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- April–May 2024
- Article
Gone with the Big Data: Institutional Lender Demand for Private Information
By: Jung Koo Kang
I explore whether big-data sources can crowd out the value of private information acquired through lending relationships. Institutional lenders have been shown to exploit their access to borrowers’ private information by trading on it in financial markets. As a shock...
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Keywords:
Analytics and Data Science;
Borrowing and Debt;
Financial Markets;
Value;
Knowledge Dissemination;
Financing and Loans
Kang, Jung Koo. "Gone with the Big Data: Institutional Lender Demand for Private Information." Art. 101663. Journal of Accounting & Economics 77, nos. 2-3 (April–May 2024).
- February 2024
- Teaching Note
AB InBev: Brewing Up Forecasts during COVID-19
By: Mark Egan and C. Fritz Foley
Teaching Note for HBS Case No. 224-020. In July 2021, the CEO of AB InBev's European operations and his team strategized to position the company for success post-pandemic. As the world's largest beer company, boasting over 500 brands, revenue of $46 billion, and a...
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- February 2024
- Case
ReSpo.Vision: The Kickstart of an AI Sports Revolution
By: Paul A. Gompers, Elena Corsi and Nikolina Jonsson
This case study explores the growth journey of Polish computer vision sports start-up ReSpo.Vision in an emerging entrepreneurial ecosystem. By providing 3D data and analysis to soccer clubs, ReSpo.Vision achieved significant milestones with a €1 million seed round, an...
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Keywords:
Business Startups;
Business Plan;
Experience and Expertise;
Talent and Talent Management;
Decisions;
Decision Choices and Conditions;
Forecasting and Prediction;
Entrepreneurship;
Venture Capital;
AI and Machine Learning;
Analytics and Data Science;
Applications and Software;
Business Strategy;
Sports Industry;
Technology Industry;
Poland;
Europe
Gompers, Paul A., Elena Corsi, and Nikolina Jonsson. "ReSpo.Vision: The Kickstart of an AI Sports Revolution." Harvard Business School Case 824-151, February 2024.
- 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,...
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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.
- 2024
- Working Paper
The Impact of Culture Consistency on Subunit Outcomes
By: Jasmijn Bol, Robert Grasser, Serena Loftus and Tatiana Sandino
We examine the association between subunit culture consistency—defined as the
congruence between the organizational values espoused by top management and those
perceived and practiced by subunit employees—and subunit outcomes. Using data
from 235 subunits of a North...
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Bol, Jasmijn, Robert Grasser, Serena Loftus, and Tatiana Sandino. "The Impact of Culture Consistency on Subunit Outcomes." Working Paper, January 2024.
- 2023
- Working Paper
Debiasing Treatment Effect Estimation for Privacy-Protected Data: A Model Auditing and Calibration Approach
By: Ta-Wei Huang and Eva Ascarza
Data-driven targeted interventions have become a powerful tool for organizations to optimize business outcomes
by utilizing individual-level data from experiments. A key element of this process is the estimation
of Conditional Average Treatment Effects (CATE), which...
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Huang, Ta-Wei, and Eva Ascarza. "Debiasing Treatment Effect Estimation for Privacy-Protected Data: A Model Auditing and Calibration Approach." Harvard Business School Working Paper, No. 24-034, December 2023.
- 2023
- Other Article
The Harvard USPTO Patent Dataset: A Large-Scale, Well-Structured, and Multi-Purpose Corpus of Patent Applications
By: Mirac Suzgun, Luke Melas-Kyriazi, Suproteem K. Sarkar, Scott Duke Kominers and Stuart Shieber
Innovation is a major driver of economic and social development, and information about many kinds of innovation is embedded in semi-structured data from patents and patent applications. Though the impact and novelty of innovations expressed in patent data are difficult...
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Keywords:
USPTO;
Natural Language Processing;
Classification;
Summarization;
Patent Novelty;
Patent Trolls;
Patent Enforceability;
Patents;
Innovation and Invention;
Intellectual Property;
AI and Machine Learning;
Analytics and Data Science
Suzgun, Mirac, Luke Melas-Kyriazi, Suproteem K. Sarkar, Scott Duke Kominers, and Stuart Shieber. "The Harvard USPTO Patent Dataset: A Large-Scale, Well-Structured, and Multi-Purpose Corpus of Patent Applications." Conference on Neural Information Processing Systems (NeurIPS), Datasets and Benchmarks Track 36 (2023).
- 2023
- Working Paper
The Customer Journey as a Source of Information
By: Nicolas Padilla, Eva Ascarza and Oded Netzer
In the face of heightened data privacy concerns and diminishing third-party data access,
firms are placing increased emphasis on first-party data (1PD) for marketing decisions.
However, in environments with infrequent purchases, reliance on past purchases 1PD...
