira, Maya Balakrishnan and Jordan Tong
Problem definition: While artificial intelligence (AI) algorithms may perform well on data that are representative of the training set (inliers), they may err when extrapolating on non-representative data (outliers). How can humans and algorithms work together to make... View Details
DosSantos DiSorbo, Matthew, Kris Ferreira, Maya Balakrishnan, and Jordan Tong. "Warnings and Endorsements: Improving Human-AI Collaboration in the Presence of Outliers." Working Paper, May 2025.
- 2020
- Working Paper
Machine Learning for Pattern Discovery in Management Research
Supervised machine learning (ML) methods are a powerful toolkit for discovering robust patterns in quantitative data. The patterns identified by ML could be used as an observation for further inductive or abductive research, but should not be treated as the result of a... View Details
Keywords: Machine Learning; Theory Building; Induction; Decision Trees; Random Forests; K-nearest Neighbors; Neural Network; P-hacking; Analytics and Data Science; Analysis
Choudhury, Prithwiraj, Ryan Allen, and Michael G. Endres. "Machine Learning for Pattern Discovery in Management Research." Harvard Business School Working Paper, No. 19-032, September 2018. (Revised June 2020.)
- February 2024
- Module Note
Data-Driven Marketing in Retail Markets
By: Ayelet Israeli
This note describes an eight-class sessions module on data-driven marketing in retail markets. The module aims to familiarize students with core concepts of data-driven marketing in retail, including exploring the opportunities and challenges, adopting best practices,... View Details
Keywords: Data; Data Analytics; Retail; Retail Analytics; Data Science; Business Analytics; ＂Marketing Analytics＂; Omnichannel; Omnichannel Retailing; Omnichannel Retail; DTC; Direct To Consumer Marketing; Ethical Decision Making; Algorithmic Bias; Privacy; A/B Testing; Descriptive Analytics; Prescriptive Analytics; Predictive Analytics; Analytics and Data Science; E-commerce; Marketing Channels; Demand and Consumers; Marketing Strategy; Retail Industry
Israeli, Ayelet. "Data-Driven Marketing in Retail Markets." Harvard Business School Module Note 524-062, February 2024.