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Keywords:
Customer Journey;
Privacy;
Consumer Behavior;
Analytics and Data Science;
AI and Machine Learning;
Customer Focus and Relationships
Padilla, Nicolas, Eva Ascarza, and Oded Netzer. "The Customer Journey as a Source of Information." Harvard Business School Working Paper, No. 24-035, October 2023. (Revised October 2023.)
- September 2023 (Revised January 2024)
- Case
AB InBev: Brewing Up Forecasts during COVID-19
By: Mark Egan, C. Fritz Foley, Esel Cekin and Emilie Billaud
In July 2021, the CEO of AB InBev's European operations and his team strategized to position the company for success post-pandemic. As the world's largest beer company, boasting over 500 brands, revenue of $46 billion, and a workforce of 160,000 in 2020, AB InBev...
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Keywords:
Beer;
Forecasting;
COVID-19;
Decision;
Forecasting and Prediction;
Analytics and Data Science;
Crisis Management;
Decisions;
Financing and Loans;
Investment Return;
Resource Allocation;
Distribution;
Production;
Business Processes;
Strategic Planning;
Health Pandemics;
Digital Transformation;
Markets;
Food and Beverage Industry;
Belgium;
Europe;
Latin America;
North and Central America
Egan, Mark, C. Fritz Foley, Esel Cekin, and Emilie Billaud. "AB InBev: Brewing Up Forecasts during COVID-19." Harvard Business School Case 224-020, September 2023. (Revised January 2024.)
- September 2023 (Revised December 2023)
- Case
TetraScience: Noise and Signal
By: Thomas R. Eisenmann and Tom Quinn
In 2019, TetraScience CEO “Spin” Wang needed advice. Five years earlier, he had cofounded a startup that saw early success with a hardware product designed to help laboratory scientists in the biotechnology and pharmaceutical spaces more easily collect data from...
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Keywords:
Entrepreneurship;
Business Growth and Maturation;
Business Organization;
Restructuring;
Forecasting and Prediction;
Digital Platforms;
Analytics and Data Science;
AI and Machine Learning;
Organizational Structure;
Network Effects;
Competitive Strategy;
Biotechnology Industry;
Pharmaceutical Industry;
United States;
Boston
Eisenmann, Thomas R., and Tom Quinn. "TetraScience: Noise and Signal." Harvard Business School Case 824-024, September 2023. (Revised December 2023.)
- September 2023 (Revised October 2024)
- Case
Forecasting Climate Risks: Aviva’s Climate Calculus
By: Mark Egan and Peter Tufano
In late 2021, Ben Carr, Director of Analytics and Capital Modeling at Aviva Plc (Aviva)—a leading insurer with core operations in the UK, Ireland and Canada,—was preparing for an upcoming presentation before the company's board which included its CEO, Amanda Blanc,...
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Keywords:
Climate Risk;
Climate Finance;
Forecasting;
Insurance;
Risk Measurement;
Climate Change;
Risk Management;
Forecasting and Prediction;
Insurance Industry;
United States
Egan, Mark, and Peter Tufano. "Forecasting Climate Risks: Aviva’s Climate Calculus." Harvard Business School Case 224-025, September 2023. (Revised October 2024.)
- July 2023 (Revised July 2023)
- Background Note
Generative AI Value Chain
By: Andy Wu and Matt Higgins
Generative AI refers to a type of artificial intelligence (AI) that can create new content (e.g., text, image, or audio) in response to a prompt from a user. ChatGPT, Bard, and Claude are examples of text generating AIs, and DALL-E, Midjourney, and Stable Diffusion are...
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Keywords:
AI;
Artificial Intelligence;
Model;
Hardware;
Data Centers;
AI and Machine Learning;
Applications and Software;
Analytics and Data Science;
Value
Wu, Andy, and Matt Higgins. "Generative AI Value Chain." Harvard Business School Background Note 724-355, July 2023. (Revised July 2023.)
- July 2023
- Article
Takahashi-Alexander Revisited: Modeling Private Equity Portfolio Outcomes Using Historical Simulations
By: Dawson Beutler, Alex Billias, Sam Holt, Josh Lerner and TzuHwan Seet
In 2001, Dean Takahashi and Seth Alexander of the Yale University Investments Office developed a deterministic model for estimating future cash flows and valuations for the Yale endowment’s private equity portfolio. Their model, which is simple and intuitive, is still...
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Beutler, Dawson, Alex Billias, Sam Holt, Josh Lerner, and TzuHwan Seet. "Takahashi-Alexander Revisited: Modeling Private Equity Portfolio Outcomes Using Historical Simulations." Journal of Portfolio Management 49, no. 7 (July 2023): 144–158.
- 2023
- Working Paper
Feature Importance Disparities for Data Bias Investigations
By: Peter W. Chang, Leor Fishman and Seth Neel
It is widely held that one cause of downstream bias in classifiers is bias present in the training data. Rectifying such biases may involve context-dependent interventions such as training separate models on subgroups, removing features with bias in the collection...
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Chang, Peter W., Leor Fishman, and Seth Neel. "Feature Importance Disparities for Data Bias Investigations." Working Paper, March 2023.
- 2023
- Working Paper
PRIMO: Private Regression in Multiple Outcomes
By: Seth Neel
We introduce a new differentially private regression setting we call Private Regression in Multiple Outcomes (PRIMO), inspired the common situation where a data analyst wants to perform a set of l regressions while preserving privacy, where the covariates...
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Neel, Seth. "PRIMO: Private Regression in Multiple Outcomes." Working Paper, March 2023.
- March 2023
- Supplement
Allianz Türkiye (C): Managing the 2017 Hail Storm
By: John D. Macomber and Fares Khrais
Allianz Turkey is a property casualty insurance company operating in a region experiencing increasing losses from natural catastrophe events related to climate change, for example hail, wildfire, and flooding. There are also substantial other natural catastrophe...
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Keywords:
Insurance And Reinsurance;
Natural Disasters;
Turkey;
Insurance;
Climate Change;
Analytics and Data Science;
Insurance Industry;
Financial Services Industry;
Turkey
Macomber, John D., and Fares Khrais. "Allianz Türkiye (C): Managing the 2017 Hail Storm." Harvard Business School Supplement 223-084, March 2023.
- March 2023 (Revised April 2024)
- Case
Allianz Türkiye: Adapting to Climate Change
By: John D. Macomber and Fares Khrais
Allianz Turkey is a property casualty insurance company operating in a region experiencing increasing losses from natural catastrophe events related to climate change, for example hail, wildfire, and flooding. There are also substantial other natural catastrophe...
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Keywords:
Insurance And Reinsurance;
Natural Disasters;
Turkey;
Insurance;
Climate Change;
Analytics and Data Science;
Insurance Industry;
Financial Services Industry;
Turkey
Macomber, John D., and Fares Khrais. "Allianz Türkiye: Adapting to Climate Change." Harvard Business School Case 223-074, March 2023. (Revised April 2024.)
- March–April 2023
- Article
Market Segmentation Trees
By: Ali Aouad, Adam Elmachtoub, Kris J. Ferreira and Ryan McNellis
Problem definition: We seek to provide an interpretable framework for segmenting users in a population for personalized decision making. Methodology/results: We propose a general methodology, market segmentation trees (MSTs), for learning market...
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Keywords:
Decision Trees;
Computational Advertising;
Market Segmentation;
Analytics and Data Science;
E-commerce;
Consumer Behavior;
Marketplace Matching;
Marketing Channels;
Digital Marketing
Aouad, Ali, Adam Elmachtoub, Kris J. Ferreira, and Ryan McNellis. "Market Segmentation Trees." Manufacturing & Service Operations Management 25, no. 2 (March–April 2023): 648–667.
- January–February 2023
- Article
Forecasting COVID-19 and Analyzing the Effect of Government Interventions
By: Michael Lingzhi Li, Hamza Tazi Bouardi, Omar Skali Lami, Thomas Trikalinos, Nikolaos Trichakis and Dimitris Bertsimas
We developed DELPHI, a novel epidemiological model for predicting detected cases and deaths in the prevaccination era of the COVID-19 pandemic. The model allows for underdetection of infections and effects of government interventions. We have applied DELPHI across more...
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Keywords:
COVID-19 Pandemic;
Epidemics;
Analytics and Data Science;
Health Pandemics;
AI and Machine Learning;
Forecasting and Prediction
Li, Michael Lingzhi, Hamza Tazi Bouardi, Omar Skali Lami, Thomas Trikalinos, Nikolaos Trichakis, and Dimitris Bertsimas. "Forecasting COVID-19 and Analyzing the Effect of Government Interventions." Operations Research 71, no. 1 (January–February 2023): 184–201.
- 2023
- Working Paper
Nailing Prediction: Experimental Evidence on the Value of Tools in Predictive Model Development
By: Daniel Yue, Paul Hamilton and Iavor Bojinov
Predictive model development is understudied despite its centrality in modern artificial
intelligence and machine learning business applications. Although prior discussions
highlight advances in methods (along the dimensions of data, computing power, and
algorithms)...
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Keywords:
Analytics and Data Science
Yue, Daniel, Paul Hamilton, and Iavor Bojinov. "Nailing Prediction: Experimental Evidence on the Value of Tools in Predictive Model Development." Harvard Business School Working Paper, No. 23-029, December 2022. (Revised April 2023.)